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PyPI | GHSA-hr84-fqvp-48mm | Segfault in SparseCountSparseOutput | ### Impact
Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken.
```python
import tensorflow as tf
indices = tf.constant([], shape=[0, 0], dtype=tf.int64)
values = tf.constant([], shape=[0, 0], dtype=tf.int64)
dense_shape = tf.constant([-100, -100, -100], shape=[3], dtype=tf.int64)
weights = tf.constant([], shape=[0, 0], dtype=tf.int64)
tf.raw_ops.SparseCountSparseOutput(indices=indices, values=values, dense_shape=dense_shape, weights=weights, minlength=79, maxlength=96, binary_output=False)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., [`std::vector<absl::flat_hash_map<int64,T>>`](https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure.
```cc
bool is_1d = shape.NumElements() == 1;
int num_batches = is_1d ? 1 : shape.flat<int64>()(0);
...
auto per_batch_counts = BatchedMap<W>(num_batches);
```
If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`.
Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue.
### Patches
We have patched the issue in GitHub commit [c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5](https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. | {'CVE-2021-29521'} | 2022-03-03T05:13:33.495080Z | 2021-05-21T14:21:16Z | LOW | null | {'CWE-131'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29521', 'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm'} | null | {'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5'} | {'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5'} |
PyPI | PYSEC-2021-746 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-cgfm-62j4-v4rf', 'CVE-2021-37635'} | 2021-12-09T06:35:35.309422Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cgfm-62j4-v4rf'} | null | {'https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750'} | {'https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750'} |
PyPI | PYSEC-2020-316 | null | In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'CVE-2020-15201', 'GHSA-p5f8-gfw5-33w4'} | 2021-12-09T06:35:13.253783Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | PYSEC-2017-104 | null | An incorrect implementation of "XEP-0280: Message Carbons" in multiple XMPP clients allows a remote attacker to impersonate any user, including contacts, in the vulnerable application's display. This allows for various kinds of social engineering attacks. This CVE is for SleekXMPP up to 1.3.1 and Slixmpp all versions up to 1.2.3, as bundled in poezio (0.8 - 0.10) and other products. | {'CVE-2017-5591'} | 2021-12-14T08:19:29.481755Z | 2017-02-09T20:59:00Z | null | null | null | {'https://github.com/poezio/slixmpp/commit/22664ee7b86c8e010f312b66d12590fb47160ad8', 'http://www.securityfocus.com/bid/96166', 'https://pypi.org/project/slixmpp', 'https://rt-solutions.de/en/2017/02/CVE-2017-5589_xmpp_carbons/', 'https://rt-solutions.de/wp-content/uploads/2017/02/CVE-2017-5589_xmpp_carbons.pdf', 'https://nvd.nist.gov/vuln/detail/CVE-2017-5591', 'http://openwall.com/lists/oss-security/2017/02/09/29'} | null | {'https://github.com/poezio/slixmpp/commit/22664ee7b86c8e010f312b66d12590fb47160ad8'} | {'https://github.com/poezio/slixmpp/commit/22664ee7b86c8e010f312b66d12590fb47160ad8'} |
PyPI | PYSEC-2020-253 | null | TensorFlow before 1.7.0 has an integer overflow that causes an out-of-bounds read, possibly causing disclosure of the contents of process memory. This occurs in the DecodeBmp feature of the BMP decoder in core/kernels/decode_bmp_op.cc. | {'CVE-2018-21233', 'GHSA-h98h-8mxr-m8gx'} | 2021-08-27T03:22:22.195752Z | 2020-05-04T15:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-h98h-8mxr-m8gx', 'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2018-001.md', 'https://github.com/tensorflow/tensorflow/commit/49f73c55d56edffebde4bca4a407ad69c1cae433'} | null | {'https://github.com/tensorflow/tensorflow/commit/49f73c55d56edffebde4bca4a407ad69c1cae433'} | {'https://github.com/tensorflow/tensorflow/commit/49f73c55d56edffebde4bca4a407ad69c1cae433'} |
PyPI | GHSA-cr3f-r24j-3chw | Cobbler before 3.3.0 allows authorization bypass for modification of settings. | Cobbler before 3.3.0 allows authorization bypass for modification of settings. | {'CVE-2021-40325'} | 2022-03-03T05:12:47.833092Z | 2021-10-05T17:53:29Z | MODERATE | null | {'CWE-863'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-40325', 'https://github.com/cobbler/cobbler/commit/d8f60bbf14a838c8c8a1dba98086b223e35fe70a', 'https://github.com/cobbler/cobbler', 'https://github.com/cobbler/cobbler/releases/tag/v3.3.0'} | null | {'https://github.com/cobbler/cobbler/commit/d8f60bbf14a838c8c8a1dba98086b223e35fe70a'} | {'https://github.com/cobbler/cobbler/commit/d8f60bbf14a838c8c8a1dba98086b223e35fe70a'} |
PyPI | PYSEC-2021-253 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of TrySimplify(https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that result in optimizing a node with no inputs. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-4hvv-7x94-7vq8', 'CVE-2021-29616'} | 2021-08-27T03:22:42.041590Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvv-7x94-7vq8'} | null | {'https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3'} | {'https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3'} |
PyPI | PYSEC-2021-603 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions (such as `MutableHashTableShape`) produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37690', 'GHSA-3hxh-8cp2-g4hg'} | 2021-12-09T06:35:06.680335Z | 2021-08-13T00:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3hxh-8cp2-g4hg'} | null | {'https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1'} | {'https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1'} |
PyPI | PYSEC-2021-552 | null | TensorFlow is an end-to-end open source platform for machine learning. When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read. We have patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-gh6x-4whr-2qv4', 'CVE-2021-37639'} | 2021-12-09T06:35:02.331501Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh6x-4whr-2qv4', 'https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622'} | null | {'https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622'} | {'https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622'} |
PyPI | GHSA-8wwf-2644-f8x4 | Path traversal | The thefuck (aka The Fuck) package before 3.31 for Python allows Path Traversal that leads to arbitrary file deletion via the `undo archive operation` feature. | {'CVE-2021-34363'} | 2022-03-29T22:31:59.709279Z | 2021-06-15T15:49:01Z | CRITICAL | null | {'CWE-22'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/4MEDDLBFVRUQHPYIBJ4MFM3M4NUJUXL5/', 'https://github.com/nvbn/thefuck', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/YA6UNQSOY6M3NJDZLS6YJXTS4WGDMEEJ/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-34363', 'https://vuln.ryotak.me/advisories/48', 'https://github.com/nvbn/thefuck/releases/tag/3.31', 'https://github.com/nvbn/thefuck/commit/e343c577cd7da4d304b837d4a07ab4df1e023092'} | null | {'https://github.com/nvbn/thefuck/commit/e343c577cd7da4d304b837d4a07ab4df1e023092'} | {'https://github.com/nvbn/thefuck/commit/e343c577cd7da4d304b837d4a07ab4df1e023092'} |
PyPI | PYSEC-2021-801 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions (such as `MutableHashTableShape`) produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37690', 'GHSA-3hxh-8cp2-g4hg'} | 2021-12-09T06:35:40.227651Z | 2021-08-13T00:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3hxh-8cp2-g4hg'} | null | {'https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1'} | {'https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1'} |
PyPI | GHSA-23c7-6444-399m | Improper Input Validation in sopel-plugins.channelmgnt | ### Impact
On some IRC servers, restrictions around the removal of the bot using the kick/kickban command could be bypassed when kicking multiple users at once.
We also believe it may have been possible to remove users from other channels but due to the wonder that is IRC and following RfCs, We have no POC for that.
Freenode is not affected.
### Patches
Upgrade to 2.0.1 or higher
### Workarounds
Do not use this plugin on networks where TARGMAX > 1.
### For more information
If you have any questions or comments about this advisory:
* Open an issue on [phab](https://phab.mirahezebots.org/maniphest/task/edit/form/1/).
* Email us at [staff(at)mirahezebots(dot)org](mailto:staff@mirahezebots.org) | {'CVE-2021-21431'} | 2022-03-03T05:13:40.160944Z | 2021-04-09T15:42:40Z | HIGH | null | {'CWE-20', 'CWE-284'} | {'https://github.com/MirahezeBots/sopel-channelmgnt/commit/643388365f28c5cc682254ab913c401f0e53260a', 'https://github.com/MirahezeBots/sopel-channelmgnt/security/advisories/GHSA-23c7-6444-399m', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21431', 'https://pypi.org/project/sopel-plugins.channelmgnt/', 'https://github.com/MirahezeBots/sopel-channelmgnt/commit/7c96d400358221e59135f0a0be0744f3fad73856'} | null | {'https://github.com/MirahezeBots/sopel-channelmgnt/commit/7c96d400358221e59135f0a0be0744f3fad73856', 'https://github.com/MirahezeBots/sopel-channelmgnt/commit/643388365f28c5cc682254ab913c401f0e53260a'} | {'https://github.com/MirahezeBots/sopel-channelmgnt/commit/7c96d400358221e59135f0a0be0744f3fad73856', 'https://github.com/MirahezeBots/sopel-channelmgnt/commit/643388365f28c5cc682254ab913c401f0e53260a'} |
PyPI | PYSEC-2016-1 | null | The create_script function in the lxc_container module in Ansible before 1.9.6-1 and 2.x before 2.0.2.0 allows local users to write to arbitrary files or gain privileges via a symlink attack on (1) /opt/.lxc-attach-script, (2) the archived container in the archive_path directory, or the (3) lxc-attach-script.log or (4) lxc-attach-script.err files in the temporary directory. | {'GHSA-rh6x-qvg7-rrmj', 'CVE-2016-3096'} | 2021-07-02T02:41:33.519196Z | 2016-06-03T14:59:00Z | null | null | null | {'https://groups.google.com/forum/#!topic/ansible-announce/tqiZbcWxYig', 'https://groups.google.com/forum/#!topic/ansible-announce/E80HLZilTU0', 'https://github.com/ansible/ansible/blob/v1.9.6-1/CHANGELOG.md#196-dancing-in-the-street---tbd', 'https://github.com/ansible/ansible/blob/v2.0.2.0-1/CHANGELOG.md#202-over-the-hills-and-far-away', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-April/183103.html', 'https://github.com/ansible/ansible-modules-extras/pull/1941', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-April/183274.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-April/183252.html', 'https://github.com/ansible/ansible-modules-extras/pull/1941/commits/8c6fe646ee79f5e55361b885b7efed5bec72d4a4', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-May/184175.html', 'https://github.com/advisories/GHSA-rh6x-qvg7-rrmj', 'https://bugzilla.redhat.com/show_bug.cgi?id=1322925', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-April/183132.html', 'https://security.gentoo.org/glsa/201607-14'} | null | {'https://github.com/ansible/ansible-modules-extras/pull/1941/commits/8c6fe646ee79f5e55361b885b7efed5bec72d4a4'} | {'https://github.com/ansible/ansible-modules-extras/pull/1941/commits/8c6fe646ee79f5e55361b885b7efed5bec72d4a4'} |
PyPI | PYSEC-2021-417 | null | TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's Grappler optimizer has a use of unitialized variable. If the `train_nodes` vector (obtained from the saved model that gets optimized) does not contain a `Dequeue` node, then `dequeue_node` is left unitialized. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41225', 'GHSA-7r94-xv9v-63jw'} | 2021-11-13T06:52:45.918636Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw'} | null | {'https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3'} | {'https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3'} |
PyPI | GHSA-47qg-q58v-7vrp | UNEDITABLE_SCHEMAS and UNEDITABLE_TABLE_DESCRIPTION_MATCH_RULES not respected by frontend service backend | ### Impact
Any install that has `UNEDITABLE_SCHEMAS` and/or `UNEDITABLE_TABLE_DESCRIPTION_MATCH_RULES` set in the front-end, is being impacted. The value of these properties is ignored if set, allowing any user to modify table and column descriptions, even though the properties imply they shouldn't be.
### Patches
There is an attached PR that applies this restriction on the back-end.
### Workarounds
N/A
### References
N/A
### For more information
If you have any questions or comments about this advisory:
* Email us at [amundsen-security@lists.lfaidata.foundation](mailto:amundsen-security@lists.lfaidata.foundation)
### More details
Summary: I believe that UNEDITABLE_SCHEMAS and
UNEDITABLE_TABLE_DESCRIPTION_MATCH_RULES are only being applied on the
front-end, not on the frontend service back-end, allowing any user to
modify table and column descriptions even if this configuration parameter
is set.
Repro steps:
1. docker-compose -f docker-amundsen.yml up neo4j elasticsearch
amundsensearch amundsenmetadata
2. python example/scripts/sample_data_loader.py
3. FRONTEND_SVC_CONFIG_MODULE_CLASS=amundsen_application.config.TestConfig
PYTHONPATH=. python3 amundsen_application/wsgi.py
4. Attempt a modification to a table description:
curl '<http://localhost:5000/api/metadata/v0/put_table_description>' \\\\
-X 'PUT' \\\\
-H 'Content-Type: application/json;charset=UTF-8' \\\\
--data-binary '{"description":"2t test table","key":"hive://gold.test_schema/test_table1","source":"user"}'
{"msg":"Success"}
5. This correctly succeeds, which can be validated by GETing the info:
curl '<http://localhost:5000/api/metadata/v0/get_table_description?key=hive://gold.test_schema/test_table1>'
{"description":"1st test table","msg":"Success"}
At this point, modify TestConfig inside config.py to add this line: UNEDITABLE_SCHEMAS
= set(['test_schema'])
You can now re-run step 4, and step 5 with different data, and confirm
that the modification has persisted. If you build and run the UI, you can
see that on the page
<http://localhost:5000/table_detail/gold/hive/test_schema/test_table1>
http://localhost:5000/table_detail/gold/hive/test_schema/test_table1, the
inline editor is correctly disabled.
Looking at
amundsenfrontendlibrary/amundsen_application/api/metadata/v0.py:268
put_table_description, you can see there's no reference to
UNEDITABLE_SCHEMAS or UNEDITABLE_TABLE_DESCRIPTION_MATCH_RULES.
The only place I can find these referenced is in
amundsenfrontendlibrary/amundsen_application/api/utils/metadata_utils.py:marshall_table_full,
which would explain why the UI is correctly respecting this setting.
If this is correct, put_column_description would also be similarly
affected.
I believe the correct fix for all of these methods is to load the table,
run it through marshall_dashboard_partial to fully evaluate what's
editable or not (to reuse the same code path for FE and back-end), and
reject the response if it's not editable. I'll implement a fix along these
lines once someone confirms this.
History: This functionality was introduced in
<https://github.com/amundsen-io/amundsenfrontendlibrary/pull/497/files>
https://github.com/amundsen-io/amundsenfrontendlibrary/pull/497 on July
9, corresponding to the 2.3.0 release of amundsenfrontend. That release was
introduced into the main repo dockerfile on October 28 in
<https://github.com/amundsen-io/amundsen/pull/785>
https://github.com/amundsen-io/amundsen/pull/785 | null | 2022-03-03T05:12:41.098968Z | 2020-12-02T18:28:10Z | LOW | null | {'CWE-602'} | {'https://github.com/amundsen-io/amundsenfrontendlibrary/commit/0b47694ea74cbbef34e03eb45f29643b16a1332a', 'https://github.com/amundsen-io/amundsenfrontendlibrary/security/advisories/GHSA-47qg-q58v-7vrp'} | null | {'https://github.com/amundsen-io/amundsenfrontendlibrary/commit/0b47694ea74cbbef34e03eb45f29643b16a1332a'} | {'https://github.com/amundsen-io/amundsenfrontendlibrary/commit/0b47694ea74cbbef34e03eb45f29643b16a1332a'} |
PyPI | PYSEC-2021-694 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-4p4p-www8-8fv9', 'CVE-2021-29568'} | 2021-12-09T06:35:26.499647Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4p4p-www8-8fv9'} | null | {'https://github.com/tensorflow/tensorflow/commit/5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8'} | {'https://github.com/tensorflow/tensorflow/commit/5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8'} |
PyPI | GHSA-3mw4-6rj6-74g5 | Null pointer dereference in TensorFlow | ### Impact
The [implementation of `QuantizedMaxPool`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/quantized_pooling_ops.cc#L114-L130) has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer.
```python
import tensorflow as tf
tf.raw_ops.QuantizedMaxPool(
input = tf.constant([[[[4]]]], dtype=tf.quint8),
min_input = [],
max_input = [1],
ksize = [1, 1, 1, 1],
strides = [1, 1, 1, 1],
padding = "SAME", name=None
)
```
### Patches
We have patched the issue in GitHub commit [53b0dd6dc5957652f35964af16b892ec9af4a559](https://github.com/tensorflow/tensorflow/commit/53b0dd6dc5957652f35964af16b892ec9af4a559).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Faysal Hossain Shezan from University of Virginia. | {'CVE-2022-21739'} | 2022-03-03T05:13:37.611501Z | 2022-02-09T23:46:46Z | MODERATE | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-21739', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3mw4-6rj6-74g5', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/53b0dd6dc5957652f35964af16b892ec9af4a559', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/quantized_pooling_ops.cc#L114-L130'} | null | {'https://github.com/tensorflow/tensorflow/commit/53b0dd6dc5957652f35964af16b892ec9af4a559'} | {'https://github.com/tensorflow/tensorflow/commit/53b0dd6dc5957652f35964af16b892ec9af4a559'} |
PyPI | GHSA-r35g-4525-29fq | Division by 0 in `FusedBatchNorm` | ### Impact
An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.FusedBatchNorm`:
```python
import tensorflow as tf
x = tf.constant([], shape=[1, 1, 1, 0], dtype=tf.float32)
scale = tf.constant([], shape=[0], dtype=tf.float32)
offset = tf.constant([], shape=[0], dtype=tf.float32)
mean = tf.constant([], shape=[0], dtype=tf.float32)
variance = tf.constant([], shape=[0], dtype=tf.float32)
epsilon = 0.0
exponential_avg_factor = 0.0
data_format = "NHWC"
is_training = False
tf.raw_ops.FusedBatchNorm(
x=x, scale=scale, offset=offset, mean=mean,
variance=variance, epsilon=epsilon,
exponential_avg_factor=exponential_avg_factor,
data_format=data_format, is_training=is_training)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/828f346274841fa7505f7020e88ca36c22e557ab/tensorflow/core/kernels/fused_batch_norm_op.cc#L295-L297) performs a division based on the last dimension of the `x` tensor:
```cc
const int depth = x.dimension(3);
const int rest_size = size / depth;
```
Since this is controlled by the user, an attacker can trigger a denial of service.
### Patches
We have patched the issue in GitHub commit [1a2a87229d1d61e23a39373777c056161eb4084d](https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29555'} | 2022-03-03T05:10:28.715091Z | 2021-05-21T14:23:58Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r35g-4525-29fq', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29555'} | null | {'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d'} | {'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d'} |
PyPI | PYSEC-2021-34 | null | models/metadata.py in the pikepdf package 1.3.0 through 2.9.2 for Python allows XXE when parsing XMP metadata entries. | {'CVE-2021-29421', 'GHSA-ccgm-3xw4-h5p8'} | 2021-04-09T19:15:00Z | 2021-04-01T20:15:00Z | null | null | null | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/3QFLBBYGEDNXJ7FS6PIWTVI4T4BUPGEQ/', 'https://github.com/advisories/GHSA-ccgm-3xw4-h5p8', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/36P4HTLBJPO524WMQWW57N3QRF4RFSJG/', 'https://github.com/pikepdf/pikepdf/commit/3f38f73218e5e782fe411ccbb3b44a793c0b343a'} | null | {'https://github.com/pikepdf/pikepdf/commit/3f38f73218e5e782fe411ccbb3b44a793c0b343a'} | {'https://github.com/pikepdf/pikepdf/commit/3f38f73218e5e782fe411ccbb3b44a793c0b343a'} |
PyPI | PYSEC-2021-381 | null | Rasa is an open source machine learning framework to automate text-and voice-based conversations. In affected versions a vulnerability exists in the functionality that loads a trained model `tar.gz` file which allows a malicious actor to craft a `model.tar.gz` file which can overwrite or replace bot files in the bot directory. The vulnerability is fixed in Rasa 2.8.10. For users unable to update ensure that users do not upload untrusted model files, and restrict CLI or API endpoint access where a malicious actor could target a deployed Rasa instance. | {'GHSA-4365-fhm5-qcrx', 'CVE-2021-41127'} | 2021-10-24T23:24:39.410729Z | 2021-10-21T21:15:00Z | null | null | null | {'https://github.com/RasaHQ/rasa/commit/1b6b502f52d73b4f8cd1959ce724b8ad0eb33989', 'https://github.com/RasaHQ/rasa/security/advisories/GHSA-4365-fhm5-qcrx'} | null | {'https://github.com/RasaHQ/rasa/commit/1b6b502f52d73b4f8cd1959ce724b8ad0eb33989'} | {'https://github.com/RasaHQ/rasa/commit/1b6b502f52d73b4f8cd1959ce724b8ad0eb33989'} |
PyPI | PYSEC-2020-155 | null | Waitress version 1.4.2 allows a DOS attack When waitress receives a header that contains invalid characters. When a header like "Bad-header: xxxxxxxxxxxxxxx\x10" is received, it will cause the regular expression engine to catastrophically backtrack causing the process to use 100% CPU time and blocking any other interactions. This allows an attacker to send a single request with an invalid header and take the service offline. This issue was introduced in version 1.4.2 when the regular expression was updated to attempt to match the behaviour required by errata associated with RFC7230. The regular expression that is used to validate incoming headers has been updated in version 1.4.3, it is recommended that people upgrade to the new version of Waitress as soon as possible. | {'CVE-2020-5236', 'GHSA-73m2-3pwg-5fgc'} | 2020-02-06T18:46:00Z | 2020-02-04T03:15:00Z | null | null | null | {'https://github.com/Pylons/waitress/security/advisories/GHSA-73m2-3pwg-5fgc', 'https://github.com/Pylons/waitress/commit/6e46f9e3f014d64dd7d1e258eaf626e39870ee1f'} | null | {'https://github.com/Pylons/waitress/commit/6e46f9e3f014d64dd7d1e258eaf626e39870ee1f'} | {'https://github.com/Pylons/waitress/commit/6e46f9e3f014d64dd7d1e258eaf626e39870ee1f'} |
PyPI | PYSEC-2018-27 | null | qutebrowser before version 1.4.1 is vulnerable to a cross-site request forgery flaw that allows websites to access 'qute://*' URLs. A malicious website could exploit this to load a 'qute://settings/set' URL, which then sets 'editor.command' to a bash script, resulting in arbitrary code execution. | {'CVE-2018-10895', 'GHSA-wgmx-52ph-qqcw'} | 2021-06-10T06:51:37.378319Z | 2018-07-12T12:29:00Z | null | null | null | {'https://github.com/qutebrowser/qutebrowser/commit/43e58ac865ff862c2008c510fc5f7627e10b4660', 'https://github.com/advisories/GHSA-wgmx-52ph-qqcw', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2018-10895', 'http://www.openwall.com/lists/oss-security/2018/07/11/7'} | null | {'https://github.com/qutebrowser/qutebrowser/commit/43e58ac865ff862c2008c510fc5f7627e10b4660'} | {'https://github.com/qutebrowser/qutebrowser/commit/43e58ac865ff862c2008c510fc5f7627e10b4660'} |
PyPI | PYSEC-2021-155 | null | TensorFlow is an end-to-end open source platform for machine learning. In eager mode (default in TF 2.0 and later), session operations are invalid. However, users could still call the raw ops associated with them and trigger a null pointer dereference. The implementation(https://github.com/tensorflow/tensorflow/blob/eebb96c2830d48597d055d247c0e9aebaea94cd5/tensorflow/core/kernels/session_ops.cc#L104) dereferences the session state pointer without checking if it is valid. Thus, in eager mode, `ctx->session_state()` is nullptr and the call of the member function is undefined behavior. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-62gx-355r-9fhg', 'CVE-2021-29518'} | 2021-08-27T03:22:24.585448Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-62gx-355r-9fhg', 'https://github.com/tensorflow/tensorflow/commit/ff70c47a396ef1e3cb73c90513da4f5cb71bebba'} | null | {'https://github.com/tensorflow/tensorflow/commit/ff70c47a396ef1e3cb73c90513da4f5cb71bebba'} | {'https://github.com/tensorflow/tensorflow/commit/ff70c47a396ef1e3cb73c90513da4f5cb71bebba'} |
PyPI | GHSA-q3m9-9fj2-mfwr | URL Redirection to Untrusted Site ('Open Redirect') in Products.isurlinportal | ### Impact
Various parts of Plone use the 'is url in portal' check for security, mostly to see if it is safe to redirect to a url. A url like `https://example.org` is not in the portal.
But the url `https:example.org` without slashes tricks our code and it _is_ considered to be in the portal.
When redirecting, some browsers go to `https://example.org`, others give an error.
Attackers may use this to redirect you to their site, especially as part of a phishing attack.
### Patches
The problem has been patched in `Products.isurlinportal` 1.2.0.
This is a recommended upgrade for all users of Plone 4.3 and 5, on Python 2.7 or higher.
It has not been tested on earlier Plone or Python versions.
Upcoming Plone 5.2.5 and higher will include the new version.
### Discovered
This vulnerability was discovered and reported by Yuji Tounai of Mitsui Bussan Secure Directions, Inc. Thank you!
### For more information
If you have any questions or comments about this advisory:
* Email the Plone Security Team at [security@plone.org](mailto:security@plone.org), especially when you think you have discovered a security problem or when you are not sure.
* Open an issue in [the tracker](https://github.com/plone/Products.isurlinportal/issues) if your question or comment can be public.
| {'CVE-2021-32806'} | 2022-03-03T05:12:39.025583Z | 2021-08-05T17:02:12Z | MODERATE | null | {'CWE-601'} | {'https://github.com/plone/Products.isurlinportal/commit/d4fd34990d18adf05a10dc5e2bb4b066798280ba', 'http://jvn.jp/en/jp/JVN50804280/index.html', 'https://github.com/plone/Products.isurlinportal/security/advisories/GHSA-q3m9-9fj2-mfwr', 'https://github.com/plone/Products.isurlinportal', 'https://nvd.nist.gov/vuln/detail/CVE-2021-32806'} | null | {'https://github.com/plone/Products.isurlinportal/commit/d4fd34990d18adf05a10dc5e2bb4b066798280ba'} | {'https://github.com/plone/Products.isurlinportal/commit/d4fd34990d18adf05a10dc5e2bb4b066798280ba'} |
PyPI | PYSEC-2021-505 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-v6r6-84gr-92rm', 'CVE-2021-29577'} | 2021-12-09T06:34:55.310052Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6r6-84gr-92rm'} | null | {'https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d'} | {'https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d'} |
PyPI | PYSEC-2021-440 | null | TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected. | {'GHSA-4278-2v5v-65r4', 'CVE-2021-29512'} | 2021-12-09T06:34:45.216617Z | 2021-05-14T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4278-2v5v-65r4'} | null | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} |
PyPI | GHSA-87v6-crgm-2gfj | Division by zero in Tensorflow | ### Impact
The [implementation of `FractionalMaxPool`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_max_pool_op.cc#L36-L192) can be made to crash a TensorFlow process via a division by 0:
```python
import tensorflow as tf
import numpy as np
tf.raw_ops.FractionalMaxPool(
value=tf.constant(value=[[[[1, 4, 2, 3]]]], dtype=tf.int64),
pooling_ratio=[1.0, 1.44, 1.73, 1.0],
pseudo_random=False,
overlapping=False,
deterministic=False,
seed=0,
seed2=0,
name=None)
```
### Patches
We have patched the issue in GitHub commit [ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb](https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Faysal Hossain Shezan from University of Virginia. | {'CVE-2022-21735'} | 2022-03-03T05:13:41.296850Z | 2022-02-10T00:21:32Z | MODERATE | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-21735', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_max_pool_op.cc#L36-L192', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-87v6-crgm-2gfj'} | null | {'https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb'} | {'https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb'} |
PyPI | PYSEC-2022-73 | null | Tensorflow is an Open Source Machine Learning Framework. When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-8rcj-c8pj-v3m3', 'CVE-2022-23564'} | 2022-03-09T00:17:32.923545Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/14fea662350e7c26eb5fe1be2ac31704e5682ee6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rcj-c8pj-v3m3'} | null | {'https://github.com/tensorflow/tensorflow/commit/14fea662350e7c26eb5fe1be2ac31704e5682ee6'} | {'https://github.com/tensorflow/tensorflow/commit/14fea662350e7c26eb5fe1be2ac31704e5682ee6'} |
PyPI | PYSEC-2021-769 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all operations of type `tf.raw_ops.MatrixSetDiagV*`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of `k` is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. We have patched the issue in GitHub commit ff8894044dfae5568ecbf2ed514c1a37dc394f1b. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37658', 'GHSA-6p5r-g9mq-ggh2'} | 2021-12-09T06:35:37.342418Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ff8894044dfae5568ecbf2ed514c1a37dc394f1b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6p5r-g9mq-ggh2'} | null | {'https://github.com/tensorflow/tensorflow/commit/ff8894044dfae5568ecbf2ed514c1a37dc394f1b'} | {'https://github.com/tensorflow/tensorflow/commit/ff8894044dfae5568ecbf2ed514c1a37dc394f1b'} |
PyPI | GHSA-rhrq-64mq-hf9h | FPE in TFLite division operations | ### Impact
The implementation of division in TFLite is [vulnerable to a division by 0 error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/div.cc)
There is no check that the divisor tensor does not contain zero elements.
### Patches
We have patched the issue in GitHub commit [1e206baedf8bef0334cca3eb92bab134ef525a28](https://github.com/tensorflow/tensorflow/commit/1e206baedf8bef0334cca3eb92bab134ef525a28).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37683'} | 2021-08-24T17:06:32Z | 2021-08-25T14:40:16Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/1e206baedf8bef0334cca3eb92bab134ef525a28', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rhrq-64mq-hf9h', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37683'} | null | {'https://github.com/tensorflow/tensorflow/commit/1e206baedf8bef0334cca3eb92bab134ef525a28'} | {'https://github.com/tensorflow/tensorflow/commit/1e206baedf8bef0334cca3eb92bab134ef525a28'} |
PyPI | PYSEC-2021-293 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.Map*` and `tf.raw_ops.OrderedMap*` operations. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L222-L248) has a check in place to ensure that `indices` is in ascending order, but does not check that `indices` is not empty. We have patched the issue in GitHub commit 532f5c5a547126c634fefd43bbad1dc6417678ac. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37671', 'GHSA-qr82-2c78-4m8h'} | 2021-08-27T03:22:45.925209Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qr82-2c78-4m8h', 'https://github.com/tensorflow/tensorflow/commit/532f5c5a547126c634fefd43bbad1dc6417678ac'} | null | {'https://github.com/tensorflow/tensorflow/commit/532f5c5a547126c634fefd43bbad1dc6417678ac'} | {'https://github.com/tensorflow/tensorflow/commit/532f5c5a547126c634fefd43bbad1dc6417678ac'} |
PyPI | PYSEC-2020-293 | null | In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. | {'CVE-2020-15213', 'GHSA-hjmq-236j-8m87'} | 2021-12-09T06:34:43.849481Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87', 'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} |
PyPI | GHSA-qc9x-gjcv-465w | Pipenv's requirements.txt parsing allows malicious index url in comments | ## Issue Summary
Due to a flaw in pipenv's parsing of requirements files, an attacker can insert a specially crafted string inside a comment anywhere within a requirements.txt file, which will cause victims who use pipenv to install the requirements file (e.g. with "`pipenv install -r requirements.txt`") to download dependencies from a package index server controlled by the attacker. By embedding malicious code in packages served from their malicious index server, the attacker can trigger arbitrary remote code execution (RCE) on the victims' systems.
### Impact
The impact of successful exploitation is **severe/critical**.
If an attacker is able to hide a malicious `--index-url` option in a requirements file that a victim installs with pipenv, the attacker can embed arbitrary malicious code in packages served from their malicious index server that will be executed on the victim's host during installation (remote code execution/RCE). Exploitation using this technique would be relatively simple to achieve for an attacker with basic knowledge of Python, as the attacker can simply build a source distribution for any of the packages specified in the requirements file, and embed arbitrary malicious code in the setup.py file. When pip installs from a source distribution, any code in the setup.py is executed by the install process.
Basic attacks might use the initial RCE triggered when a victim installs the attacker's malicious package to steal credentials from the victim's host, leach the host's resources to mine cryptocurrency, or install exploit kits or other malware. More sophisticated attackers may use more advanced techniques to persist access to the victim's host, hide or remove evidence of their attack by deleting references to the malicious index server in the Pipfile and Pipfile.lock generated by pipenv or other potential indicators of compromise. Highly sophisticated attackers could attempt to pivot to additional targets from the initial compromised host, and might leverage any exposed credentials in the compromised host environment or implicit authorization granted to the host to gain privileged access to other systems or resources, such as source repositories or package registries.
### Likelihood
The overall likelihood of exploitation is **low to moderate** depending on a range of factors.
The primary hurdle to successful exploitation of this vulnerability depends on an attacker's ability to surreptitiously insert a specially crafted string into a requirements.txt file which will be installed by a victim (or victims). Unfortunately, because the attacker can insert this string into a comment, the attacker's ability to evade suspicion is greatly increased, and they may even be able to hide the initial payload in plain sight if a victim assumes that comments will be ignored by pipenv as expected.
In many common usage contexts — for example in environments where a requirements file is used to lock or "freeze" dependency versions for reproducible builds — requirements files can often become quite large, particularly when leveraging pip's integrity checking, which requires every dependency specified in the requirements file to includes hashes for all of its distribution files. In such cases, a malicious actor might mask an exploitation attempt by opening a pull request ostensibly to update or "bump" the project's dependencies to their latest versions, but surreptitiously insert a malicious `—index-url` option amidst the many other changes associated with updating the dependencies in a lock file. As these dependency updates often result in hundreds or even thousands of changes spread across the requirements file and are not easy to review manually, such an attack could be difficult to identify or prevent without tools or other mitigating controls.
Moreover, because the `argparse` module is used to parse the `--index-url`, `--extra-index-url`, and `--trusted-host` options, an attacker's ability to obfuscate their payload and hide their malicious intent is even more greatly enhanced, as the attacker may use abbreviated option names, which are supported by default with `argparse`. For example, an attacker can insert the string, "`--t pypi.org`" into a comment anywhere in the requirements file, which will automatically be expanded to "`--trusted-host pypi.org`" during processing by pipenv. This "`--trusted-host pypi.org`" option will disable SSL/TLS validation when pipenv attempts to connect to the default/official package index server (https://pypi.org/simple), and could allow a malicious index server to pose as the pipi.org index server in a man-in-the-middle attack.
Setting up the malicious index server to serve compromised package versions is relatively simple, even for a non-sophisticated attacker. As `pip` uses a simple directory format for serving packages, the malicious packages simply need to be placed in the correct folder structure and served using an HTTP server with autoindex enabled (e.g. `python3 -m http.server`).
Packaging up the exploit code into the malicious package versions would also be trivial for an attacker with basic knowledge of Python development, as the attacker can simply clone the source code for any of the packages specified in the requirements file, embed their malicious exploit code in the cloned package's setup.py file, and then build a source distribution of the package. When pip installs a package from a source distribution, any code in the setup.py is executed by the install process.
### Additional Context & Details
According to the requirements file format specification (https://pip.pypa.io/en/stable/reference/requirements-file-format/#comments), any lines which begin with a "#" character, and/or any text in a line following a whitespace and a "#" character, should be interpreted as a comment which will be removed/ignored during processing of the requirements file.
However, due to a flaw in pipenv's parsing of requirements files, an attacker can insert a specially crafted string inside a comment anywhere within a requirements.txt file, which will cause victims who use pipenv to install the requirements file (e.g. with "`pipenv install -r requirements.txt`") to download dependencies from a package index server controlled by the attacker. By embedding malicious code in packages served from their malicious index server, the attacker is then able to gain arbitrary remote code execution on the victims' systems.
The vulnerable requirements file parsing code is in the parse_indexes(str: line) function of the pipenv.utils module:
https://github.com/pypa/pipenv/blob/cdde3f7bcee6bacba89538f73aba9401337be10c/pipenv/utils.py#L2061-L2078
This function is called iteratively on each line of a requirements file, and uses the argparse module to find and process `--index-url`, `--extra-index-url`, and `--trusted-host` options (and variations thereof). However, it does not ignore these options when they appear in comments, or validate that these options appear on their own lines as required by the requirements file specification (see: https://pip.pypa.io/en/stable/reference/requirements-file-format/#global-options). The options can also be abbreviated due to default behavior provided by the `argparse.ArgumentParser` object used to parse these options in the requirements file, so that `--trusted-host` and `--t` will be treated as equivalent by pipenv, for example.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [https://github.com/pypa/pipenv/](https://github.com/pypa/pipenv/)
* Contact the pipenv maintainers:
* [Dan Ryan](https://github.com/techalchemy)
* [Tzu-ping Chung](https://github.com/uranusjr)
* [Nate Prewitt](https://github.com/nateprewitt)
* Contact the contributor who discovered the issue and authored this report:
* [Chris Passarello](https://github.com/milo-minderbinder) | {'CVE-2022-21668'} | 2022-03-30T16:46:59.674900Z | 2022-01-12T22:29:41Z | HIGH | null | {'CWE-20', 'CWE-77'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/QHQRIWKDP3SVJABAPEXBIQPKDI6UP7G4/', 'https://github.com/pypa/pipenv', 'https://github.com/pypa/pipenv/releases/tag/v2022.1.8', 'https://github.com/pypa/pipenv/security/advisories/GHSA-qc9x-gjcv-465w', 'https://github.com/pypa/pipenv/commit/439782a8ae36c4762c88e43d5f0d8e563371b46f', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/KCROBYHUS6DKQPCXBRPCZ5CDBNQTYAWT/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/56HBA3EOSLEDNCCBJVHE6DO34P56EOUM/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21668'} | null | {'https://github.com/pypa/pipenv/commit/439782a8ae36c4762c88e43d5f0d8e563371b46f'} | {'https://github.com/pypa/pipenv/commit/439782a8ae36c4762c88e43d5f0d8e563371b46f'} |
PyPI | PYSEC-2017-14 | null | The serializer in html5lib before 0.99999999 might allow remote attackers to conduct cross-site scripting (XSS) attacks by leveraging mishandling of the < (less than) character in attribute values. | {'CVE-2016-9909'} | 2021-07-05T00:01:21.837127Z | 2017-02-22T16:59:00Z | null | null | null | {'http://www.securityfocus.com/bid/95132', 'https://github.com/html5lib/html5lib-python/commit/9b8d8eb5afbc066b7fac9390f5ec75e5e8a7cab7', 'http://www.openwall.com/lists/oss-security/2016/12/08/8', 'https://github.com/html5lib/html5lib-python/issues/11', 'https://html5lib.readthedocs.io/en/latest/changes.html#b9', 'https://github.com/html5lib/html5lib-python/issues/12', 'http://www.openwall.com/lists/oss-security/2016/12/06/5'} | null | {'https://github.com/html5lib/html5lib-python/commit/9b8d8eb5afbc066b7fac9390f5ec75e5e8a7cab7'} | {'https://github.com/html5lib/html5lib-python/commit/9b8d8eb5afbc066b7fac9390f5ec75e5e8a7cab7'} |
PyPI | GHSA-hvmf-r92r-27hr | Django allows unintended model editing | Django 2.1 before 2.1.15 and 2.2 before 2.2.8 allows unintended model editing. A Django model admin displaying inline related models, where the user has view-only permissions to a parent model but edit permissions to the inline model, would be presented with an editing UI, allowing POST requests, for updating the inline model. Directly editing the view-only parent model was not possible, but the parent model's save() method was called, triggering potential side effects, and causing pre and post-save signal handlers to be invoked. (To resolve this, the Django admin is adjusted to require edit permissions on the parent model in order for inline models to be editable.) | {'CVE-2019-19118'} | 2022-03-03T05:12:44.044245Z | 2019-12-04T21:26:28Z | MODERATE | null | {'CWE-276'} | {'https://github.com/django/django/commit/36f580a17f0b3cb087deadf3b65eea024f479c21', 'https://github.com/django/django/commit/103ebe2b5ff1b2614b85a52c239f471904d26244', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/6R4HD22PVEVQ45H2JA2NXH443AYJOPL5/', 'https://nvd.nist.gov/vuln/detail/CVE-2019-19118', 'https://security.netapp.com/advisory/ntap-20191217-0003/', 'https://docs.djangoproject.com/en/dev/releases/security/', 'https://www.djangoproject.com/weblog/2019/dec/02/security-releases/', 'http://www.openwall.com/lists/oss-security/2019/12/02/1', 'https://security.gentoo.org/glsa/202004-17', 'https://groups.google.com/forum/#!topic/django-announce/GjGqDvtNmWQ'} | null | {'https://github.com/django/django/commit/103ebe2b5ff1b2614b85a52c239f471904d26244', 'https://github.com/django/django/commit/36f580a17f0b3cb087deadf3b65eea024f479c21'} | {'https://github.com/django/django/commit/103ebe2b5ff1b2614b85a52c239f471904d26244', 'https://github.com/django/django/commit/36f580a17f0b3cb087deadf3b65eea024f479c21'} |
PyPI | GHSA-ccmq-qvcp-5mrm | Critical severity vulnerability that affects owlmixin | An exploitable vulnerability exists in the YAML loading functionality of util.py in OwlMixin before 2.0.0a12. A "Load YAML" string or file (aka load_yaml or load_yamlf) can execute arbitrary Python commands resulting in command execution because load is used where safe_load should have been used. An attacker can insert Python into loaded YAML to trigger this vulnerability. | {'CVE-2017-16618'} | 2022-03-22T22:01:52.729397Z | 2018-07-13T16:01:12Z | CRITICAL | null | null | {'https://joel-malwarebenchmark.github.io/blog/2017/11/08/cve-2017-16618-convert-through-owlmixin/', 'https://nvd.nist.gov/vuln/detail/CVE-2017-16618', 'https://github.com/advisories/GHSA-ccmq-qvcp-5mrm', 'https://github.com/tadashi-aikawa/owlmixin/commit/5d0575303f6df869a515ced4285f24ba721e0d4e', 'https://github.com/tadashi-aikawa/owlmixin/issues/12', 'https://github.com/tadashi-aikawa/owlmixin'} | null | {'https://github.com/tadashi-aikawa/owlmixin/commit/5d0575303f6df869a515ced4285f24ba721e0d4e'} | {'https://github.com/tadashi-aikawa/owlmixin/commit/5d0575303f6df869a515ced4285f24ba721e0d4e'} |
PyPI | PYSEC-2021-786 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions most implementations of convolution operators in TensorFlow are affected by a division by 0 vulnerability where an attacker can trigger a denial of service via a crash. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/framework/common_shape_fns.cc#L577) is missing several validations before doing divisions and modulo operations. We have patched the issue in GitHub commit 8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37675', 'GHSA-9c8h-2mv3-49ww'} | 2021-12-09T06:35:38.896417Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c8h-2mv3-49ww'} | null | {'https://github.com/tensorflow/tensorflow/commit/8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4'} | {'https://github.com/tensorflow/tensorflow/commit/8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4'} |
PyPI | PYSEC-2021-513 | null | TensorFlow is an end-to-end open source platform for machine learning. The TFLite computation for size of output after padding, `ComputeOutSize`(https://github.com/tensorflow/tensorflow/blob/0c9692ae7b1671c983569e5d3de5565843d500cf/tensorflow/lite/kernels/padding.h#L43-L55), does not check that the `stride` argument is not 0 before doing the division. Users can craft special models such that `ComputeOutSize` is called with `stride` set to 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-mv78-g7wq-mhp4', 'CVE-2021-29585'} | 2021-12-09T06:34:56.553875Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/49847ae69a4e1a97ae7f2db5e217c77721e37948', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv78-g7wq-mhp4'} | null | {'https://github.com/tensorflow/tensorflow/commit/49847ae69a4e1a97ae7f2db5e217c77721e37948'} | {'https://github.com/tensorflow/tensorflow/commit/49847ae69a4e1a97ae7f2db5e217c77721e37948'} |
PyPI | GHSA-m3f9-w3p3-p669 | Heap buffer overflow in `QuantizedMul` | ### Impact
An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization:
```python
import tensorflow as tf
x = tf.constant([256, 328], shape=[1, 2], dtype=tf.quint8)
y = tf.constant([256, 328], shape=[1, 2], dtype=tf.quint8)
min_x = tf.constant([], dtype=tf.float32)
max_x = tf.constant([], dtype=tf.float32)
min_y = tf.constant([], dtype=tf.float32)
max_y = tf.constant([], dtype=tf.float32)
tf.raw_ops.QuantizedMul(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly:
```cc
const float min_x = context->input(2).flat<float>()(0);
const float max_x = context->input(3).flat<float>()(0);
const float min_y = context->input(4).flat<float>()(0);
const float max_y = context->input(5).flat<float>()(0);
```
However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow.
### Patches
We have patched the issue in GitHub commit [efea03b38fb8d3b81762237dc85e579cc5fc6e87](https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29535'} | 2022-03-03T05:13:58.080072Z | 2021-05-21T14:22:28Z | LOW | null | {'CWE-787', 'CWE-131'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m3f9-w3p3-p669', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29535', 'https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87'} | null | {'https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87'} | {'https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87'} |
PyPI | PYSEC-2017-5 | null | An exploitable vulnerability exists in the yaml loading functionality of ansible-vault before 1.0.5. A specially crafted vault can execute arbitrary python commands resulting in command execution. An attacker can insert python into the vault to trigger this vulnerability. | {'CVE-2017-2809'} | 2021-07-05T00:01:14.915465Z | 2017-09-14T19:29:00Z | null | null | null | {'https://github.com/tomoh1r/ansible-vault/commit/3f8f659ef443ab870bb19f95d43543470168ae04', 'https://www.talosintelligence.com/vulnerability_reports/TALOS-2017-0305', 'http://www.securityfocus.com/bid/100824', 'https://github.com/tomoh1r/ansible-vault/blob/v1.0.5/CHANGES.txt', 'https://github.com/tomoh1r/ansible-vault/issues/4'} | null | {'https://github.com/tomoh1r/ansible-vault/commit/3f8f659ef443ab870bb19f95d43543470168ae04'} | {'https://github.com/tomoh1r/ansible-vault/commit/3f8f659ef443ab870bb19f95d43543470168ae04'} |
PyPI | PYSEC-2020-143 | null | tlslite-ng is an open source python library that implements SSL and TLS cryptographic protocols. In tlslite-ng before versions 0.7.6 and 0.8.0-alpha39, the code that performs decryption and padding check in RSA PKCS#1 v1.5 decryption is data dependant. In particular, the code has multiple ways in which it leaks information about the decrypted ciphertext. It aborts as soon as the plaintext doesn't start with 0x00, 0x02. All TLS servers that enable RSA key exchange as well as applications that use the RSA decryption API directly are vulnerable. This is patched in versions 0.7.6 and 0.8.0-alpha39. Note: the patches depend on Python processing the individual bytes in side-channel free manner, this is known to not the case (see reference). As such, users that require side-channel resistance are recommended to use different TLS implementations, as stated in the security policy of tlslite-ng. | {'CVE-2020-26263', 'GHSA-wvcv-832q-fjg7'} | 2020-12-23T16:09:00Z | 2020-12-21T17:15:00Z | null | null | null | {'https://github.com/tlsfuzzer/tlslite-ng/pull/439', 'https://pypi.org/project/tlslite-ng/', 'https://github.com/tlsfuzzer/tlslite-ng/security/advisories/GHSA-wvcv-832q-fjg7', 'https://securitypitfalls.wordpress.com/2018/08/03/constant-time-compare-in-python/', 'https://github.com/tlsfuzzer/tlslite-ng/commit/c28d6d387bba59d8bd5cb3ba15edc42edf54b368', 'https://github.com/tlsfuzzer/tlslite-ng/pull/438'} | null | {'https://github.com/tlsfuzzer/tlslite-ng/commit/c28d6d387bba59d8bd5cb3ba15edc42edf54b368'} | {'https://github.com/tlsfuzzer/tlslite-ng/commit/c28d6d387bba59d8bd5cb3ba15edc42edf54b368'} |
PyPI | PYSEC-2022-65 | null | Tensorflow is an Open Source Machine Learning Framework. ### Impact An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-428x-9xc2-m8mj', 'CVE-2022-21741'} | 2022-03-09T00:17:31.924375Z | 2022-02-03T15:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-428x-9xc2-m8mj', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/lite/kernels/depthwise_conv.cc#L96', 'https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc'} | null | {'https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc'} | {'https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc'} |
PyPI | PYSEC-2021-456 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedMul`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55900e961ed4a23b438392024912154a2c2f5e85/tensorflow/core/kernels/quantized_mul_op.cc#L188-L198) does a division by a quantity that is controlled by the caller. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29528', 'GHSA-6f84-42vf-ppwp'} | 2021-12-09T06:34:47.721328Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6f84-42vf-ppwp', 'https://github.com/tensorflow/tensorflow/commit/a1b11d2fdd1e51bfe18bb1ede804f60abfa92da6'} | null | {'https://github.com/tensorflow/tensorflow/commit/a1b11d2fdd1e51bfe18bb1ede804f60abfa92da6'} | {'https://github.com/tensorflow/tensorflow/commit/a1b11d2fdd1e51bfe18bb1ede804f60abfa92da6'} |
PyPI | PYSEC-2020-285 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15205', 'GHSA-g7p5-5759-qv46'} | 2021-12-09T06:34:42.433318Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/0462de5b544ed4731aa2fb23946ac22c01856b80', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/0462de5b544ed4731aa2fb23946ac22c01856b80'} | {'https://github.com/tensorflow/tensorflow/commit/0462de5b544ed4731aa2fb23946ac22c01856b80'} |
PyPI | GHSA-wvjw-p9f5-vq28 | Segfault in `tf.raw_ops.SparseCountSparseOutput` | ### Impact
Passing invalid arguments (e.g., discovered via fuzzing) to `tf.raw_ops.SparseCountSparseOutput` results in segfault.
### Patches
We have patched the issue in GitHub commit [82e6203221865de4008445b13c69b6826d2b28d9](https://github.com/tensorflow/tensorflow/commit/82e6203221865de4008445b13c69b6826d2b28d9).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2021-29619'} | 2022-03-03T05:13:41.308754Z | 2021-05-21T14:29:02Z | LOW | null | {'CWE-755'} | {'https://github.com/tensorflow/tensorflow/commit/82e6203221865de4008445b13c69b6826d2b28d9', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29619', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wvjw-p9f5-vq28'} | null | {'https://github.com/tensorflow/tensorflow/commit/82e6203221865de4008445b13c69b6826d2b28d9'} | {'https://github.com/tensorflow/tensorflow/commit/82e6203221865de4008445b13c69b6826d2b28d9'} |
PyPI | GHSA-ggmv-6q9p-9gm6 | Cross-site Scripting in django-unicorn | The Unicorn framework before 0.36.1 for Django allows XSS via a component. NOTE: this issue exists because of an incomplete fix for CVE-2021-42053. | {'CVE-2021-42134'} | 2022-03-03T05:13:20.261904Z | 2021-10-12T17:51:04Z | MODERATE | null | {'CWE-79'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-42134', 'https://github.com/adamghill/django-unicorn/commit/3a832a9e3f6455ddd3b87f646247269918ad10c6', 'https://github.com/adamghill/django-unicorn/compare/0.36.0...0.36.1', 'https://github.com/adamghill/django-unicorn'} | null | {'https://github.com/adamghill/django-unicorn/commit/3a832a9e3f6455ddd3b87f646247269918ad10c6'} | {'https://github.com/adamghill/django-unicorn/commit/3a832a9e3f6455ddd3b87f646247269918ad10c6'} |
PyPI | PYSEC-2021-285 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in `tf.raw_ops.QuantizeV2`, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-g25h-jr74-qp5j', 'CVE-2021-37663'} | 2021-08-27T03:22:45.209094Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g25h-jr74-qp5j'} | null | {'https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708'} | {'https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708'} |
PyPI | PYSEC-2021-790 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37679', 'GHSA-g8wg-cjwc-xhhp'} | 2021-12-09T06:35:39.261433Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp', 'https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12'} | null | {'https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12'} | {'https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12'} |
PyPI | GHSA-p2cq-cprg-frvm | Out of bounds write in tensorflow-lite | ### Impact
In TensorFlow Lite models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44
This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reference_ops.h#L2625-L2631
This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits.
### Patches
We have patched the issue in 204945b and will release patch releases for all affected versions.
We recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1.
### Workarounds
A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model.
A similar validation could be done if the segment ids are generated at runtime between inference steps.
If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2020-15214'} | 2021-08-26T15:20:06Z | 2020-09-25T18:28:56Z | HIGH | null | {'CWE-787'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15214', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2cq-cprg-frvm', 'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} |
PyPI | GHSA-9f66-54xg-pc2c | Open redirect vulnerability | ### Impact
_What kind of vulnerability is it? Who is impacted?_
Open redirect vulnerability - a maliciously crafted link to a jupyter server could redirect the browser to a different website.
All jupyter servers running without a base_url prefix are technically affected, however, these maliciously crafted links can only be reasonably made for known jupyter server hosts. A link to your jupyter server may *appear* safe, but ultimately redirect to a spoofed server on the public internet. This same vulnerability was patched in upstream notebook v5.7.8.
### Patches
_Has the problem been patched? What versions should users upgrade to?_
Patched in jupyter_server 1.1.1. If upgrade is not available, a workaround can be to run your server on a url prefix:
```
jupyter server --ServerApp.base_url=/jupyter/
```
### References
[OWASP page on open redirects](https://cheatsheetseries.owasp.org/cheatsheets/Unvalidated_Redirects_and_Forwards_Cheat_Sheet.html)
### For more information
If you have any questions or comments about this advisory, or vulnerabilities to report, please email our security list [security@ipython.org](mailto:security@ipython.org).
Credit: Yaniv Nizry from CxSCA group at Checkmarx | {'CVE-2020-26275'} | 2022-03-03T05:13:03.409856Z | 2020-12-21T18:01:41Z | LOW | null | {'CWE-601'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-26275', 'https://github.com/jupyter-server/jupyter_server/commit/85e4abccf6ea9321d29153f73b0bd72ccb3a6bca', 'https://advisory.checkmarx.net/advisory/CX-2020-4291', 'https://pypi.org/project/jupyter-server/', 'https://github.com/jupyter-server/jupyter_server/security/advisories/GHSA-9f66-54xg-pc2c'} | null | {'https://github.com/jupyter-server/jupyter_server/commit/85e4abccf6ea9321d29153f73b0bd72ccb3a6bca'} | {'https://github.com/jupyter-server/jupyter_server/commit/85e4abccf6ea9321d29153f73b0bd72ccb3a6bca'} |
PyPI | GHSA-56wv-2wr9-3h9r | Improper Verification of Cryptographic Signature in fastecdsa | An issue was discovered in fastecdsa before 2.1.2. When using the NIST P-256 curve in the ECDSA implementation, the point at infinity is mishandled. This means that for an extreme value in k and s^-1, the signature verification fails even if the signature is correct. This behavior is not solely a usability problem. There are some threat models where an attacker can benefit by successfully guessing users for whom signature verification will fail. | {'CVE-2020-12607'} | 2022-03-03T05:13:42.856644Z | 2021-10-12T16:30:37Z | HIGH | null | {'CWE-347'} | {'https://github.com/AntonKueltz/fastecdsa/commit/e592f106edd5acf6dacedfab2ad16fe6c735c9d1', 'https://nvd.nist.gov/vuln/detail/CVE-2020-12607', 'https://github.com/AntonKueltz/fastecdsa/commit/4a16daeaf139be20654ef58a9fe4c79dc030458c', 'https://github.com/AntonKueltz/fastecdsa/issues/52', 'https://github.com/AntonKueltz/fastecdsa', 'https://github.com/AntonKueltz/fastecdsa/commit/7b64e3efaa806b4daaf73bb5172af3581812f8de'} | null | {'https://github.com/AntonKueltz/fastecdsa/commit/7b64e3efaa806b4daaf73bb5172af3581812f8de', 'https://github.com/AntonKueltz/fastecdsa/commit/4a16daeaf139be20654ef58a9fe4c79dc030458c', 'https://github.com/AntonKueltz/fastecdsa/commit/e592f106edd5acf6dacedfab2ad16fe6c735c9d1'} | {'https://github.com/AntonKueltz/fastecdsa/commit/e592f106edd5acf6dacedfab2ad16fe6c735c9d1', 'https://github.com/AntonKueltz/fastecdsa/commit/7b64e3efaa806b4daaf73bb5172af3581812f8de', 'https://github.com/AntonKueltz/fastecdsa/commit/4a16daeaf139be20654ef58a9fe4c79dc030458c'} |
PyPI | PYSEC-2020-114 | null | In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to `dlpack.to_dlpack` the expected validations will cause variables to bind to `nullptr` while setting a `status` variable to the error condition. However, this `status` argument is not properly checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1. | {'GHSA-q8qj-fc9q-cphr', 'CVE-2020-15191'} | 2021-09-01T08:19:32.360913Z | 2020-09-25T19:15:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8qj-fc9q-cphr', 'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} |
PyPI | PYSEC-2021-817 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for `SparseCountSparseOutput` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-m342-ff57-4jcc', 'CVE-2021-41210'} | 2021-12-09T06:35:42.682170Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m342-ff57-4jcc'} | null | {'https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2'} | {'https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2'} |
PyPI | GHSA-6r97-cj55-9hrq | SQL Injection in Django | An issue was discovered in Django 1.11.x before 1.11.23, 2.1.x before 2.1.11, and 2.2.x before 2.2.4. Due to an error in shallow key transformation, key and index lookups for django.contrib.postgres.fields.JSONField, and key lookups for django.contrib.postgres.fields.HStoreField, were subject to SQL injection. This could, for example, be exploited via crafted use of "OR 1=1" in a key or index name to return all records, using a suitably crafted dictionary, with dictionary expansion, as the **kwargs passed to the QuerySet.filter() function. | {'CVE-2019-14234'} | 2022-03-21T21:16:55.353080Z | 2019-08-16T14:00:34Z | CRITICAL | null | {'CWE-89'} | {'https://www.djangoproject.com/weblog/2019/aug/01/security-releases/', 'https://github.com/django/django/commit/4f5b58f5cd3c57fee9972ab074f8dc6895d8f387', 'https://groups.google.com/forum/#!topic/django-announce/jIoju2-KLDs', 'https://nvd.nist.gov/vuln/detail/CVE-2019-14234'} | null | {'https://github.com/django/django/commit/4f5b58f5cd3c57fee9972ab074f8dc6895d8f387'} | {'https://github.com/django/django/commit/4f5b58f5cd3c57fee9972ab074f8dc6895d8f387'} |
PyPI | PYSEC-2021-544 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of TrySimplify(https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that result in optimizing a node with no inputs. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-4hvv-7x94-7vq8', 'CVE-2021-29616'} | 2021-12-09T06:35:01.440204Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvv-7x94-7vq8'} | null | {'https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3'} | {'https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3'} |
PyPI | PYSEC-2021-401 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementations for convolution operators trigger a division by 0 if passed empty filter tensor arguments. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-6hpv-v2rx-c5g6', 'CVE-2021-41209'} | 2021-11-13T06:52:43.607331Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hpv-v2rx-c5g6'} | null | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} |
PyPI | PYSEC-2022-32 | null | B2 Command Line Tool is the official command line tool for the backblaze cloud storage service. Linux and Mac releases of the B2 command-line tool version 3.2.0 and below contain a key disclosure vulnerability that, in certain conditions, can be exploited by local attackers through a time-of-check-time-of-use (TOCTOU) race condition. The command line tool saves API keys (and bucket name-to-id mapping) in a local database file (`$XDG_CONFIG_HOME/b2/account_info`, `~/.b2_account_info` or a user-defined path) when `b2 authorize-account` is first run. This happens regardless of whether a valid key is provided or not. When first created, the file is world readable and is (typically a few milliseconds) later altered to be private to the user. If the directory is readable by a local attacker and the user did not yet run `b2 authorize-account` then during the brief period between file creation and permission modification, a local attacker can race to open the file and maintain a handle to it. This allows the local attacker to read the contents after the file after the sensitive information has been saved to it. Users that have not yet run `b2 authorize-account` should upgrade to B2 Command-Line Tool v3.2.1 before running it. Users that have run `b2 authorize-account` are safe if at the time of the file creation no other local users had read access to the local configuration file. Users that have run `b2 authorize-account` where the designated path could be opened by another local user should upgrade to B2 Command-Line Tool v3.2.1 and remove the database and regenerate all application keys. Note that `b2 clear-account` does not remove the database file and it should not be used to ensure that all open handles to the file are invalidated. If B2 Command-Line Tool cannot be upgraded to v3.2.1 due to a dependency conflict, a binary release can be used instead. Alternatively a new version could be installed within a virtualenv, or the permissions can be changed to prevent local users from opening the database file. | {'CVE-2022-23653', 'GHSA-8wr4-2wm6-w3pr'} | 2022-03-07T17:33:45.997096Z | 2022-02-23T23:15:00Z | null | null | null | {'https://github.com/Backblaze/B2_Command_Line_Tool/security/advisories/GHSA-8wr4-2wm6-w3pr', 'https://github.com/Backblaze/B2_Command_Line_Tool/commit/c74029f9f75065e8f7e3c3ec8e0a23fb8204feeb'} | null | {'https://github.com/Backblaze/B2_Command_Line_Tool/commit/c74029f9f75065e8f7e3c3ec8e0a23fb8204feeb'} | {'https://github.com/Backblaze/B2_Command_Line_Tool/commit/c74029f9f75065e8f7e3c3ec8e0a23fb8204feeb'} |
PyPI | GHSA-8jj7-5vxc-pg2q | Integer overflow in TensorFlow | ### Impact
Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during [cost estimation for crop and resize](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L2621-L2689). Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior.
### Patches
We have patched the issue in GitHub commit [0aaaae6eca5a7175a193696383f582f53adab23f](https://github.com/tensorflow/tensorflow/commit/0aaaae6eca5a7175a193696383f582f53adab23f).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2022-23587'} | 2022-03-03T05:13:04.949665Z | 2022-02-09T23:27:49Z | HIGH | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/commit/0aaaae6eca5a7175a193696383f582f53adab23f', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L2621-L2689', 'https://github.com/tensorflow/tensorflow/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23587', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8jj7-5vxc-pg2q'} | null | {'https://github.com/tensorflow/tensorflow/commit/0aaaae6eca5a7175a193696383f582f53adab23f'} | {'https://github.com/tensorflow/tensorflow/commit/0aaaae6eca5a7175a193696383f582f53adab23f'} |
PyPI | PYSEC-2021-728 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `DepthwiseConv` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/depthwise_conv.cc#L287-L288). An attacker can craft a model such that `input`'s fourth dimension would be 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-rf3h-xgv5-2q39', 'CVE-2021-29602'} | 2021-12-09T06:35:32.373015Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/cbda3c6b2dbbd3fbdc482ff8c0170a78ec2e97d0', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rf3h-xgv5-2q39'} | null | {'https://github.com/tensorflow/tensorflow/commit/cbda3c6b2dbbd3fbdc482ff8c0170a78ec2e97d0'} | {'https://github.com/tensorflow/tensorflow/commit/cbda3c6b2dbbd3fbdc482ff8c0170a78ec2e97d0'} |
PyPI | GHSA-wm93-f238-7v37 | Integer overflow in Tensorflow | ### Impact
The [implementation of `OpLevelCostEstimator::CalculateOutputSize`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L1598-L1617) is vulnerable to an integer overflow if an attacker can create an operation which would involve tensors with large enough number of elements:
```cc
for (const auto& dim : output_shape.dim()) {
output_size *= dim.size();
}
```
Here, we can have a large enough number of dimensions in `output_shape.dim()` or just a small number of dimensions being large enough to cause an overflow in the multiplication.
### Patches
We have patched the issue in GitHub commit [b9bd6cfd1c50e6807846af9a86f9b83cafc9c8ae](https://github.com/tensorflow/tensorflow/commit/b9bd6cfd1c50e6807846af9a86f9b83cafc9c8ae).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
| {'CVE-2022-23576'} | 2022-03-03T05:13:44.914237Z | 2022-02-10T00:32:44Z | MODERATE | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L1598-L1617', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23576', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/commit/b9bd6cfd1c50e6807846af9a86f9b83cafc9c8ae', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wm93-f238-7v37'} | null | {'https://github.com/tensorflow/tensorflow/commit/b9bd6cfd1c50e6807846af9a86f9b83cafc9c8ae'} | {'https://github.com/tensorflow/tensorflow/commit/b9bd6cfd1c50e6807846af9a86f9b83cafc9c8ae'} |
PyPI | PYSEC-2021-682 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.Reverse`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-fxqh-cfjm-fp93', 'CVE-2021-29556'} | 2021-12-09T06:35:24.446471Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxqh-cfjm-fp93'} | null | {'https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1'} | {'https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1'} |
PyPI | GHSA-g7p5-5759-qv46 | Data leak in Tensorflow | ### Impact
The `data_splits` argument of [`tf.raw_ops.StringNGrams`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/StringNGrams) lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory
```python
>>> tf.raw_ops.StringNGrams(data=["aa", "bb", "cc", "dd", "ee", "ff"], data_splits=[0,8], separator=" ", ngram_widths=[3], left_pad="", right_pad="", pad_width=0, preserve_short_sequences=False)
StringNGrams(ngrams=<tf.Tensor: shape=(6,), dtype=string, numpy=
array([b'aa bb cc', b'bb cc dd', b'cc dd ee', b'dd ee ff',
b'ee ff \xf4j\xa7q\x7f\x00\x00q\x00\x00\x00\x00\x00\x00\x00\xd8\x9b~\xa8q\x7f\x00',
b'ff \xf4j\xa7q\x7f\x00\x00q\x00\x00\x00\x00\x00\x00\x00\xd8\x9b~\xa8q\x7f\x00 \x9b~\xa8q\x7f\x00\x00p\xf5j\xa7q\x7f\x00\x00H\xf8j\xa7q\x7f\x00\x00\xf0\xf3\xf7\x85q\x7f\x00\x00`}\xa6\x00\x00\x00\x00\x00`~\xa6\x00\x00\x00\x00\x00\xb0~\xeb\x9bq\x7f\x00'],...
```
All the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR.
### Patches
We have patched the issue in 0462de5b544ed4731aa2fb23946ac22c01856b80 and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2020-15205'} | 2022-03-03T05:14:10.912571Z | 2020-09-25T18:28:38Z | CRITICAL | null | {'CWE-787', 'CWE-119', 'CWE-122'} | {'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15205', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46', 'https://github.com/tensorflow/tensorflow/commit/0462de5b544ed4731aa2fb23946ac22c01856b80', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/0462de5b544ed4731aa2fb23946ac22c01856b80'} | {'https://github.com/tensorflow/tensorflow/commit/0462de5b544ed4731aa2fb23946ac22c01856b80'} |
PyPI | PYSEC-2021-397 | null | TensorFlow is an open source platform for machine learning. In affected versions during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-786j-5qwq-r36x', 'CVE-2021-41204'} | 2021-11-13T06:52:42.949977Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-786j-5qwq-r36x', 'https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659'} | null | {'https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659'} | {'https://github.com/tensorflow/tensorflow/commit/7731e8dfbe4a56773be5dc94d631611211156659'} |
PyPI | PYSEC-2021-22 | null | Sydent is a reference Matrix identity server. Sydent can be induced to send HTTP GET requests to internal systems, due to lack of parameter validation or IP address blacklisting. It is not possible to exfiltrate data or control request headers, but it might be possible to use the attack to perform an internal port enumeration. This issue has been addressed in in 9e57334, 8936925, 3d531ed, 0f00412. A potential workaround would be to use a firewall to ensure that Sydent cannot reach internal HTTP resources. | {'CVE-2021-29431', 'GHSA-9jhm-8m8c-c3f4'} | 2021-04-22T15:27:00Z | 2021-04-15T21:15:00Z | null | null | null | {'https://github.com/matrix-org/sydent/commit/9e573348d81df8191bbe8c266c01999c9d57cd5f', 'https://github.com/matrix-org/sydent/releases/tag/v2.3.0', 'https://github.com/matrix-org/sydent/commit/8936925f561b0c352c2fa922d5097d7245aad00a', 'https://github.com/matrix-org/sydent/commit/0f00412017f25619bc36c264b29ea96808bf310a', 'https://pypi.org/project/matrix-sydent/', 'https://github.com/matrix-org/sydent/commit/3d531ed50d2fd41ac387f36d44d3fb2c62dd22d3', 'https://github.com/matrix-org/sydent/security/advisories/GHSA-9jhm-8m8c-c3f4'} | null | {'https://github.com/matrix-org/sydent/commit/0f00412017f25619bc36c264b29ea96808bf310a', 'https://github.com/matrix-org/sydent/commit/8936925f561b0c352c2fa922d5097d7245aad00a', 'https://github.com/matrix-org/sydent/commit/3d531ed50d2fd41ac387f36d44d3fb2c62dd22d3', 'https://github.com/matrix-org/sydent/commit/9e573348d81df8191bbe8c266c01999c9d57cd5f'} | {'https://github.com/matrix-org/sydent/commit/0f00412017f25619bc36c264b29ea96808bf310a', 'https://github.com/matrix-org/sydent/commit/9e573348d81df8191bbe8c266c01999c9d57cd5f', 'https://github.com/matrix-org/sydent/commit/3d531ed50d2fd41ac387f36d44d3fb2c62dd22d3', 'https://github.com/matrix-org/sydent/commit/8936925f561b0c352c2fa922d5097d7245aad00a'} |
PyPI | GHSA-34f9-hjfq-rr8j | Overflow and uncaught divide by zero in Tensorflow | ### Impact
The [implementation of `UnravelIndex`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/unravel_index_op.cc#L36-L135) is vulnerable to a division by zero caused by an integer overflow bug:
```python
import tensorflow as tf
tf.raw_ops.UnravelIndex(indices=-0x100000,dims=[0x100000,0x100000])
```
### Patches
We have patched the issue in GitHub commit [58b34c6c8250983948b5a781b426f6aa01fd47af](https://github.com/tensorflow/tensorflow/commit/58b34c6c8250983948b5a781b426f6aa01fd47af).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team. | {'CVE-2022-21729'} | 2022-03-03T05:13:12.330440Z | 2022-02-10T00:18:53Z | MODERATE | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-34f9-hjfq-rr8j', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/unravel_index_op.cc#L36-L135', 'https://github.com/tensorflow/tensorflow/commit/58b34c6c8250983948b5a781b426f6aa01fd47af', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21729'} | null | {'https://github.com/tensorflow/tensorflow/commit/58b34c6c8250983948b5a781b426f6aa01fd47af'} | {'https://github.com/tensorflow/tensorflow/commit/58b34c6c8250983948b5a781b426f6aa01fd47af'} |
PyPI | PYSEC-2021-180 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.CTCGreedyDecoder`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1615440b17b364b875eb06f43d087381f1460a65/tensorflow/core/kernels/ctc_decoder_ops.cc#L37-L50) has a `CHECK_LT` inserted to validate some invariants. When this condition is false, the program aborts, instead of returning a valid error to the user. This abnormal termination can be weaponized in denial of service attacks. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-fphq-gw9m-ghrv', 'CVE-2021-29543'} | 2021-08-27T03:22:29.100995Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fphq-gw9m-ghrv', 'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2'} | null | {'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2'} | {'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2'} |
PyPI | PYSEC-2022-49 | null | Tensorflow is an Open Source Machine Learning Framework. The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-v3f7-j968-4h5f', 'CVE-2022-21725'} | 2022-03-09T00:17:29.922594Z | 2022-02-03T13:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f', 'https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198', 'https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a'} | null | {'https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a'} | {'https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a'} |
PyPI | PYSEC-2021-495 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.SparseDenseCwiseMul`, an attacker can trigger denial of service via `CHECK`-fails or accesses to outside the bounds of heap allocated data. Since the implementation(https://github.com/tensorflow/tensorflow/blob/38178a2f7a681a7835bb0912702a134bfe3b4d84/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L68-L80) only validates the rank of the input arguments but no constraints between dimensions(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseDenseCwiseMul), an attacker can abuse them to trigger internal `CHECK` assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29567', 'GHSA-wp3c-xw9g-gpcg'} | 2021-12-09T06:34:53.752999Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp3c-xw9g-gpcg', 'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc'} | null | {'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc'} | {'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc'} |
PyPI | PYSEC-2021-5 | null | CairoSVG is a Python (pypi) package. CairoSVG is an SVG converter based on Cairo. In CairoSVG before version 2.5.1, there is a regular expression denial of service (REDoS) vulnerability. When processing SVG files, the python package CairoSVG uses two regular expressions which are vulnerable to Regular Expression Denial of Service (REDoS). If an attacker provides a malicious SVG, it can make cairosvg get stuck processing the file for a very long time. This is fixed in version 2.5.1. See Referenced GitHub advisory for more information. | {'CVE-2021-21236', 'GHSA-hq37-853p-g5cf'} | 2021-01-13T15:43:00Z | 2021-01-06T17:15:00Z | null | null | null | {'https://github.com/Kozea/CairoSVG/commit/cfc9175e590531d90384aa88845052de53d94bf3', 'https://pypi.org/project/CairoSVG/', 'https://github.com/Kozea/CairoSVG/releases/tag/2.5.1', 'https://github.com/Kozea/CairoSVG/security/advisories/GHSA-hq37-853p-g5cf'} | null | {'https://github.com/Kozea/CairoSVG/commit/cfc9175e590531d90384aa88845052de53d94bf3'} | {'https://github.com/Kozea/CairoSVG/commit/cfc9175e590531d90384aa88845052de53d94bf3'} |
PyPI | GHSA-r6pg-pjwc-j585 | Null pointer dereference in `SparseFillEmptyRows` | ### Impact
An attacker can trigger a null pointer dereference in the implementation of `tf.raw_ops.SparseFillEmptyRows`:
```python
import tensorflow as tf
indices = tf.constant([], shape=[0, 0], dtype=tf.int64)
values = tf.constant([], shape=[0], dtype=tf.int64)
dense_shape = tf.constant([], shape=[0], dtype=tf.int64)
default_value = 0
tf.raw_ops.SparseFillEmptyRows(
indices=indices, values=values, dense_shape=dense_shape,
default_value=default_value)
```
This is because of missing [validation](https://github.com/tensorflow/tensorflow/blob/fdc82089d206e281c628a93771336bf87863d5e8/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L230-L231) that was covered under a `TODO`. If the `dense_shape` tensor is empty, then `dense_shape_t.vec<>()` would cause a null pointer dereference in the implementation of the op:
```cc
template <typename T, typename Tindex>
struct SparseFillEmptyRows<CPUDevice, T, Tindex> {
Status operator()(OpKernelContext* context, const Tensor& default_value_t,
const Tensor& indices_t, const Tensor& values_t,
const Tensor& dense_shape_t,
typename AsyncOpKernel::DoneCallback done) {
...
const auto dense_shape = dense_shape_t.vec<Tindex>();
...
}
}
```
### Patches
We have patched the issue in GitHub commit [faa76f39014ed3b5e2c158593b1335522e573c7f](https://github.com/tensorflow/tensorflow/commit/faa76f39014ed3b5e2c158593b1335522e573c7f).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. | {'CVE-2021-29565'} | 2022-03-03T05:14:21.468966Z | 2021-05-21T14:25:11Z | LOW | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/commit/faa76f39014ed3b5e2c158593b1335522e573c7f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r6pg-pjwc-j585', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29565'} | null | {'https://github.com/tensorflow/tensorflow/commit/faa76f39014ed3b5e2c158593b1335522e573c7f'} | {'https://github.com/tensorflow/tensorflow/commit/faa76f39014ed3b5e2c158593b1335522e573c7f'} |
PyPI | GHSA-7pxj-m4jf-r6h2 | Missing validation during checkpoint loading | ### Impact
An attacker can trigger undefined behavior, integer overflows, segfaults and `CHECK`-fail crashes if they can change saved checkpoints from outside of TensorFlow.
This is because the checkpoints loading infrastructure is missing validation for invalid file formats.
### Patches
We have patched the issue in GitHub commits [b619c6f865715ca3b15ef1842b5b95edbaa710ad](https://github.com/tensorflow/tensorflow/commit/b619c6f865715ca3b15ef1842b5b95edbaa710ad), [e8dc63704c88007ee4713076605c90188d66f3d2](https://github.com/tensorflow/tensorflow/commit/e8dc63704c88007ee4713076605c90188d66f3d2), [368af875869a204b4ac552b9ddda59f6a46a56ec](https://github.com/tensorflow/tensorflow/commit/368af875869a204b4ac552b9ddda59f6a46a56ec), and [abcced051cb1bd8fb05046ac3b6023a7ebcc4578](https://github.com/tensorflow/tensorflow/commit/abcced051cb1bd8fb05046ac3b6023a7ebcc4578).
These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2021-41203'} | 2022-03-03T05:12:50.093057Z | 2021-11-10T19:12:46Z | HIGH | null | {'CWE-345'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-41203', 'https://github.com/tensorflow/tensorflow/commit/e8dc63704c88007ee4713076605c90188d66f3d2', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/b619c6f865715ca3b15ef1842b5b95edbaa710ad', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7pxj-m4jf-r6h2', 'https://github.com/tensorflow/tensorflow/commit/368af875869a204b4ac552b9ddda59f6a46a56ec', 'https://github.com/tensorflow/tensorflow/commit/abcced051cb1bd8fb05046ac3b6023a7ebcc4578'} | null | {'https://github.com/tensorflow/tensorflow/commit/e8dc63704c88007ee4713076605c90188d66f3d2', 'https://github.com/tensorflow/tensorflow/commit/abcced051cb1bd8fb05046ac3b6023a7ebcc4578', 'https://github.com/tensorflow/tensorflow/commit/b619c6f865715ca3b15ef1842b5b95edbaa710ad', 'https://github.com/tensorflow/tensorflow/commit/368af875869a204b4ac552b9ddda59f6a46a56ec'} | {'https://github.com/tensorflow/tensorflow/commit/e8dc63704c88007ee4713076605c90188d66f3d2', 'https://github.com/tensorflow/tensorflow/commit/abcced051cb1bd8fb05046ac3b6023a7ebcc4578', 'https://github.com/tensorflow/tensorflow/commit/b619c6f865715ca3b15ef1842b5b95edbaa710ad', 'https://github.com/tensorflow/tensorflow/commit/368af875869a204b4ac552b9ddda59f6a46a56ec'} |
PyPI | PYSEC-2021-59 | null | The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted. | {'GHSA-5phf-pp7p-vc2r', 'CVE-2021-28363'} | 2021-03-23T16:47:00Z | 2021-03-15T18:15:00Z | null | null | null | {'https://github.com/urllib3/urllib3/security/advisories/GHSA-5phf-pp7p-vc2r', 'https://pypi.org/project/urllib3/1.26.4/', 'https://github.com/urllib3/urllib3/commit/8d65ea1ecf6e2cdc27d42124e587c1b83a3118b0', 'https://github.com/urllib3/urllib3/commits/main'} | null | {'https://github.com/urllib3/urllib3/commit/8d65ea1ecf6e2cdc27d42124e587c1b83a3118b0'} | {'https://github.com/urllib3/urllib3/commit/8d65ea1ecf6e2cdc27d42124e587c1b83a3118b0'} |
PyPI | PYSEC-2021-616 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `ParallelConcat` misses some input validation and can produce a division by 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-7v94-64hj-m82h', 'CVE-2021-41207'} | 2021-12-09T06:35:08.527679Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7v94-64hj-m82h', 'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} | null | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} |
PyPI | PYSEC-2021-246 | null | TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-cjc7-49v2-jp64', 'CVE-2021-29609'} | 2021-08-27T03:22:40.807777Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/41727ff06111117bdf86b37db198217fd7a143cc', 'https://github.com/tensorflow/tensorflow/commit/6fd02f44810754ae7481838b6a67c5df7f909ca3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cjc7-49v2-jp64'} | null | {'https://github.com/tensorflow/tensorflow/commit/41727ff06111117bdf86b37db198217fd7a143cc', 'https://github.com/tensorflow/tensorflow/commit/6fd02f44810754ae7481838b6a67c5df7f909ca3'} | {'https://github.com/tensorflow/tensorflow/commit/41727ff06111117bdf86b37db198217fd7a143cc', 'https://github.com/tensorflow/tensorflow/commit/6fd02f44810754ae7481838b6a67c5df7f909ca3'} |
PyPI | PYSEC-2021-753 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.ResourceScatterDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/resource_variable_ops.cc#L865) uses a common class for all binary operations but fails to treat the division by 0 case separately. We have patched the issue in GitHub commit 4aacb30888638da75023e6601149415b39763d76. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37642', 'GHSA-ch4f-829c-v5pw'} | 2021-12-09T06:35:35.943696Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/4aacb30888638da75023e6601149415b39763d76', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-ch4f-829c-v5pw'} | null | {'https://github.com/tensorflow/tensorflow/commit/4aacb30888638da75023e6601149415b39763d76'} | {'https://github.com/tensorflow/tensorflow/commit/4aacb30888638da75023e6601149415b39763d76'} |
PyPI | PYSEC-2021-303 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of SVDF in TFLite is [vulnerable to a null pointer error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/svdf.cc#L300-L313). The [`GetVariableInput` function](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L115-L119) can return a null pointer but `GetTensorData` assumes that the argument is always a valid tensor. Furthermore, because `GetVariableInput` calls [`GetMutableInput`](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L82-L90) which might return `nullptr`, the `tensor->is_variable` expression can also trigger a null pointer exception. We have patched the issue in GitHub commit 5b048e87e4e55990dae6b547add4dae59f4e1c76. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37681', 'GHSA-7xwj-5r4v-429p'} | 2021-08-27T03:22:46.881278Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5b048e87e4e55990dae6b547add4dae59f4e1c76', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7xwj-5r4v-429p'} | null | {'https://github.com/tensorflow/tensorflow/commit/5b048e87e4e55990dae6b547add4dae59f4e1c76'} | {'https://github.com/tensorflow/tensorflow/commit/5b048e87e4e55990dae6b547add4dae59f4e1c76'} |
PyPI | GHSA-p75f-g7gx-2r7p | Exposure of Sensitive Information to an Unauthorized Actor in Products.PluggableAuthService ZODBRoleManager | ### Impact
_What kind of vulnerability is it? Who is impacted?_
Information disclosure vulnerability - everyone can list the names of roles defined in the ZODB Role Manager plugin if the site uses this plugin.
### Patches
_Has the problem been patched? What versions should users upgrade to?_
The problem has been fixed in version 2.6.0. Depending on how you have installed Products.PluggableAuthService, you should change the buildout version pin to 2.6.0 and re-run the buildout, or if you used pip simply do `pip install "Products.PluggableAuthService>=2.6.0"`
### Workarounds
_Is there a way for users to fix or remediate the vulnerability without upgrading?_
There is no workaround. Users are encouraged to upgrade.
### References
_Are there any links users can visit to find out more?_
- [GHSA-p75f-g7gx-2r7p](https://github.com/zopefoundation/Products.PluggableAuthService/security/advisories/GHSA-p75f-g7gx-2r7p)
- [Products.PluggableAuthService on PyPI](https://github.com/zopefoundation/Products.PluggableAuthService)
### For more information
If you have any questions or comments about this advisory:
* Open an issue in the [Products.PluggableAuthService issue tracker](https://github.com/zopefoundation/Products.PluggableAuthService/issues)
* Email us at [security@plone.org](mailto:security@plone.org) | {'CVE-2021-21336'} | 2022-03-03T05:13:33.578356Z | 2021-03-08T20:38:35Z | LOW | null | {'CWE-200'} | {'https://github.com/zopefoundation/Products.PluggableAuthService/security/advisories/GHSA-p75f-g7gx-2r7p', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21336', 'http://www.openwall.com/lists/oss-security/2021/05/22/1', 'http://www.openwall.com/lists/oss-security/2021/05/21/1', 'https://pypi.org/project/Products.PluggableAuthService/', 'https://github.com/zopefoundation/Products.PluggableAuthService/commit/2dad81128250cb2e5d950cddc9d3c0314a80b4bb'} | null | {'https://github.com/zopefoundation/Products.PluggableAuthService/commit/2dad81128250cb2e5d950cddc9d3c0314a80b4bb'} | {'https://github.com/zopefoundation/Products.PluggableAuthService/commit/2dad81128250cb2e5d950cddc9d3c0314a80b4bb'} |
PyPI | PYSEC-2020-303 | null | In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled. This issue is patched in TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched. TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected. Users are encouraged to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0. | {'GHSA-977j-xj7q-2jr9', 'CVE-2020-5215'} | 2021-12-09T06:34:45.123200Z | 2020-01-28T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-977j-xj7q-2jr9', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.0.1', 'https://github.com/tensorflow/tensorflow/commit/5ac1b9e24ff6afc465756edf845d2e9660bd34bf', 'https://github.com/tensorflow/tensorflow/releases/tag/v1.15.2'} | null | {'https://github.com/tensorflow/tensorflow/commit/5ac1b9e24ff6afc465756edf845d2e9660bd34bf'} | {'https://github.com/tensorflow/tensorflow/commit/5ac1b9e24ff6afc465756edf845d2e9660bd34bf'} |
PyPI | GHSA-p44j-xrqg-4xrr | URL Redirection to Untrusted Site ('Open Redirect') in Products.PluggableAuthService | ### Impact
_What kind of vulnerability is it? Who is impacted?_
Open redirect vulnerability - a maliciously crafted link to the login form and login functionality could redirect the browser to a different website.
### Patches
_Has the problem been patched? What versions should users upgrade to?_
The problem has been fixed in version 2.6.1. Depending on how you have installed Products.PluggableAuthService, you should change the buildout version pin to `2.6.1` and re-run the buildout, or if you used `pip` simply do `pip install "Products.PluggableAuthService>=2.6.1"`
### Workarounds
_Is there a way for users to fix or remediate the vulnerability without upgrading?_
There is no workaround. Users are encouraged to upgrade.
### References
_Are there any links users can visit to find out more?_
- [GHSA-p44j-xrqg-4xrr](https://github.com/zopefoundation/Products.PluggableAuthService/security/advisories/GHSA-p44j-xrqg-4xrr)
- [Products.PluggableAuthService on PyPI](https://pypi.org/project/Products.PluggableAuthService/)
- [OWASP page on open redirects](https://cheatsheetseries.owasp.org/cheatsheets/Unvalidated_Redirects_and_Forwards_Cheat_Sheet.html)
### For more information
If you have any questions or comments about this advisory:
* Open an issue in the [Products.PluggableAuthService issue tracker](https://github.com/zopefoundation/Products.PluggableAuthService/issues)
* Email us at [security@plone.org](mailto:security@plone.org) | {'CVE-2021-21337'} | 2022-03-03T05:13:28.916575Z | 2021-03-08T21:06:23Z | LOW | null | {'CWE-601'} | {'https://github.com/zopefoundation/Products.PluggableAuthService/commit/7eead067898852ebd3e0f143bc51295928528dfa', 'https://github.com/zopefoundation/Products.PluggableAuthService/security/advisories/GHSA-p44j-xrqg-4xrr', 'https://pypi.org/project/Products.PluggableAuthService/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21337', 'http://packetstormsecurity.com/files/162911/Products.PluggableAuthService-2.6.0-Open-Redirect.html'} | null | {'https://github.com/zopefoundation/Products.PluggableAuthService/commit/7eead067898852ebd3e0f143bc51295928528dfa'} | {'https://github.com/zopefoundation/Products.PluggableAuthService/commit/7eead067898852ebd3e0f143bc51295928528dfa'} |
PyPI | PYSEC-2022-131 | null | Tensorflow is an Open Source Machine Learning Framework. The implementations of `Sparse*Cwise*` ops are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or `CHECK`-fails when building new `TensorShape` objects (so, assert failures based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-rrx2-r989-2c43', 'CVE-2022-23567'} | 2022-03-09T00:18:26.570322Z | 2022-02-03T12:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1b54cadd19391b60b6fcccd8d076426f7221d5e8', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc', 'https://github.com/tensorflow/tensorflow/commit/e952a89b7026b98fe8cbe626514a93ed68b7c510', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrx2-r989-2c43', 'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md'} | null | {'https://github.com/tensorflow/tensorflow/commit/1b54cadd19391b60b6fcccd8d076426f7221d5e8', 'https://github.com/tensorflow/tensorflow/commit/e952a89b7026b98fe8cbe626514a93ed68b7c510'} | {'https://github.com/tensorflow/tensorflow/commit/1b54cadd19391b60b6fcccd8d076426f7221d5e8', 'https://github.com/tensorflow/tensorflow/commit/e952a89b7026b98fe8cbe626514a93ed68b7c510'} |
PyPI | PYSEC-2021-587 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a segmentation fault in `tf.raw_ops.MaxPoolGrad` caused by missing validation. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/maxpooling_op.cc) misses some validation for the `orig_input` and `orig_output` tensors. The fixes for CVE-2021-29579 were incomplete. We have patched the issue in GitHub commit 136b51f10903e044308cf77117c0ed9871350475. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-7ghq-fvr3-pj2x', 'CVE-2021-37674'} | 2021-12-09T06:35:05.322708Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7ghq-fvr3-pj2x', 'https://github.com/tensorflow/tensorflow/commit/136b51f10903e044308cf77117c0ed9871350475', 'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-068.md'} | null | {'https://github.com/tensorflow/tensorflow/commit/136b51f10903e044308cf77117c0ed9871350475'} | {'https://github.com/tensorflow/tensorflow/commit/136b51f10903e044308cf77117c0ed9871350475'} |
PyPI | PYSEC-2021-568 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. We have patched the issue in GitHub commit 01cff3f986259d661103412a20745928c727326f. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-7fvx-3jfc-2cpc', 'CVE-2021-37655'} | 2021-12-09T06:35:03.682408Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/01cff3f986259d661103412a20745928c727326f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7fvx-3jfc-2cpc'} | null | {'https://github.com/tensorflow/tensorflow/commit/01cff3f986259d661103412a20745928c727326f'} | {'https://github.com/tensorflow/tensorflow/commit/01cff3f986259d661103412a20745928c727326f'} |
PyPI | GHSA-jggw-2q6g-c3m6 | Out-of-bounds Read in OpenCV | An out-of-bounds read was discovered in OpenCV before 4.1.1 (OpenCV-Python before 4.1.0.25). Specifically, variable coarsest_scale is assumed to be greater than or equal to finest_scale within the calc()/ocl_calc() functions in dis_flow.cpp. However, this is not true when dealing with small images, leading to an out-of-bounds read of the heap-allocated arrays Ux and Uy. | {'CVE-2019-19624'} | 2022-03-03T05:13:13.001034Z | 2021-10-12T22:22:58Z | MODERATE | null | {'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2019-19624', 'https://github.com/opencv/opencv-python/releases/tag/25', 'https://github.com/opencv/opencv/commit/d1615ba11a93062b1429fce9f0f638d1572d3418', 'https://github.com/opencv/opencv/issues/14554', 'https://access.redhat.com/security/cve/cve-2019-19624', 'https://github.com/opencv/opencv-python'} | null | {'https://github.com/opencv/opencv/commit/d1615ba11a93062b1429fce9f0f638d1572d3418'} | {'https://github.com/opencv/opencv/commit/d1615ba11a93062b1429fce9f0f638d1572d3418'} |
PyPI | GHSA-wp3c-xw9g-gpcg | Lack of validation in `SparseDenseCwiseMul` | ### Impact
Due to lack of validation in `tf.raw_ops.SparseDenseCwiseMul`, an attacker can trigger denial of service via `CHECK`-fails or accesses to outside the bounds of heap allocated data:
```python
import tensorflow as tf
indices = tf.constant([], shape=[10, 0], dtype=tf.int64)
values = tf.constant([], shape=[0], dtype=tf.int64)
shape = tf.constant([0, 0], shape=[2], dtype=tf.int64)
dense = tf.constant([], shape=[0], dtype=tf.int64)
tf.raw_ops.SparseDenseCwiseMul(
sp_indices=indices, sp_values=values, sp_shape=shape, dense=dense)
```
Since the [implementation](https://github.com/tensorflow/tensorflow/blob/38178a2f7a681a7835bb0912702a134bfe3b4d84/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L68-L80) only validates the rank of the input arguments but no [constraints between dimensions](https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseDenseCwiseMul), an attacker can abuse them to trigger internal `CHECK` assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers.
### Patches
We have patched the issue in GitHub commit [7ae2af34087fb4b5c8915279efd03da3b81028bc](https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. | {'CVE-2021-29567'} | 2022-03-03T05:13:36.442445Z | 2021-05-21T14:25:16Z | LOW | null | {'CWE-617'} | {'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp3c-xw9g-gpcg', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29567'} | null | {'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc'} | {'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc'} |
PyPI | PYSEC-2020-138 | null | In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. | {'GHSA-rrfp-j2mp-hq9c', 'CVE-2020-15265'} | 2021-09-01T08:19:35.574576Z | 2020-10-21T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/42105', 'https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrfp-j2mp-hq9c'} | null | {'https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808'} | {'https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808'} |
PyPI | PYSEC-2021-211 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors. The implementation(https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-828x-qc2p-wprq', 'CVE-2021-29574'} | 2021-08-27T03:22:34.535736Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-828x-qc2p-wprq'} | null | {'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4'} | {'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4'} |
PyPI | GHSA-c84h-w6cr-5v8q | Markdown-supplied Shell Command Execution | ### Impact
lookatme versions prior to 2.3.0 automatically loaded the built-in "terminal" and "file_loader" extensions. Users that use lookatme to render untrusted markdown may have malicious shell commands automatically run on their system.
### Patches
Users should upgrade to lookatme versions 2.3.0 or above.
### Workarounds
The `lookatme/contrib/terminal.py` and `lookatme/contrib/file_loader.py` files may be manually deleted. Additionally, it is always recommended to be aware of what is being rendered with lookatme.
### References
* https://github.com/d0c-s4vage/lookatme/pull/110
* https://github.com/d0c-s4vage/lookatme/releases/tag/v2.3.0
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [d0c-s4vage/lookatme](https://github.com/d0c-s4vage/lookatme) | {'CVE-2020-15271'} | 2022-03-22T20:47:04.232825Z | 2020-10-27T17:59:54Z | CRITICAL | null | {'CWE-78'} | {'https://github.com/d0c-s4vage/lookatme/commit/72fe36b784b234548d49dae60b840c37f0eb8d84', 'https://pypi.org/project/lookatme/#history', 'https://github.com/d0c-s4vage/lookatme', 'https://github.com/d0c-s4vage/lookatme/releases/tag/v2.3.0', 'https://github.com/d0c-s4vage/lookatme/pull/110', 'https://github.com/d0c-s4vage/lookatme/security/advisories/GHSA-c84h-w6cr-5v8q', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15271'} | null | {'https://github.com/d0c-s4vage/lookatme/commit/72fe36b784b234548d49dae60b840c37f0eb8d84'} | {'https://github.com/d0c-s4vage/lookatme/commit/72fe36b784b234548d49dae60b840c37f0eb8d84'} |
PyPI | PYSEC-2021-641 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixDiag*` operations(https://github.com/tensorflow/tensorflow/blob/4c4f420e68f1cfaf8f4b6e8e3eb857e9e4c3ff33/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L195-L197) does not validate that the tensor arguments are non-empty. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29515', 'GHSA-hc6c-75p4-hmq4'} | 2021-12-09T06:35:17.530281Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hc6c-75p4-hmq4'} | null | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6'} | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6'} |
PyPI | PYSEC-2020-211 | null | packet.py in pyrad before 2.1 uses weak random numbers to generate RADIUS authenticators and hash passwords, which makes it easier for remote attackers to obtain sensitive information via a brute force attack. | {'CVE-2013-0294'} | 2021-07-05T00:01:25.072912Z | 2020-01-28T16:15:00Z | null | null | null | {'http://lists.fedoraproject.org/pipermail/package-announce/2013-September/115677.html', 'https://bugzilla.redhat.com/show_bug.cgi?id=911682', 'http://lists.fedoraproject.org/pipermail/package-announce/2013-September/116567.html', 'https://github.com/wichert/pyrad/commit/38f74b36814ca5b1a27d9898141126af4953bee5', 'http://www.securityfocus.com/bid/57984', 'http://www.openwall.com/lists/oss-security/2013/02/15/13', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/82133', 'http://lists.fedoraproject.org/pipermail/package-announce/2013-September/115705.html'} | null | {'https://github.com/wichert/pyrad/commit/38f74b36814ca5b1a27d9898141126af4953bee5'} | {'https://github.com/wichert/pyrad/commit/38f74b36814ca5b1a27d9898141126af4953bee5'} |
PyPI | PYSEC-2017-96 | null | The tlslite library before 0.4.9 for Python allows remote attackers to trigger a denial of service (runtime exception and process crash). | {'CVE-2015-3220'} | 2021-08-27T03:22:47.903181Z | 2017-06-13T16:29:00Z | null | null | null | {'https://groups.google.com/forum/#!topic/tlslite-dev/MoWE7B0A4iU', 'https://github.com/trevp/tlslite/commit/aca8d4f898b436ff6754e1a9ab96cae976c8a853', 'https://bugzilla.redhat.com/show_bug.cgi?id=1254215'} | null | {'https://github.com/trevp/tlslite/commit/aca8d4f898b436ff6754e1a9ab96cae976c8a853'} | {'https://github.com/trevp/tlslite/commit/aca8d4f898b436ff6754e1a9ab96cae976c8a853'} |
PyPI | GHSA-5hx2-qx8j-qjqm | Overflow/crash in `tf.image.resize` when size is large | ### Impact
If `tf.image.resize` is called with a large input argument then the TensorFlow process will crash due to a `CHECK`-failure caused by an overflow.
```python
import tensorflow as tf
import numpy as np
tf.keras.layers.UpSampling2D(
size=1610637938,
data_format='channels_first',
interpolation='bilinear')(np.ones((5,1,1,1)))
```
The number of elements in the output tensor is too much for the `int64_t` type and the overflow is detected via a `CHECK` statement. This aborts the process.
### Patches
We have patched the issue in GitHub commit [e5272d4204ff5b46136a1ef1204fc00597e21837](https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837) (merging [#51497](https://github.com/tensorflow/tensorflow/pull/51497)).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/46914). | {'CVE-2021-41199'} | 2022-03-03T05:14:00.716577Z | 2021-11-10T19:33:24Z | MODERATE | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837', 'https://github.com/tensorflow/tensorflow/issues/46914', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41199', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hx2-qx8j-qjqm'} | null | {'https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837'} | {'https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837'} |
PyPI | GHSA-m6gj-h9gm-gw44 | Incorrect Default Permissions | An issue was discovered in Django 2.2 before 2.2.16, 3.0 before 3.0.10, and 3.1 before 3.1.1 (when Python 3.7+ is used). FILE_UPLOAD_DIRECTORY_PERMISSIONS mode was not applied to intermediate-level directories created in the process of uploading files. It was also not applied to intermediate-level collected static directories when using the collectstatic management command. | {'CVE-2020-24583'} | 2022-03-03T05:14:06.757335Z | 2021-03-18T20:30:13Z | HIGH | null | {'CWE-276'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-24583', 'https://github.com/django/django/commit/8d7271578d7b153435b40fe40236ebec43cbf1b9', 'https://usn.ubuntu.com/4479-1/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/OLGFFLMF3X6USMJD7V5F5P4K2WVUTO3T/', 'https://www.openwall.com/lists/oss-security/2020/09/01/2', 'https://www.djangoproject.com/weblog/2020/sep/01/security-releases/', 'https://docs.djangoproject.com/en/dev/releases/security/', 'https://groups.google.com/forum/#!topic/django-announce/Gdqn58RqIDM', 'https://www.oracle.com/security-alerts/cpujan2021.html', 'https://security.netapp.com/advisory/ntap-20200918-0004/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/F2ZHO3GZCJMP3DDTXCNVFV6ED3W64NAU/', 'https://groups.google.com/forum/#!topic/django-announce/zFCMdgUnutU', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/ZCRPQCBTV3RZHKVZ6K6QOAANPRZQD3GI/'} | null | {'https://github.com/django/django/commit/8d7271578d7b153435b40fe40236ebec43cbf1b9'} | {'https://github.com/django/django/commit/8d7271578d7b153435b40fe40236ebec43cbf1b9'} |
PyPI | PYSEC-2021-704 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29578', 'GHSA-6f89-8j54-29xf'} | 2021-12-09T06:35:28.203971Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6f89-8j54-29xf'} | null | {'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f'} | {'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f'} |
PyPI | PYSEC-2021-591 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/python/keras/saving/model_config.py#L66-L104) uses `yaml.unsafe_load` which can perform arbitrary code execution on the input. Given that YAML format support requires a significant amount of work, we have removed it for now. We have patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37678', 'GHSA-r6jx-9g48-2r5r'} | 2021-12-09T06:35:05.654112Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/23d6383eb6c14084a8fc3bdf164043b974818012', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r6jx-9g48-2r5r'} | null | {'https://github.com/tensorflow/tensorflow/commit/23d6383eb6c14084a8fc3bdf164043b974818012'} | {'https://github.com/tensorflow/tensorflow/commit/23d6383eb6c14084a8fc3bdf164043b974818012'} |
PyPI | PYSEC-2020-35 | null | Django 1.11 before 1.11.28, 2.2 before 2.2.10, and 3.0 before 3.0.3 allows SQL Injection if untrusted data is used as a StringAgg delimiter (e.g., in Django applications that offer downloads of data as a series of rows with a user-specified column delimiter). By passing a suitably crafted delimiter to a contrib.postgres.aggregates.StringAgg instance, it was possible to break escaping and inject malicious SQL. | {'GHSA-hmr4-m2h5-33qx', 'CVE-2020-7471'} | 2020-06-19T03:15:00Z | 2020-02-03T12:15:00Z | null | null | null | {'https://github.com/django/django/commit/eb31d845323618d688ad429479c6dda973056136', 'https://docs.djangoproject.com/en/3.0/releases/security/', 'https://security.netapp.com/advisory/ntap-20200221-0006/', 'https://www.djangoproject.com/weblog/2020/feb/03/security-releases/', 'https://www.openwall.com/lists/oss-security/2020/02/03/1', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/4A2AP4T7RKPBCLTI2NNQG3T6MINDUUMZ/', 'https://groups.google.com/forum/#!topic/django-announce/X45S86X5bZI', 'https://security.gentoo.org/glsa/202004-17', 'http://www.openwall.com/lists/oss-security/2020/02/03/1', 'https://usn.ubuntu.com/4264-1/', 'https://seclists.org/bugtraq/2020/Feb/30', 'https://www.debian.org/security/2020/dsa-4629', 'https://github.com/advisories/GHSA-hmr4-m2h5-33qx'} | null | {'https://github.com/django/django/commit/eb31d845323618d688ad429479c6dda973056136'} | {'https://github.com/django/django/commit/eb31d845323618d688ad429479c6dda973056136'} |
PyPI | GHSA-vfj6-275q-4pvm | graphite.composer.views.send_email vulnerable to SSRF | ### Impact
send_email in graphite-web/webapp/graphite/composer/views.py in Graphite through 1.1.5 is vulnerable to SSRF. The vulnerable SSRF endpoint can be used by an attacker to have the Graphite web server request any resource. The response to this SSRF request is encoded into an image file and then sent to an e-mail address that can be supplied by the attacker. Thus, an attacker can exfiltrate any information. Email will be send through SMTP server configured in Graphite, by default it's 'localhost'
### Patches
Problem was patched in Graphite-web 1.1.6. Also patches was released for graphite-web [1.0.x](https://github.com/graphite-project/graphite-web/pull/2501) and [0.9.x](https://github.com/graphite-project/graphite-web/pull/2500), and we'll discuss releases of non-supported branches later.
### Workarounds
You can manually remove function `send_email` from file `webapp/graphite/composer/views.py`. This function are not in use and will not affect your Graphite installation.
### References
For more information check this graphite-web Github issue #2008
### For more information
If you have any questions or comments about this advisory:
* Add comment in [issue #2008](https://github.com/graphite-project/graphite-web/issues/2008) | {'CVE-2017-18638'} | 2022-03-03T05:12:40.339114Z | 2019-10-25T13:55:20Z | HIGH | null | {'CWE-918'} | {'https://github.com/graphite-project/graphite-web/pull/2499', 'https://www.youtube.com/watch?v=ds4Gp4xoaeA', 'https://github.com/graphite-project/graphite-web/security/advisories/GHSA-vfj6-275q-4pvm', 'https://lists.debian.org/debian-lts-announce/2019/10/msg00030.html', 'https://nvd.nist.gov/vuln/detail/CVE-2017-18638', 'https://blog.orange.tw/2017/07/how-i-chained-4-vulnerabilities-on.html#second-bug-internal-graphite-ssrf', 'https://github.com/graphite-project/graphite-web/commit/71726a0e41a5263f49b973a7b856505a5b931c1f', 'https://github.com/graphite-project/graphite-web/issues/2008', 'https://github.com/graphite-project/graphite-web'} | null | {'https://github.com/graphite-project/graphite-web/commit/71726a0e41a5263f49b973a7b856505a5b931c1f'} | {'https://github.com/graphite-project/graphite-web/commit/71726a0e41a5263f49b973a7b856505a5b931c1f'} |
PyPI | PYSEC-2021-657 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when calling `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the first line of `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29531', 'GHSA-3qxp-qjq7-w4hf'} | 2021-12-09T06:35:20.083523Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3qxp-qjq7-w4hf'} | null | {'https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1'} | {'https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1'} |
PyPI | PYSEC-2021-207 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ef0c008ee84bad91ec6725ddc42091e19a30cf0e/tensorflow/core/kernels/maxpooling_op.cc#L1016-L1017) uses the same value to index in two different arrays but there is no guarantee that the sizes are identical. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29570', 'GHSA-545v-42p7-98fq'} | 2021-08-27T03:22:33.847369Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-545v-42p7-98fq', 'https://github.com/tensorflow/tensorflow/commit/dcd7867de0fea4b72a2b34bd41eb74548dc23886'} | null | {'https://github.com/tensorflow/tensorflow/commit/dcd7867de0fea4b72a2b34bd41eb74548dc23886'} | {'https://github.com/tensorflow/tensorflow/commit/dcd7867de0fea4b72a2b34bd41eb74548dc23886'} |
PyPI | PYSEC-2021-712 | null | TensorFlow is an end-to-end open source platform for machine learning. Optimized pooling implementations in TFLite fail to check that the stride arguments are not 0 before calling `ComputePaddingHeightWidth`(https://github.com/tensorflow/tensorflow/blob/3f24ccd932546416ec906a02ddd183b48a1d2c83/tensorflow/lite/kernels/pooling.cc#L90). Since users can craft special models which will have `params->stride_{height,width}` be zero, this will result in a division by zero. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29586', 'GHSA-26j7-6w8w-7922'} | 2021-12-09T06:35:29.712146Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-26j7-6w8w-7922', 'https://github.com/tensorflow/tensorflow/commit/5f7975d09eac0f10ed8a17dbb6f5964977725adc'} | null | {'https://github.com/tensorflow/tensorflow/commit/5f7975d09eac0f10ed8a17dbb6f5964977725adc'} | {'https://github.com/tensorflow/tensorflow/commit/5f7975d09eac0f10ed8a17dbb6f5964977725adc'} |
PyPI | GHSA-jhq9-wm9m-cf89 | CHECK-failure in `UnsortedSegmentJoin` | ### Impact
An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`:
```python
import tensorflow as tf
inputs = tf.constant([], dtype=tf.string)
segment_ids = tf.constant([], dtype=tf.int32)
num_segments = tf.constant([], dtype=tf.int32)
separator = ''
tf.raw_ops.UnsortedSegmentJoin(
inputs=inputs, segment_ids=segment_ids,
num_segments=num_segments, separator=separator)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar:
```cc
const Tensor& num_segments_tensor = context->input(2);
auto num_segments = num_segments_tensor.scalar<NUM_SEGMENTS_TYPE>()();
```
Since the tensor is empty the `CHECK` involved in `.scalar<T>()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination.
### Patches
We have patched the issue in GitHub commit [704866eabe03a9aeda044ec91a8d0c83fc1ebdbe](https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29552'} | 2022-03-03T05:14:20.674010Z | 2021-05-21T14:23:48Z | LOW | null | {'CWE-617'} | {'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29552', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jhq9-wm9m-cf89'} | null | {'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe'} | {'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe'} |
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