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stringdate 2019-03-26 14:13:00
2022-05-10 08:46:52
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | GHSA-3h8m-483j-7xxm | Heap out of bounds read in `RequantizationRange` | ### Impact
The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:
```python
import tensorflow as tf
input = tf.constant([1], shape=[1], dtype=tf.qint32)
input_max = tf.constant([], dtype=tf.float32)
input_min = tf.constant([], dtype=tf.float32)
tf.raw_ops.RequantizationRange(input=input, input_min=input_min, input_max=input_max)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays:
```cc
const float input_min_float = ctx->input(1).flat<float>()(0);
const float input_max_float = ctx->input(2).flat<float>()(0);
```
If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds.
### Patches
We have patched the issue in GitHub commit [ef0c008ee84bad91ec6725ddc42091e19a30cf0e](https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e).
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-2021-29569'} | 2022-03-03T05:12:04.409348Z | 2021-05-21T14:25:22Z | LOW | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29569', 'https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e'} | null | {'https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e'} | {'https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e'} |
PyPI | PYSEC-2021-372 | null | OMERO.web provides a web based client and plugin infrastructure. In versions prior to 5.11.0, a variety of templates do not perform proper sanitization through HTML escaping. Due to the lack of sanitization and use of ``jQuery.html()``, there are a whole host of cross-site scripting possibilities with specially crafted input to a variety of fields. This issue is patched in version 5.11.0. There are no known workarounds aside from upgrading. | {'CVE-2021-41132', 'GHSA-g67g-hvc3-xmvf'} | 2021-10-17T22:58:18.538383Z | 2021-10-14T16:15:00Z | null | null | null | {'https://github.com/ome/omero-web/security/advisories/GHSA-g67g-hvc3-xmvf', 'https://github.com/ome/omero-web/commit/0168067accde5e635341b3c714b1d53ae92ba424', 'https://www.openmicroscopy.org/security/advisories/2021-SV3/'} | null | {'https://github.com/ome/omero-web/commit/0168067accde5e635341b3c714b1d53ae92ba424'} | {'https://github.com/ome/omero-web/commit/0168067accde5e635341b3c714b1d53ae92ba424'} |
PyPI | GHSA-mcg6-h362-cmq5 | Improper Authorization in cobbler | ### Impact
If PAM is correctly configured and a user account is set to expired, the expired user-account is still able to successfully log into Cobbler in all places (Web UI, CLI & XMLRPC-API).
The same applies to user accounts with passwords set to be expired.
### Patches
There is a patch for the latest Cobbler `3.3.2` available, however a backport will be done for `3.2.x`.
### Workarounds
- Delete expired accounts which are able to access Cobbler via PAM.
- Use `chage -l <username>` to lock the account. If the account has SSH-Keys attached then remove them completely.
### References
- Originally discovered by @ysf at https://www.huntr.dev/bounties/c458b868-63df-414e-af10-47e3745caa1d/
### How to test if my Cobbler instance is affected?
The following `pytest` test assumes that your PAM setup is correct. In case the added user is not able to login, this test does not make sense to be executed.
```python
def test_pam_login_with_expired_user():
# Arrange
# create pam testuser
test_username = "expired_user"
test_password = "password"
test_api = CobblerAPI()
subprocess_1 = subprocess.run(
["perl", "-e", "'print crypt(\"%s\", \"%s\")'" % (test_username, test_password)],
stdout=subprocess.PIPE
)
subprocess.run(["useradd", "-p", subprocess_1.stdout, test_username])
# change user to be expired
subprocess.run(["chage", "-E0", test_username])
# Act
result = pam.authenticate(test_api, test_username, test_password)
# Assert - login should fail
assert not result
```
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [the Cobbler repository](https://github.com/cobbler/cobbler/issues/new/choose)
* Ask in the [Gitter/Matrix Chat](https://gitter.im/cobbler/community)
* Email us at [cobbler.project@gmail.com](mailto:cobbler.project@gmail.com)
| {'CVE-2022-0860'} | 2022-04-01T20:17:01.937311Z | 2022-03-11T20:52:04Z | HIGH | null | {'CWE-863', 'CWE-285'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-0860', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DYWYHWVVRUSPCV5SWBOSAMQJQLTSBTKY/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/D4KCNZYBQC2FM5SEEDRQZO4LRZ4ZECMG/', 'https://github.com/cobbler/cobbler/commit/9044aa990a94752fa5bd5a24051adde099280bfa', 'https://github.com/cobbler/cobbler/security/advisories/GHSA-mcg6-h362-cmq5', 'https://huntr.dev/bounties/c458b868-63df-414e-af10-47e3745caa1d', 'https://github.com/cobbler/cobbler', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/IYSHMF6MEIITFAG7EJ3IQKVUN7MDV2XM/'} | null | {'https://github.com/cobbler/cobbler/commit/9044aa990a94752fa5bd5a24051adde099280bfa'} | {'https://github.com/cobbler/cobbler/commit/9044aa990a94752fa5bd5a24051adde099280bfa'} |
PyPI | PYSEC-2021-688 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.IRFFT`. 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-2021-29562', 'GHSA-36vm-xw34-x4pj'} | 2021-12-09T06:35:25.478301Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-36vm-xw34-x4pj', 'https://github.com/tensorflow/tensorflow/commit/1c56f53be0b722ca657cbc7df461ed676c8642a2'} | null | {'https://github.com/tensorflow/tensorflow/commit/1c56f53be0b722ca657cbc7df461ed676c8642a2'} | {'https://github.com/tensorflow/tensorflow/commit/1c56f53be0b722ca657cbc7df461ed676c8642a2'} |
PyPI | PYSEC-2021-722 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `EmbeddingLookup` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e4b29809543b250bc9b19678ec4776299dd569ba/tensorflow/lite/kernels/embedding_lookup.cc#L73-L74). An attacker can craft a model such that the first dimension of the `value` input is 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-4vrf-ff7v-hpgr', 'CVE-2021-29596'} | 2021-12-09T06:35:31.406437Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4vrf-ff7v-hpgr'} | null | {'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e'} | {'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e'} |
PyPI | GHSA-hwr7-8gxx-fj5p | Null pointer dereference in `RaggedTensorToTensor` | ### Impact
Sending invalid argument for `row_partition_types` of `tf.raw_ops.RaggedTensorToTensor` API results in a null pointer dereference and undefined behavior:
```python
import tensorflow as tf
tf.raw_ops.RaggedTensorToTensor(
shape=1,
values=10,
default_value=21,
row_partition_tensors=tf.constant([0,0,0,0]),
row_partition_types=[])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L328) accesses the first element of a user supplied list of values without validating that the provided list is not empty.
### Patches
We have patched the issue in GitHub commit [301ae88b331d37a2a16159b65b255f4f9eb39314](https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314).
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-2021-37638'} | 2022-03-03T05:14:10.600697Z | 2021-08-25T14:44:09Z | HIGH | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37638', 'https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hwr7-8gxx-fj5p'} | null | {'https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314'} | {'https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314'} |
PyPI | GHSA-q8gv-q7wr-9jf8 | Segfault in Tensorflow | ### Impact
In eager mode, TensorFlow does not set the session state. Hence, calling `tf.raw_ops.GetSessionHandle` or `tf.raw_ops.GetSessionHandleV2` results in a null pointer dereference:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/session_ops.cc#L45
In the above snippet, in eager mode, `ctx->session_state()` returns `nullptr`. Since code immediately dereferences this, we get a segmentation fault.
### Patches
We have patched the issue in 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1 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-2020-15204'} | 2022-03-03T05:13:50.625303Z | 2020-09-25T18:28:41Z | MODERATE | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15204', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8gv-q7wr-9jf8', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1'} | {'https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1'} |
PyPI | PYSEC-2021-237 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `OneHot` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/f61c57bd425878be108ec787f4d96390579fb83e/tensorflow/lite/kernels/one_hot.cc#L68-L72). An attacker can craft a model such that at least one of the dimensions of `indices` would be 0. In turn, the `prefix_dim_size` value would become 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-j8qh-3xrq-c825', 'CVE-2021-29600'} | 2021-08-27T03:22:39.194303Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3ebedd7e345453d68e279cfc3e4072648e5e12e5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qh-3xrq-c825'} | null | {'https://github.com/tensorflow/tensorflow/commit/3ebedd7e345453d68e279cfc3e4072648e5e12e5'} | {'https://github.com/tensorflow/tensorflow/commit/3ebedd7e345453d68e279cfc3e4072648e5e12e5'} |
PyPI | GHSA-jwqp-28gf-p498 | HTTP authentication credentials potentially leaked to target websites | ### Impact
If you use [`HttpAuthMiddleware`](http://doc.scrapy.org/en/latest/topics/downloader-middleware.html#module-scrapy.downloadermiddlewares.httpauth) (i.e. the `http_user` and `http_pass` spider attributes) for HTTP authentication, all requests will expose your credentials to the request target.
This includes requests generated by Scrapy components, such as `robots.txt` requests sent by Scrapy when the `ROBOTSTXT_OBEY` setting is set to `True`, or as requests reached through redirects.
### Patches
Upgrade to Scrapy 2.5.1 and use the new `http_auth_domain` spider attribute to control which domains are allowed to receive the configured HTTP authentication credentials.
If you are using Scrapy 1.8 or a lower version, and upgrading to Scrapy 2.5.1 is not an option, you may upgrade to Scrapy 1.8.1 instead.
### Workarounds
If you cannot upgrade, set your HTTP authentication credentials on a per-request basis, using for example the [`w3lib.http.basic_auth_header`](https://w3lib.readthedocs.io/en/latest/w3lib.html#w3lib.http.basic_auth_header) function to convert your credentials into a value that you can assign to the `Authorization` header of your request, instead of defining your credentials globally using [`HttpAuthMiddleware`](http://doc.scrapy.org/en/latest/topics/downloader-middleware.html#module-scrapy.downloadermiddlewares.httpauth).
### For more information
If you have any questions or comments about this advisory:
* [Open an issue](https://github.com/scrapy/scrapy/issues)
* [Email us](mailto:opensource@zyte.com)
| {'CVE-2021-41125'} | 2022-04-25T23:46:57.785253Z | 2021-10-06T17:46:22Z | MODERATE | null | {'CWE-522', 'CWE-200'} | {'http://doc.scrapy.org/en/latest/topics/downloader-middleware.html#module-scrapy.downloadermiddlewares.httpauth', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41125', 'https://github.com/scrapy/scrapy/security/advisories/GHSA-jwqp-28gf-p498', 'https://github.com/scrapy/scrapy', 'https://lists.debian.org/debian-lts-announce/2022/03/msg00021.html', 'https://w3lib.readthedocs.io/en/latest/w3lib.html#w3lib.http.basic_auth_header', 'https://github.com/scrapy/scrapy/commit/b01d69a1bf48060daec8f751368622352d8b85a6'} | null | {'https://github.com/scrapy/scrapy/commit/b01d69a1bf48060daec8f751368622352d8b85a6'} | {'https://github.com/scrapy/scrapy/commit/b01d69a1bf48060daec8f751368622352d8b85a6'} |
PyPI | PYSEC-2021-667 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a dereference of a null pointer in `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L67-L74) does not fully validate the `data_splits` argument. This would result in `ngrams_data`(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L106-L110) to be a null pointer when the output would be computed to have 0 or negative size. Later writes to the output tensor would then cause a null pointer dereference. 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-2021-29541', 'GHSA-xqfj-35wv-m3cr'} | 2021-12-09T06:35:21.834705Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xqfj-35wv-m3cr', 'https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b'} | null | {'https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b'} | {'https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b'} |
PyPI | GHSA-qhxx-j73r-qpm2 | Uninitialized memory access in TensorFlow | ### Impact
Under certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to [default initialize the quantized floating point types in Eigen](https://github.com/tensorflow/tensorflow/blob/f70160322a579144950dff1537dcbe3c7c09d6f5/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h#L61-L104):
```cc
struct QUInt8 {
QUInt8() {}
// ...
uint8_t value;
};
struct QInt16 {
QInt16() {}
// ...
int16_t value;
};
struct QUInt16 {
QUInt16() {}
// ...
uint16_t value;
};
struct QInt32 {
QInt32() {}
// ...
int32_t value;
};
```
### Patches
We have patched the issue in GitHub commit [ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2](https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.
### 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-2020-26266'} | 2022-03-03T05:13:51.980464Z | 2020-12-10T19:07:24Z | LOW | null | {'CWE-908'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhxx-j73r-qpm2', 'https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26266'} | null | {'https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2'} | {'https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2'} |
PyPI | GHSA-2hwx-mjrm-v3g8 | Denial of service attack via .well-known lookups | ### Impact
A malicious homeserver could redirect requests to their .well-known file to a large file. This can lead to a denial of service attack where homeservers will consume significantly more resources when requesting the .well-known file of a malicious homeserver.
This affects any server which accepts federation requests from untrusted servers.
### Patches
Issue is resolved by #8950. A bug not affecting the security aspects of this was fixed in #9108.
### Workarounds
The `federation_domain_whitelist` setting can be used to restrict the homeservers communicated with over federation. | {'CVE-2021-21274'} | 2022-03-03T05:13:28.871198Z | 2021-03-01T19:34:54Z | MODERATE | null | {'CWE-400'} | {'https://github.com/matrix-org/synapse/pull/8950', 'https://github.com/matrix-org/synapse', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21274', 'https://github.com/matrix-org/synapse/releases/tag/v1.25.0', 'https://github.com/matrix-org/synapse/commit/ff5c4da1289cb5e097902b3e55b771be342c29d6', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-2hwx-mjrm-v3g8', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TNNAJOZNMVMXM6AS7RFFKB4QLUJ4IFEY/'} | null | {'https://github.com/matrix-org/synapse/commit/ff5c4da1289cb5e097902b3e55b771be342c29d6'} | {'https://github.com/matrix-org/synapse/commit/ff5c4da1289cb5e097902b3e55b771be342c29d6'} |
PyPI | PYSEC-2022-117 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseCountSparseOutput` can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation. 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-x4qx-4fjv-hmw6', 'CVE-2022-21738'} | 2022-03-09T00:18:24.751245Z | 2022-02-03T14:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/6f4d3e8139ec724dbbcb40505891c81dd1052c4a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4qx-4fjv-hmw6', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/count_ops.cc#L168-L273'} | null | {'https://github.com/tensorflow/tensorflow/commit/6f4d3e8139ec724dbbcb40505891c81dd1052c4a'} | {'https://github.com/tensorflow/tensorflow/commit/6f4d3e8139ec724dbbcb40505891c81dd1052c4a'} |
PyPI | GHSA-7r94-xv9v-63jw | A use of uninitialized value vulnerability in Tensorflow | ### Impact
TensorFlow's Grappler optimizer has a [use of unitialized variable](https://github.com/tensorflow/tensorflow/blob/3457a2b122e50b4d44ceaaed5a663d635e5c22df/tensorflow/core/grappler/optimizers/auto_parallel.cc#L155-L164):
```cc
const NodeDef* dequeue_node;
for (const auto& train_node : train_nodes) {
if (IsDequeueOp(*train_node)) {
dequeue_node = train_node;
break;
}
}
if (dequeue_node) {
...
}
```
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.
### Patches
We have patched the issue in GitHub commit [68867bf01239d9e1048f98cbad185bf4761bedd3](https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3).
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 by Qian Feng from Baidu Security Team.
| {'CVE-2021-41225'} | 2022-03-03T05:12:47.207669Z | 2021-11-10T18:44:11Z | MODERATE | null | {'CWE-908'} | {'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41225', '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-vq2r-5xvm-3hc3 | Segfault in `CTCBeamSearchDecoder` | ### Impact
Due to lack of validation in `tf.raw_ops.CTCBeamSearchDecoder`, an attacker can trigger denial of service via segmentation faults:
```python
import tensorflow as tf
inputs = tf.constant([], shape=[18, 8, 0], dtype=tf.float32)
sequence_length = tf.constant([11, -43, -92, 11, -89, -83, -35, -100],
shape=[8], dtype=tf.int32)
beam_width = 10
top_paths = 3
merge_repeated = True
tf.raw_ops.CTCBeamSearchDecoder(
inputs=inputs, sequence_length=sequence_length, beam_width=beam_width,
top_paths=top_paths, merge_repeated=merge_repeated)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7/tensorflow/core/kernels/ctc_decoder_ops.cc#L68-L79) fails to detect cases when the input tensor is empty and proceeds to read data from a null buffer.
### Patches
We have patched the issue in GitHub commit [b1b323042264740c398140da32e93fb9c2c9f33e](https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e).
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-2021-29581'} | 2022-03-03T05:13:03.293126Z | 2021-05-21T14:26:30Z | LOW | null | {'CWE-908'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29581', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vq2r-5xvm-3hc3', 'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} | null | {'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} | {'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} |
PyPI | PYSEC-2021-309 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`GatherNd` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in `indices`. Similar issue exists in [`Gather` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc). We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. 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-jwf9-w5xm-f437', 'CVE-2021-37687'} | 2021-08-27T03:22:47.431884Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jwf9-w5xm-f437', 'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d'} | null | {'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d'} | {'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d'} |
PyPI | GHSA-qm57-vhq3-3fwf | Header injection possible in Django | In Django 2.2 before 2.2.22, 3.1 before 3.1.10, and 3.2 before 3.2.2 (with Python 3.9.5+), URLValidator does not prohibit newlines and tabs (unless the URLField form field is used). If an application uses values with newlines in an HTTP response, header injection can occur. Django itself is unaffected because HttpResponse prohibits newlines in HTTP headers. | {'CVE-2021-32052'} | 2022-03-03T05:12:23.541212Z | 2021-06-09T17:14:51Z | MODERATE | null | {'CWE-88', 'CWE-79'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-32052', 'https://github.com/django/django/commit/e1e81aa1c4427411e3c68facdd761229ffea6f6f', 'https://docs.djangoproject.com/en/3.2/releases/security/', 'https://groups.google.com/forum/#!forum/django-announce', 'https://bugzilla.redhat.com/show_bug.cgi?id=1944801', 'https://www.djangoproject.com/weblog/2021/may/06/security-releases/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/ZVKYPHR3TKR2ESWXBPOJEKRO2OSJRZUE/', 'https://security.netapp.com/advisory/ntap-20210611-0002/', 'http://www.openwall.com/lists/oss-security/2021/05/06/1'} | null | {'https://github.com/django/django/commit/e1e81aa1c4427411e3c68facdd761229ffea6f6f'} | {'https://github.com/django/django/commit/e1e81aa1c4427411e3c68facdd761229ffea6f6f'} |
PyPI | GHSA-rhm9-p9w5-fwm7 | Symmetrically encrypting large values can lead to integer overflow | cryptography is a package designed to expose cryptographic primitives and recipes to Python developers. When certain sequences of `update()` calls with large values (multiple GBs) for symetric encryption or decryption occur, it's possible for an integer overflow to happen, leading to mishandling of buffers. This is patched in version 3.3.2 and newer.
| {'CVE-2020-36242'} | 2022-04-22T18:45:09.196238Z | 2021-02-10T01:32:27Z | CRITICAL | null | {'CWE-190', 'CWE-787'} | {'https://github.com/pyca/cryptography/security/advisories/GHSA-rhm9-p9w5-fwm7', 'https://github.com/pyca/cryptography/compare/3.3.1...3.3.2', 'https://github.com/pyca/cryptography/issues/5615', 'https://www.oracle.com/security-alerts/cpuapr2022.html', 'https://github.com/pyca/cryptography', 'https://github.com/pyca/cryptography/blob/master/CHANGELOG.rst', 'https://nvd.nist.gov/vuln/detail/CVE-2020-36242', 'https://github.com/pyca/cryptography/commit/82b6ce28389f0a317bc55ba2091a74b346db7cae', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/L7RGQLK4J5ZQFRLKCHVVG6BKZTUQMG7E/'} | null | {'https://github.com/pyca/cryptography/commit/82b6ce28389f0a317bc55ba2091a74b346db7cae'} | {'https://github.com/pyca/cryptography/commit/82b6ce28389f0a317bc55ba2091a74b346db7cae'} |
PyPI | GHSA-93xj-8mrv-444m | Regular Expression Denial of Service (REDoS) in httplib2 | ### Impact
A malicious server which responds with long series of `\xa0` characters in the `www-authenticate` header may cause Denial of Service (CPU burn while parsing header) of the httplib2 client accessing said server.
### Patches
Version 0.19.0 contains new implementation of auth headers parsing, using pyparsing library.
https://github.com/httplib2/httplib2/pull/182
### Workarounds
```py
import httplib2
httplib2.USE_WWW_AUTH_STRICT_PARSING = True
```
### Technical Details
The vulnerable regular expression is https://github.com/httplib2/httplib2/blob/595e248d0958c00e83cb28f136a2a54772772b50/python3/httplib2/__init__.py#L336-L338
The section before the equals sign contains multiple overlapping groups. Ignoring the optional part containing a comma, we have:
\s*[^ \t\r\n=]+\s*=
Since all three infinitely repeating groups accept the non-breaking space character `\xa0`, a long string of `\xa0` causes catastrophic backtracking.
The complexity is cubic, so doubling the length of the malicious string of `\xa0` makes processing take 8 times as long.
### Reproduction Steps
Run a malicious server which responds with
www-authenticate: x \xa0\xa0\xa0\xa0x
but with many more `\xa0` characters.
An example malicious python server is below:
```py
from http.server import BaseHTTPRequestHandler, HTTPServer
def make_header_value(n_spaces):
repeat = "\xa0" * n_spaces
return f"x {repeat}x"
class Handler(BaseHTTPRequestHandler):
def do_GET(self):
self.log_request(401)
self.send_response_only(401) # Don't bother sending Server and Date
n_spaces = (
int(self.path[1:]) # Can GET e.g. /100 to test shorter sequences
if len(self.path) > 1 else
65512 # Max header line length 65536
)
value = make_header_value(n_spaces)
self.send_header("www-authenticate", value) # This header can actually be sent multiple times
self.end_headers()
if __name__ == "__main__":
HTTPServer(("", 1337), Handler).serve_forever()
```
Connect to the server with httplib2:
```py
import httplib2
httplib2.Http(".cache").request("http://localhost:1337", "GET")
```
To benchmark performance with shorter strings, you can set the path to a number e.g. http://localhost:1337/1000
### References
Thanks to [Ben Caller](https://github.com/b-c-ds) ([Doyensec](https://doyensec.com)) for finding vulnerability and discrete notification.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [httplib2](https://github.com/httplib2/httplib2/issues/new)
* Email [current maintainer at 2021-01](mailto:temotor@gmail.com) | {'CVE-2021-21240'} | 2022-03-03T05:13:25.212878Z | 2021-02-08T19:41:59Z | LOW | null | {'CWE-400'} | {'https://pypi.org/project/httplib2', 'https://github.com/httplib2/httplib2/pull/182', 'https://github.com/httplib2/httplib2/security/advisories/GHSA-93xj-8mrv-444m', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21240', 'https://github.com/httplib2/httplib2/commit/bd9ee252c8f099608019709e22c0d705e98d26bc'} | null | {'https://github.com/httplib2/httplib2/commit/bd9ee252c8f099608019709e22c0d705e98d26bc'} | {'https://github.com/httplib2/httplib2/commit/bd9ee252c8f099608019709e22c0d705e98d26bc'} |
PyPI | PYSEC-2021-759 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the code for `tf.raw_ops.SaveV2` does not properly validate the inputs and an attacker can trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/save_restore_v2_ops.cc) uses `ValidateInputs` to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above. However, the validation uses `OP_REQUIRES` which translates to setting the `Status` object of the current `OpKernelContext` to an error status, followed by an empty `return` statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the next line in `Compute` that follows the call to `ValidateInputs`. This is equivalent to lacking the validation. We have patched the issue in GitHub commit 9728c60e136912a12d99ca56e106b7cce7af5986. 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-2021-37648', 'GHSA-wp77-4gmm-7cq8'} | 2021-12-09T06:35:36.478576Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp77-4gmm-7cq8', 'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} | null | {'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} | {'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} |
PyPI | GHSA-6r3c-8xf3-ggrr | Directory traversal outside of SENDFILE_ROOT in django-sendfile2 | django-sendfile2 currently relies on the backend to correctly limit file paths to `SENDFILE_ROOT`. This is not the case for the `simple` and `development` backends, it is also not necessarily the case for any of the other backends either (it's just an assumption that was made by the original author).
This will be fixed in 0.6.0 which is to be released the same day as this advisory is made public.
When upgrading, you will need to make sure `SENDFILE_ROOT` is set in your settings module if it wasn't already. | null | 2022-03-03T05:14:17.489854Z | 2020-06-24T17:15:26Z | MODERATE | null | {'CWE-22'} | {'https://github.com/moggers87/django-sendfile2/security/advisories/GHSA-6r3c-8xf3-ggrr', 'https://github.com/moggers87/django-sendfile2', 'https://github.com/moggers87/django-sendfile2/commit/f870c52398a55b9b5189932dd8caa24efb4bc1e1'} | null | {'https://github.com/moggers87/django-sendfile2/commit/f870c52398a55b9b5189932dd8caa24efb4bc1e1'} | {'https://github.com/moggers87/django-sendfile2/commit/f870c52398a55b9b5189932dd8caa24efb4bc1e1'} |
PyPI | PYSEC-2020-309 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1." | {'CVE-2020-15194', 'GHSA-9mqp-7v2h-2382'} | 2021-12-09T06:35:12.701793Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9mqp-7v2h-2382', 'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54'} | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54'} |
PyPI | GHSA-7cqx-92hp-x6wh | Heap buffer overflow in `MaxPool3DGradGrad` | ### Impact
The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow:
```python
import tensorflow as tf
values = [0.01] * 11
orig_input = tf.constant(values, shape=[11, 1, 1, 1, 1], dtype=tf.float32)
orig_output = tf.constant([0.01], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
grad = tf.constant([0.01], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.MaxPool3DGradGrad(
orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,
strides=strides, padding=padding)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully:
```cc
Pool3dParameters params{context, ksize_, stride_,
padding_, data_format_, tensor_in.shape()};
```
Since [the constructor](https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values.
### Patches
We have patched the issue in GitHub commit [63c6a29d0f2d692b247f7bf81f8732d6442fad09](https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09).
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-2021-29576'} | 2022-04-26T18:17:16.903104Z | 2021-05-21T14:26:16Z | LOW | null | {'CWE-787', 'CWE-119'} | {'https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7cqx-92hp-x6wh', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29576'} | null | {'https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09'} | {'https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09'} |
PyPI | PYSEC-2021-470 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements. If input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`. 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-2021-29542', 'GHSA-4hrh-9vmp-2jgg'} | 2021-12-09T06:34:49.894355Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hrh-9vmp-2jgg'} | null | {'https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b'} | {'https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b'} |
PyPI | GHSA-947x-pv47-pp3q | Cross-site scripting in pywb | Webrecorder pywb before 2.6.0 allows XSS because it does not ensure that Jinja2 templates are autoescaped. | {'CVE-2021-39286'} | 2022-03-03T05:13:35.590334Z | 2021-09-02T17:16:18Z | MODERATE | null | {'CWE-79'} | {'https://github.com/webrecorder/pywb/compare/v-2.5.0...v-2.6.0', 'https://github.com/webrecorder/pywb/commit/f7bd84cdacdd665ff73ae8d09a202f60be2ebae9', 'https://github.com/webrecorder/pywb', 'https://nvd.nist.gov/vuln/detail/CVE-2021-39286'} | null | {'https://github.com/webrecorder/pywb/commit/f7bd84cdacdd665ff73ae8d09a202f60be2ebae9'} | {'https://github.com/webrecorder/pywb/commit/f7bd84cdacdd665ff73ae8d09a202f60be2ebae9'} |
PyPI | GHSA-mfjm-vh54-3f96 | Cookie-setting is not restricted based on the public suffix list | ### Impact
Responses from domain names whose public domain name suffix contains 1 or more periods (e.g. responses from `example.co.uk`, given its public domain name suffix is `co.uk`) are able to set cookies that are included in requests to any other domain sharing the same domain name suffix.
### Patches
Upgrade to Scrapy 2.6.0, which restricts cookies with their domain set to any of those in the [public suffix list](https://publicsuffix.org/).
If you are using Scrapy 1.8 or a lower version, and upgrading to Scrapy 2.6.0 is not an option, you may upgrade to Scrapy 1.8.2 instead.
### Workarounds
The only workaround for unpatched versions of Scrapy is to [disable cookies altogether](https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#std-setting-COOKIES_ENABLED), or [limit target domains](https://docs.scrapy.org/en/latest/topics/spiders.html#scrapy.spiders.Spider.allowed_domains) to a subset that does not include domain names with one of the public domain suffixes affected (those with 1 or more periods).
### References
* https://publicsuffix.org/
### For more information
If you have any questions or comments about this advisory:
* [Open an issue](https://github.com/scrapy/scrapy/issues)
* [Email us](mailto:opensource@zyte.com)
| null | 2022-03-07T20:47:42.301001Z | 2022-03-01T22:13:28Z | MODERATE | null | null | {'https://github.com/scrapy/scrapy/commit/e865c4430e58a4faa0e0766b23830f8423d6167a', 'https://github.com/scrapy/scrapy/security/advisories/GHSA-mfjm-vh54-3f96'} | null | {'https://github.com/scrapy/scrapy/commit/e865c4430e58a4faa0e0766b23830f8423d6167a'} | {'https://github.com/scrapy/scrapy/commit/e865c4430e58a4faa0e0766b23830f8423d6167a'} |
PyPI | PYSEC-2018-17 | null | Jupyter Notebook before 5.7.1 allows XSS via an untrusted notebook because nbconvert responses are considered to have the same origin as the notebook server. In other words, nbconvert endpoints can execute JavaScript with access to the server API. In notebook/nbconvert/handlers.py, NbconvertFileHandler and NbconvertPostHandler do not set a Content Security Policy to prevent this. | {'CVE-2018-19351', 'GHSA-49qr-xh3w-h436'} | 2021-06-10T06:51:21.489049Z | 2018-11-18T17:29:00Z | null | null | null | {'https://github.com/advisories/GHSA-49qr-xh3w-h436', 'https://pypi.org/project/notebook/#history', 'https://lists.debian.org/debian-lts-announce/2020/11/msg00033.html', 'https://groups.google.com/forum/#!topic/jupyter/hWzu2BSsplY', 'https://github.com/jupyter/notebook/blob/master/docs/source/changelog.rst', 'https://github.com/jupyter/notebook/commit/107a89fce5f413fb5728c1c5d2c7788e1fb17491'} | null | {'https://github.com/jupyter/notebook/commit/107a89fce5f413fb5728c1c5d2c7788e1fb17491'} | {'https://github.com/jupyter/notebook/commit/107a89fce5f413fb5728c1c5d2c7788e1fb17491'} |
PyPI | PYSEC-2021-535 | 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_sparse_binary_op_shared.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-gv26-jpj9-c8gq', 'CVE-2021-29607'} | 2021-12-09T06:35:00.010376Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gv26-jpj9-c8gq', 'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2', 'https://github.com/tensorflow/tensorflow/commit/f6fde895ef9c77d848061c0517f19d0ec2682f3a'} | null | {'https://github.com/tensorflow/tensorflow/commit/f6fde895ef9c77d848061c0517f19d0ec2682f3a', 'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2'} | {'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2', 'https://github.com/tensorflow/tensorflow/commit/f6fde895ef9c77d848061c0517f19d0ec2682f3a'} |
PyPI | PYSEC-2021-165 | 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'} | 2021-08-27T03:22:26.348588Z | 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 | GHSA-r5qj-cvf9-p85h | Code Injection in PyTorch Lightning | PyTorch Lightning version 1.5.10 and prior is vulnerable to code injection. There is currently a [patch](https://github.com/pytorchlightning/pytorch-lightning/commit/8b7a12c52e52a06408e9231647839ddb4665e8ae) available, anticipated to be part of the `1.6.0` release. | {'CVE-2022-0845'} | 2022-03-18T21:31:59.050920Z | 2022-03-06T00:00:16Z | CRITICAL | null | {'CWE-94'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-0845', 'https://github.com/PyTorchLightning/pytorch-lightning/pull/12212', 'https://github.com/pytorchlightning/pytorch-lightning/commit/8b7a12c52e52a06408e9231647839ddb4665e8ae', 'https://github.com/pytorchlightning/pytorch-lightning/', 'https://huntr.dev/bounties/a795bf93-c91e-4c79-aae8-f7d8bda92e2a'} | null | {'https://github.com/pytorchlightning/pytorch-lightning/commit/8b7a12c52e52a06408e9231647839ddb4665e8ae'} | {'https://github.com/pytorchlightning/pytorch-lightning/commit/8b7a12c52e52a06408e9231647839ddb4665e8ae'} |
PyPI | GHSA-f5cx-5wr3-5qrc | Reference binding to nullptr in boosted trees | ### Impact
An attacker can generate undefined behavior via a reference binding to nullptr in `BoostedTreesCalculateBestGainsPerFeature`:
```python
import tensorflow as tf
tf.raw_ops.BoostedTreesCalculateBestGainsPerFeature(
node_id_range=[],
stats_summary_list=[[1,2,3]],
l1=[1.0],
l2=[1.0],
tree_complexity =[1.0],
min_node_weight =[1.17],
max_splits=5)
```
A similar attack can occur in `BoostedTreesCalculateBestFeatureSplitV2`:
```python
import tensorflow as tf
tf.raw_ops.BoostedTreesCalculateBestFeatureSplitV2(
node_id_range=[],
stats_summaries_list=[[1,2,3]],
split_types=[''],
candidate_feature_ids=[1,2,3,4],
l1=[1],
l2=[1],
tree_complexity=[1.0],
min_node_weight=[1.17],
logits_dimension=5)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) does not validate the input values.
### Patches
We have patched the issue in GitHub commit [9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad](https://github.com/tensorflow/tensorflow/commit/9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad) and in commit. [429f009d2b2c09028647dd4bb7b3f6f414bbaad7](https://github.com/tensorflow/tensorflow/commit/429f009d2b2c09028647dd4bb7b3f6f414bbaad7).
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-2021-37662'} | 2022-03-03T05:14:14.804038Z | 2021-08-25T14:42:26Z | HIGH | null | {'CWE-824'} | {'https://github.com/tensorflow/tensorflow/commit/9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f5cx-5wr3-5qrc', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/429f009d2b2c09028647dd4bb7b3f6f414bbaad7', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37662'} | null | {'https://github.com/tensorflow/tensorflow/commit/429f009d2b2c09028647dd4bb7b3f6f414bbaad7', 'https://github.com/tensorflow/tensorflow/commit/9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad'} | {'https://github.com/tensorflow/tensorflow/commit/429f009d2b2c09028647dd4bb7b3f6f414bbaad7', 'https://github.com/tensorflow/tensorflow/commit/9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad'} |
PyPI | GHSA-r4pj-74mg-8868 | Division by 0 in `Conv2DBackpropFilter` | ### Impact
An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropFilter`:
```python
import tensorflow as tf
input_tensor = tf.constant([], shape=[0, 0, 1, 0], dtype=tf.float32)
filter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([], shape=[0, 0, 1, 1], dtype=tf.float32)
tf.raw_ops.Conv2DBackpropFilter(input=input_tensor, filter_sizes=filter_sizes,
out_backprop=out_backprop,
strides=[1, 66, 18, 1], use_cudnn_on_gpu=True,
padding='SAME', explicit_paddings=[],
data_format='NHWC', dilations=[1, 1, 1, 1])
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/496c2630e51c1a478f095b084329acedb253db6b/tensorflow/core/kernels/conv_grad_shape_utils.cc#L130) does a modulus operation where the divisor is controlled by the caller:
```cc
if (dims->in_depth % filter_shape.dim_size(num_dims - 2)) { ... }
```
### Patches
We have patched the issue in GitHub commit [fca9874a9b42a2134f907d2fb46ab774a831404a](https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a).
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-2021-29524'} | 2022-03-03T05:14:19.072094Z | 2021-05-21T14:21:47Z | LOW | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29524', 'https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r4pj-74mg-8868'} | null | {'https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a'} | {'https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a'} |
PyPI | GHSA-98j8-c9q4-r38g | Memory exhaustion in Tensorflow | ### Impact
The [implementation of `StringNGrams`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/string_ngrams_op.cc#L29-L161) can be used to trigger a denial of service attack by causing an OOM condition after an integer overflow:
```python
import tensorflow as tf
tf.raw_ops.StringNGrams(
data=['123456'],
data_splits=[0,1],
separator='a'*15,
ngram_widths=[],
left_pad='',
right_pad='',
pad_width=-5,
preserve_short_sequences=True)
```
We are missing a validation on `pad_witdh` and that result in computing a negative value for `ngram_width` which is later used to allocate parts of the output.
### Patches
We have patched the issue in GitHub commit [f68fdab93fb7f4ddb4eb438c8fe052753c9413e8](https://github.com/tensorflow/tensorflow/commit/f68fdab93fb7f4ddb4eb438c8fe052753c9413e8).
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-2022-21733'} | 2022-03-03T05:14:01.870612Z | 2022-02-10T00:20:51Z | MODERATE | null | {'CWE-190', 'CWE-400'} | {'https://github.com/tensorflow/tensorflow/commit/f68fdab93fb7f4ddb4eb438c8fe052753c9413e8', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/string_ngrams_op.cc#L29-L161', 'https://github.com/tensorflow/tensorflow/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21733', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98j8-c9q4-r38g'} | null | {'https://github.com/tensorflow/tensorflow/commit/f68fdab93fb7f4ddb4eb438c8fe052753c9413e8'} | {'https://github.com/tensorflow/tensorflow/commit/f68fdab93fb7f4ddb4eb438c8fe052753c9413e8'} |
PyPI | GHSA-w5gh-2wr2-pm6g | Denial of Service in Tensorflow | ### Impact
Changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments.
We have added fixes to this in f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode.
### Patches
We have patched the issue in adf095206f25471e864a8e63a0f1caef53a0e3a6 and will release patch releases for all versions between 1.15 and 2.3. Patch releases for versions between 1.15 and 2.1 will also contain cherry-picks of f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d.
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 Shuaike Dong, from Alipay Tian Qian Security Lab && Lab for Applied Security Research, CUHK. | {'CVE-2020-15206'} | 2022-03-03T05:13:13.746372Z | 2020-09-25T18:28:40Z | CRITICAL | null | {'CWE-20'} | {'https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15206', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w5gh-2wr2-pm6g', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/f760f88b4267d981e13f4b302c437ae800445968', 'https://github.com/tensorflow/tensorflow/commit/fcfef195637c6e365577829c4d67681695956e7d'} | null | {'https://github.com/tensorflow/tensorflow/commit/fcfef195637c6e365577829c4d67681695956e7d', 'https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6', 'https://github.com/tensorflow/tensorflow/commit/f760f88b4267d981e13f4b302c437ae800445968'} | {'https://github.com/tensorflow/tensorflow/commit/f760f88b4267d981e13f4b302c437ae800445968', 'https://github.com/tensorflow/tensorflow/commit/fcfef195637c6e365577829c4d67681695956e7d', 'https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6'} |
PyPI | GHSA-c5f8-35qr-q4fm | HTML injection in email and account expiry notifications | ### Impact
The notification emails sent for notifications for missed messages or for an expiring account are subject to HTML injection. In the case of the notification for missed messages, this could allow an attacker to insert forged content into the email.
The account expiry feature is not enabled by default and the HTML injection is not controllable by an attacker.
### Patches
This issue is fixed in #9200.
### Workarounds
For the missed messages notifications:
The `notif.html`, `notif_mail.html`, and `room.html` templates can be overridden with custom templates that manually escapes the variables using [JInja2's `escape` filter](https://jinja.palletsprojects.com/en/2.11.x/templates/#escape). See the `email.template_dir` setting.
For the account expiry notifications:
1. Account expiry can be disabled via the `account_validity.enabled` setting.
2. The `notice_expiry.html` template can be overridden with a custom template that manually escapes the variables using [JInja2's `escape` filter](https://jinja.palletsprojects.com/en/2.11.x/templates/#escape). See the `email.template_dir` setting. | {'CVE-2021-21333'} | 2022-03-03T05:13:04.847018Z | 2021-03-26T19:53:04Z | MODERATE | null | {'CWE-74'} | {'https://github.com/matrix-org/synapse/commit/e54746bdf7d5c831eabe4dcea76a7626f1de73df', 'https://github.com/matrix-org/synapse', 'https://github.com/matrix-org/synapse/releases/tag/v1.27.0', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21333', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TNNAJOZNMVMXM6AS7RFFKB4QLUJ4IFEY/', 'https://github.com/matrix-org/synapse/pull/9200', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-c5f8-35qr-q4fm'} | null | {'https://github.com/matrix-org/synapse/commit/e54746bdf7d5c831eabe4dcea76a7626f1de73df'} | {'https://github.com/matrix-org/synapse/commit/e54746bdf7d5c831eabe4dcea76a7626f1de73df'} |
PyPI | PYSEC-2013-30 | null | bson/_cbsonmodule.c in the mongo-python-driver (aka. pymongo) before 2.5.2, as used in MongoDB, allows context-dependent attackers to cause a denial of service (NULL pointer dereference and crash) via vectors related to decoding of an "invalid DBRef." | {'CVE-2013-2132'} | 2021-08-27T03:22:17.455260Z | 2013-08-15T17:55:00Z | null | null | null | {'http://ubuntu.com/usn/usn-1897-1', 'https://github.com/mongodb/mongo-python-driver/commit/a060c15ef87e0f0e72974c7c0e57fe811bbd06a2', 'http://www.debian.org/security/2013/dsa-2705', 'http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=710597', 'http://www.osvdb.org/93804', 'https://jira.mongodb.org/browse/PYTHON-532', 'http://lists.opensuse.org/opensuse-updates/2013-06/msg00180.html', 'http://seclists.org/oss-sec/2013/q2/447', 'http://www.securityfocus.com/bid/60252'} | null | {'https://github.com/mongodb/mongo-python-driver/commit/a060c15ef87e0f0e72974c7c0e57fe811bbd06a2'} | {'https://github.com/mongodb/mongo-python-driver/commit/a060c15ef87e0f0e72974c7c0e57fe811bbd06a2'} |
PyPI | GHSA-f8mr-jv2c-v8mg | Invalid root may become trusted root in The Update Framework (TUF) | ### Impact
The Python TUF reference implementation `tuf<0.12` will incorrectly trust a previously downloaded root metadata file which failed verification at download time. This allows an attacker who is able to serve multiple new versions of root metadata (i.e. by a man-in-the-middle attack) culminating in a version which has not been correctly signed to control the trust chain for future updates.
While investigating the reported vulnerability, we discovered that the detailed client workflow was not fully implemented. Specifically, for step 1.3 the newly downloaded root metadata was not being verified with a threshold of keys specified in the new root metadata file.
This missing step of the client workflow has been implemented in [PR #1101](https://github.com/theupdateframework/tuf/pull/1101), which is included in [v0.14.0](https://github.com/theupdateframework/tuf/releases/tag/v0.14.0) of tuf.
### Patches
A [fix](https://github.com/theupdateframework/tuf/pull/885), is available in version [0.12](https://github.com/theupdateframework/tuf/releases/tag/v0.12.0) and newer.
### Workarounds
No workarounds are known for this issue.
### References
* Pull request resolving the invalid root becoming trusted issue [PR 885](https://github.com/theupdateframework/tuf/pull/885)
* Pull request implementing self verification of newly downloaded root metadata [PR 1101](https://github.com/theupdateframework/tuf/pull/1101) | {'CVE-2020-15163'} | 2022-03-03T05:12:35.740359Z | 2020-09-09T17:29:27Z | HIGH | null | {'CWE-863', 'CWE-345'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15163', 'https://github.com/theupdateframework/tuf/security/advisories/GHSA-f8mr-jv2c-v8mg', 'https://github.com/theupdateframework/tuf', 'https://pypi.org/project/tuf', 'https://github.com/theupdateframework/tuf/releases/tag/v0.12.0', 'https://github.com/theupdateframework/tuf/commit/3d342e648fbacdf43a13d7ba8886aaaf07334af7', 'https://github.com/theupdateframework/tuf/pull/885'} | null | {'https://github.com/theupdateframework/tuf/commit/3d342e648fbacdf43a13d7ba8886aaaf07334af7'} | {'https://github.com/theupdateframework/tuf/commit/3d342e648fbacdf43a13d7ba8886aaaf07334af7'} |
PyPI | PYSEC-2022-14 | null | Improper Privilege Management in Conda loguru prior to 0.5.3. | {'CVE-2022-0338'} | 2022-01-28T21:25:28.417398Z | 2022-01-25T09:15:00Z | null | null | null | {'https://github.com/delgan/loguru/commit/ea39375e62f9b8f18e2ca798a5c0fb8c972b7eaa', 'https://huntr.dev/bounties/359bea50-2bc6-426a-b2f9-175d401b1ed0'} | null | {'https://github.com/delgan/loguru/commit/ea39375e62f9b8f18e2ca798a5c0fb8c972b7eaa'} | {'https://github.com/delgan/loguru/commit/ea39375e62f9b8f18e2ca798a5c0fb8c972b7eaa'} |
PyPI | GHSA-rwv7-3v45-hg29 | Out-of-bounds Read | An issue was discovered in Pillow before 8.2.0. There is an out-of-bounds read in J2kDecode, in j2ku_gray_i. This dates to Pillow 2.4.0. | {'CVE-2021-25288'} | 2022-03-07T20:47:09.921948Z | 2021-06-08T18:49:28Z | CRITICAL | null | {'CWE-125'} | {'https://pillow.readthedocs.io/en/stable/releasenotes/8.2.0.html#cve-2021-25287-cve-2021-25288-fix-oob-read-in-jpeg2kdecode', 'https://nvd.nist.gov/vuln/detail/CVE-2021-25288', 'https://github.com/python-pillow/Pillow/pull/5377#issuecomment-833821470', 'https://security.gentoo.org/glsa/202107-33', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MQHA5HAIBOYI3R6HDWCLAGFTIQP767FL/', 'https://github.com/python-pillow/Pillow/commit/3bf5eddb89afdf690eceaa52bc4d3546ba9a5f87', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/3bf5eddb89afdf690eceaa52bc4d3546ba9a5f87'} | {'https://github.com/python-pillow/Pillow/commit/3bf5eddb89afdf690eceaa52bc4d3546ba9a5f87'} |
PyPI | GHSA-wp77-4gmm-7cq8 | Incorrect validation of `SaveV2` inputs | ### Impact
The code for `tf.raw_ops.SaveV2` does not properly validate the inputs and an attacker can trigger a null pointer dereference:
```python
import tensorflow as tf
tf.raw_ops.SaveV2(
prefix=['tensorflow'],
tensor_name=['v'],
shape_and_slices=[],
tensors=[1,2,3])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/save_restore_v2_ops.cc) uses `ValidateInputs` to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above.
However, the validation uses `OP_REQUIRES` which translates to setting the `Status` object of the current `OpKernelContext` to an error status, followed by an empty `return` statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the next line in `Compute` that follows the call to `ValidateInputs`. This is equivalent to lacking the validation.
### Patches
We have patched the issue in GitHub commit [9728c60e136912a12d99ca56e106b7cce7af5986](https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986).
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-2021-37648'} | 2022-03-03T05:13:32.840734Z | 2021-08-25T14:43:29Z | HIGH | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37648', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp77-4gmm-7cq8', 'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} | null | {'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} | {'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} |
PyPI | GHSA-h6fg-mjxg-hqq4 | Integer truncation in Shard API usage | ### Impact
The `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/util/work_sharder.h#L59-L60
However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/random_op.cc#L204-L205
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/random_op.cc#L317-L318
In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption.
### Patches
We have patched the issue in 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575. We 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-2020-15202'} | 2022-03-03T05:14:22.856243Z | 2020-09-25T18:28:35Z | CRITICAL | null | {'CWE-197', 'CWE-754'} | {'https://github.com/tensorflow/tensorflow/commit/27b417360cbd671ef55915e4bb6bb06af8b8a832', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15202', 'https://github.com/tensorflow/tensorflow/commit/ca8c013b5e97b1373b3bb1c97ea655e69f31a575', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6fg-mjxg-hqq4', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/ca8c013b5e97b1373b3bb1c97ea655e69f31a575', 'https://github.com/tensorflow/tensorflow/commit/27b417360cbd671ef55915e4bb6bb06af8b8a832'} | {'https://github.com/tensorflow/tensorflow/commit/ca8c013b5e97b1373b3bb1c97ea655e69f31a575', 'https://github.com/tensorflow/tensorflow/commit/27b417360cbd671ef55915e4bb6bb06af8b8a832'} |
PyPI | PYSEC-2021-427 | null | A Regular Expression Denial of Service (ReDOS) vulnerability was discovered in Mpmath v1.0.0 when the mpmathify function is called. | {'CVE-2021-29063', 'GHSA-f865-m6cq-j9vx'} | 2021-11-16T21:20:29.021535Z | 2021-06-21T20:15:00Z | null | null | null | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/3M5O55E7VUDMXCPQR6MQTOIFDKHP36AA/', 'https://github.com/advisories/GHSA-f865-m6cq-j9vx', 'https://github.com/yetingli/SaveResults/blob/main/js/hosted-git-info.js', 'https://github.com/fredrik-johansson/mpmath/commit/46d44c3c8f3244017fe1eb102d564eb4ab8ef750', 'https://github.com/yetingli/PoCs/blob/main/CVE-2021-29063/Mpmath.md', 'https://www.npmjs.com/package/hosted-git-info', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MS2U6GLXQSRZJE2HVUAUMVFR2DWQLCZG/', 'https://github.com/npm/hosted-git-info/pull/76'} | null | {'https://github.com/fredrik-johansson/mpmath/commit/46d44c3c8f3244017fe1eb102d564eb4ab8ef750'} | {'https://github.com/fredrik-johansson/mpmath/commit/46d44c3c8f3244017fe1eb102d564eb4ab8ef750'} |
PyPI | PYSEC-2021-132 | null | Synapse is a Matrix reference homeserver written in python (pypi package matrix-synapse). Matrix is an ecosystem for open federated Instant Messaging and VoIP. In Synapse before version 1.25.0, a malicious homeserver could redirect requests to their .well-known file to a large file. This can lead to a denial of service attack where homeservers will consume significantly more resources when requesting the .well-known file of a malicious homeserver. This affects any server which accepts federation requests from untrusted servers. Issue is resolved in version 1.25.0. As a workaround the `federation_domain_whitelist` setting can be used to restrict the homeservers communicated with over federation. | {'CVE-2021-21274', 'GHSA-2hwx-mjrm-v3g8'} | 2021-08-27T03:22:06.616674Z | 2021-02-26T18:15:00Z | null | null | null | {'https://github.com/matrix-org/synapse/releases/tag/v1.25.0', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-2hwx-mjrm-v3g8', 'https://github.com/matrix-org/synapse/pull/8950', 'https://github.com/matrix-org/synapse/commit/ff5c4da1289cb5e097902b3e55b771be342c29d6'} | null | {'https://github.com/matrix-org/synapse/commit/ff5c4da1289cb5e097902b3e55b771be342c29d6'} | {'https://github.com/matrix-org/synapse/commit/ff5c4da1289cb5e097902b3e55b771be342c29d6'} |
PyPI | PYSEC-2021-562 | null | TensorFlow is an end-to-end open source platform for machine learning. The code for `tf.raw_ops.UncompressElement` can be made to trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/compression_ops.cc#L50-L53) obtains a pointer to a `CompressedElement` from a `Variant` tensor and then proceeds to dereference it for decompressing. There is no check that the `Variant` tensor contained a `CompressedElement`, so the pointer is actually `nullptr`. We have patched the issue in GitHub commit 7bdf50bb4f5c54a4997c379092888546c97c3ebd. 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-2021-37649', 'GHSA-6gv8-p3vj-pxvr'} | 2021-12-09T06:35:03.183506Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gv8-p3vj-pxvr'} | null | {'https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd'} | {'https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd'} |
PyPI | PYSEC-2021-831 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseFillEmptyRows` can be made to trigger a heap OOB access. This occurs whenever the size of `indices` does not match the size of `values`. 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-rg3m-hqc5-344v', 'CVE-2021-41224'} | 2021-12-09T06:35:44.787766Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v', 'https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b'} | null | {'https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b'} | {'https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b'} |
PyPI | PYSEC-2020-132 | null | In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with `nullptr`. However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue is patched in commit 0b5662bc, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-qh32-6jjc-qprm', 'CVE-2020-15209'} | 2020-10-29T16:15:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/0b5662bc2be13a8c8f044d925d87fb6e56247cd8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qh32-6jjc-qprm', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/0b5662bc2be13a8c8f044d925d87fb6e56247cd8'} | {'https://github.com/tensorflow/tensorflow/commit/0b5662bc2be13a8c8f044d925d87fb6e56247cd8'} |
PyPI | PYSEC-2021-718 | null | TensorFlow is an end-to-end open source platform for machine learning. The fix for CVE-2020-15209(https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15209) missed the case when the target shape of `Reshape` operator is given by the elements of a 1-D tensor. As such, the fix for the vulnerability(https://github.com/tensorflow/tensorflow/blob/9c1dc920d8ffb4893d6c9d27d1f039607b326743/tensorflow/lite/core/subgraph.cc#L1062-L1074) allowed passing a null-buffer-backed tensor with a 1D 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-2021-29592', 'GHSA-jjr8-m8g8-p6wv'} | 2021-12-09T06:35:30.746202Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f8378920345f4f4604202d4ab15ef64b2aceaa16', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jjr8-m8g8-p6wv'} | null | {'https://github.com/tensorflow/tensorflow/commit/f8378920345f4f4604202d4ab15ef64b2aceaa16'} | {'https://github.com/tensorflow/tensorflow/commit/f8378920345f4f4604202d4ab15ef64b2aceaa16'} |
PyPI | GHSA-fr28-569j-53c4 | 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). The intermediate-level directories of the filesystem cache had the system's standard umask rather than 0o077. | {'CVE-2020-24584'} | 2022-03-03T05:12:56.107855Z | 2021-03-18T20:30:01Z | HIGH | null | {'CWE-276'} | {'https://usn.ubuntu.com/4479-1/', 'https://www.openwall.com/lists/oss-security/2020/09/01/2', 'https://github.com/django/django/commit/1853724acaf17ed7414d54c7d2b5563a25025a71', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/OLGFFLMF3X6USMJD7V5F5P4K2WVUTO3T/', 'https://www.djangoproject.com/weblog/2020/sep/01/security-releases/', 'https://nvd.nist.gov/vuln/detail/CVE-2020-24584', '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/1853724acaf17ed7414d54c7d2b5563a25025a71'} | {'https://github.com/django/django/commit/1853724acaf17ed7414d54c7d2b5563a25025a71'} |
PyPI | PYSEC-2021-431 | null | django-helpdesk is vulnerable to Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') | {'GHSA-vfrc-ggmc-5jwv', 'CVE-2021-3950'} | 2021-11-23T19:22:08.078180Z | 2021-11-19T12:15:00Z | null | null | null | {'https://github.com/django-helpdesk/django-helpdesk/commit/04483bdac3b5196737516398b5ce0383875a5c60', 'https://github.com/advisories/GHSA-vfrc-ggmc-5jwv', 'https://huntr.dev/bounties/4d7a5fdd-b2de-467a-ade0-3f2fb386638e'} | null | {'https://github.com/django-helpdesk/django-helpdesk/commit/04483bdac3b5196737516398b5ce0383875a5c60'} | {'https://github.com/django-helpdesk/django-helpdesk/commit/04483bdac3b5196737516398b5ce0383875a5c60'} |
PyPI | PYSEC-2020-124 | 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'} | 2021-09-01T08:19:33.344299Z | 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-2021-827 | null | TensorFlow is an open source platform for machine learning. In affected versions the async implementation of `CollectiveReduceV2` suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected. | {'GHSA-gpfh-jvf9-7wg5', 'CVE-2021-41220'} | 2021-12-09T06:35:44.147623Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5'} | null | {'https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75'} | {'https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75'} |
PyPI | GHSA-mr7p-25v2-35wr | Path Traversal in nltk | NLTK Downloader before 3.4.5 is vulnerable to a directory traversal, allowing attackers to write arbitrary files via a ../ (dot dot slash) in an NLTK package (ZIP archive) that is mishandled during extraction. | {'CVE-2019-14751'} | 2022-03-03T05:13:40.004453Z | 2019-08-23T21:53:51Z | HIGH | null | {'CWE-22'} | {'http://lists.opensuse.org/opensuse-security-announce/2020-04/msg00001.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/QI4IJGLZQ5S7C5LNRNROHAO2P526XE3D/', 'https://github.com/mssalvatore/CVE-2019-14751_PoC', 'https://github.com/nltk/nltk/blob/3.4.5/ChangeLog', 'https://github.com/nltk/nltk/commit/f59d7ed8df2e0e957f7f247fe218032abdbe9a10', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/ZGZSSEJH7RHH3RBUEVWWYT75QU67J7SE/', 'http://lists.opensuse.org/opensuse-security-announce/2020-03/msg00054.html', 'https://salvatoresecurity.com/zip-slip-in-nltk-cve-2019-14751/', 'https://nvd.nist.gov/vuln/detail/CVE-2019-14751'} | null | {'https://github.com/nltk/nltk/commit/f59d7ed8df2e0e957f7f247fe218032abdbe9a10'} | {'https://github.com/nltk/nltk/commit/f59d7ed8df2e0e957f7f247fe218032abdbe9a10'} |
PyPI | PYSEC-2021-574 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a denial of service in `boosted_trees_create_quantile_stream_resource` by using negative arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that `num_streams` only contains non-negative numbers. In turn, [this results in using this value to allocate memory](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). However, `reserve` receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. We have patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992. 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-2021-37661', 'GHSA-gf88-j2mg-cc82'} | 2021-12-09T06:35:04.173783Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf88-j2mg-cc82'} | null | {'https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992'} | {'https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992'} |
PyPI | PYSEC-2021-45 | null | Products.PluggableAuthService is a pluggable Zope authentication and authorization framework. In Products.PluggableAuthService before version 2.6.0 there is an open redirect vulnerability. A maliciously crafted link to the login form and login functionality could redirect the browser to a different website. 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". | {'CVE-2021-21337', 'GHSA-p44j-xrqg-4xrr'} | 2021-03-12T13:48:00Z | 2021-03-08T21:15:00Z | null | null | null | {'https://github.com/zopefoundation/Products.PluggableAuthService/security/advisories/GHSA-p44j-xrqg-4xrr', 'https://pypi.org/project/Products.PluggableAuthService/', 'https://github.com/zopefoundation/Products.PluggableAuthService/commit/7eead067898852ebd3e0f143bc51295928528dfa'} | null | {'https://github.com/zopefoundation/Products.PluggableAuthService/commit/7eead067898852ebd3e0f143bc51295928528dfa'} | {'https://github.com/zopefoundation/Products.PluggableAuthService/commit/7eead067898852ebd3e0f143bc51295928528dfa'} |
PyPI | GHSA-xmq7-7fxm-rr79 | Denial of Service in Tensorflow | ### Impact
By controlling the `fill` argument of [`tf.strings.as_string`](https://www.tensorflow.org/api_docs/python/tf/strings/as_string), a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/as_string_op.cc#L68-L74
This can result in unexpected output:
```python
In [1]: tf.strings.as_string(input=[1234], width=6, fill='-')
Out[1]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['1234 '], dtype=object)>
In [2]: tf.strings.as_string(input=[1234], width=6, fill='+')
Out[2]: <tf.Tensor: shape=(1,), dtype=string, numpy=array([' +1234'], dtype=object)>
In [3]: tf.strings.as_string(input=[1234], width=6, fill="h")
Out[3]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['%6d'], dtype=object)>
In [4]: tf.strings.as_string(input=[1234], width=6, fill="d")
Out[4]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['12346d'], dtype=object)>
In [5]: tf.strings.as_string(input=[1234], width=6, fill="o")
Out[5]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['23226d'], dtype=object)>
In [6]: tf.strings.as_string(input=[1234], width=6, fill="x")
Out[6]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['4d26d'], dtype=object)>
In [7]: tf.strings.as_string(input=[1234], width=6, fill="g")
Out[7]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['8.67458e-3116d'], dtype=object)>
In [8]: tf.strings.as_string(input=[1234], width=6, fill="a")
Out[8]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['0x0.00ff7eebb4d4p-10226d'], dtype=object)>
In [9]: tf.strings.as_string(input=[1234], width=6, fill="c")
Out[9]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['\xd26d'], dtype=object)>
In [10]: tf.strings.as_string(input=[1234], width=6, fill="p")
Out[10]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['0x4d26d'], dtype=object)>
In [11]: tf.strings.as_string(input=[1234], width=6, fill='m')
Out[11]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['Success6d'], dtype=object)>
```
However, passing in `n` or `s` results in segmentation fault.
### Patches
We have patched the issue in 33be22c65d86256e6826666662e40dbdfe70ee83 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-2020-15203'} | 2022-03-03T05:14:01.043032Z | 2020-09-25T18:28:37Z | HIGH | null | {'CWE-20', 'CWE-134'} | {'https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15203', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83'} | {'https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83'} |
PyPI | GHSA-hh7m-rx4f-4vpv | CSRF can expose users authentication token | ### Issue
The /login and /change endpoints can return the authenticated user's authentication token in response to a GET request. Since GET requests aren't protected with a CSRF token, this could lead to a malicious 3rd party site acquiring the authentication token.
### Patches
Version 3.4.5 and soon to be released 4.0.0 are patched.
### Workarounds
If you aren't using authentication tokens - you can set the SECURITY_TOKEN_MAX_AGE to "0" (seconds) which should make the token unusable.
### References
None | {'CVE-2021-21241'} | 2022-03-03T05:13:30.663886Z | 2021-01-11T20:38:28Z | HIGH | null | {'CWE-352'} | {'https://github.com/Flask-Middleware/flask-security/security/advisories/GHSA-hh7m-rx4f-4vpv', 'https://github.com/Flask-Middleware/flask-security/commit/6d50ee9169acf813257c37b75babe9c28e83542a', 'https://github.com/Flask-Middleware/flask-security/releases/tag/3.4.5', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21241', 'https://github.com/Flask-Middleware/flask-security/pull/422', 'https://github.com/Flask-Middleware/flask-security/commit/61d313150b5f620d0b800896c4f2199005e84b1f', 'https://pypi.org/project/Flask-Security-Too'} | null | {'https://github.com/Flask-Middleware/flask-security/commit/61d313150b5f620d0b800896c4f2199005e84b1f', 'https://github.com/Flask-Middleware/flask-security/commit/6d50ee9169acf813257c37b75babe9c28e83542a'} | {'https://github.com/Flask-Middleware/flask-security/commit/61d313150b5f620d0b800896c4f2199005e84b1f', 'https://github.com/Flask-Middleware/flask-security/commit/6d50ee9169acf813257c37b75babe9c28e83542a'} |
PyPI | PYSEC-2022-55 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `ConcatV2` can be used to trigger a denial of service attack via a segfault caused by a type confusion. The `axis` argument is translated into `concat_dim` in the `ConcatShapeHelper` helper function. Then, a value for `min_rank` is computed based on `concat_dim`. This is then used to validate that the `values` tensor has at least the required rank. However, `WithRankAtLeast` receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that `min_rank` is a 32-bits value and the value of `axis`, the `rank` argument is a negative value, so the error check is bypassed. 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. | {'CVE-2022-21731', 'GHSA-m4hf-j54p-p353'} | 2022-03-09T00:17:30.689879Z | 2022-02-03T12:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/08d7b00c0a5a20926363849f611729f53f3ec022', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/shape_inference.cc#L345-L358', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/common_shape_fns.cc#L1961-L2059', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m4hf-j54p-p353'} | null | {'https://github.com/tensorflow/tensorflow/commit/08d7b00c0a5a20926363849f611729f53f3ec022'} | {'https://github.com/tensorflow/tensorflow/commit/08d7b00c0a5a20926363849f611729f53f3ec022'} |
PyPI | PYSEC-2020-68 | null | OAuthenticator is an OAuth login mechanism for JupyterHub. In oauthenticator from version 0.12.0 and before 0.12.2, the deprecated (in jupyterhub 1.2) configuration `Authenticator.whitelist`, which should be transparently mapped to `Authenticator.allowed_users` with a warning, is instead ignored by OAuthenticator classes, resulting in the same behavior as if this configuration has not been set. If this is the only mechanism of authorization restriction (i.e. no group or team restrictions in configuration) then all authenticated users will be allowed. Provider-based restrictions, including deprecated values such as `GitHubOAuthenticator.org_whitelist` are **not** affected. All users of OAuthenticator 0.12.0 and 0.12.1 with JupyterHub 1.2 (JupyterHub Helm chart 0.10.0-0.10.5) who use the `admin.whitelist.users` configuration in the jupyterhub helm chart or the `c.Authenticator.whitelist` configuration directly. Users of other deprecated configuration, e.g. `c.GitHubOAuthenticator.team_whitelist` are **not** affected. If you see a log line like this and expect a specific list of allowed usernames: "[I 2020-11-27 16:51:54.528 JupyterHub app:1717] Not using allowed_users. Any authenticated user will be allowed." you are likely affected. Updating oauthenticator to 0.12.2 is recommended. A workaround is to replace the deprecated `c.Authenticator.whitelist = ...` with `c.Authenticator.allowed_users = ...`. If any users have been authorized during this time who should not have been, they must be deleted via the API or admin interface, per the referenced documentation. | {'CVE-2020-26250', 'GHSA-384w-5v3f-q499'} | 2020-12-08T17:50:00Z | 2020-12-01T21:15:00Z | null | null | null | {'https://jupyterhub.readthedocs.io/en/1.2.2/getting-started/authenticators-users-basics.html#add-or-remove-users-from-the-hub', 'https://github.com/jupyterhub/oauthenticator/security/advisories/GHSA-384w-5v3f-q499', 'https://github.com/jupyterhub/oauthenticator/blob/master/docs/source/changelog.md#0122---2020-11-30', 'https://github.com/jupyterhub/oauthenticator/commit/a4aac191c16cf6281f3d346615aefa75702b02d7'} | null | {'https://github.com/jupyterhub/oauthenticator/commit/a4aac191c16cf6281f3d346615aefa75702b02d7'} | {'https://github.com/jupyterhub/oauthenticator/commit/a4aac191c16cf6281f3d346615aefa75702b02d7'} |
PyPI | PYSEC-2019-135 | null | When using wagtail-2fa before 1.3.0, if someone gains access to someone's Wagtail login credentials, they can log into the CMS and bypass the 2FA check by changing the URL. They can then add a new device and gain full access to the CMS. This problem has been patched in version 1.3.0. | {'GHSA-89px-ww3j-g2mm', 'CVE-2019-16766'} | 2020-10-09T13:27:00Z | 2019-11-29T17:15:00Z | null | null | null | {'https://github.com/LabD/wagtail-2fa/security/advisories/GHSA-89px-ww3j-g2mm', 'https://github.com/labd/wagtail-2fa/commit/13b12995d35b566df08a17257a23863ab6efb0ca', 'https://github.com/labd/wagtail-2fa/commit/a6711b29711729005770ff481b22675b35ff5c81'} | null | {'https://github.com/labd/wagtail-2fa/commit/a6711b29711729005770ff481b22675b35ff5c81', 'https://github.com/labd/wagtail-2fa/commit/13b12995d35b566df08a17257a23863ab6efb0ca'} | {'https://github.com/labd/wagtail-2fa/commit/a6711b29711729005770ff481b22675b35ff5c81', 'https://github.com/labd/wagtail-2fa/commit/13b12995d35b566df08a17257a23863ab6efb0ca'} |
PyPI | PYSEC-2021-466 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 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-j8qc-5fqr-52fp', 'CVE-2021-29538'} | 2021-12-09T06:34:49.257791Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp', 'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} | null | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} |
PyPI | PYSEC-2021-173 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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. 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-2021-29536', 'GHSA-2gfx-95x2-5v3x'} | 2021-08-27T03:22:27.845923Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2gfx-95x2-5v3x'} | null | {'https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e'} | {'https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e'} |
PyPI | PYSEC-2021-489 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from `tf.raw_ops.LoadAndRemapMatrix`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) assumes that the `ckpt_path` is always a valid scalar. However, an attacker can send any other tensor as the first argument of `LoadAndRemapMatrix`. This would cause the rank `CHECK` in `scalar<T>()()` to trigger and terminate the process. 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-2021-29561', 'GHSA-gvm4-h8j3-rjrq'} | 2021-12-09T06:34:52.832408Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gvm4-h8j3-rjrq', 'https://github.com/tensorflow/tensorflow/commit/77dd114513d7796e1e2b8aece214a380af26fbf4'} | null | {'https://github.com/tensorflow/tensorflow/commit/77dd114513d7796e1e2b8aece214a380af26fbf4'} | {'https://github.com/tensorflow/tensorflow/commit/77dd114513d7796e1e2b8aece214a380af26fbf4'} |
PyPI | GHSA-27qf-jwm8-g7f3 | FPE in LSH in TFLite | ### Impact
An attacker can craft a TFLite model that would trigger a division by zero error in LSH [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/lsh_projection.cc#L118).
```cc
int RunningSignBit(const TfLiteTensor* input, const TfLiteTensor* weight,
float seed) {
int input_item_bytes = input->bytes / SizeOfDimension(input, 0);
// ...
}
```
There is no check that the first dimension of the input is non zero.
### Patches
We have patched the issue in GitHub commit [0575b640091680cfb70f4dd93e70658de43b94f9](https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick thiscommit 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 Yakun Zhang of Baidu Security. | {'CVE-2021-37691'} | 2022-03-03T05:13:33.257862Z | 2021-08-25T14:39:17Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27qf-jwm8-g7f3', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37691'} | null | {'https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9'} | {'https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9'} |
PyPI | PYSEC-2021-523 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `DepthToSpace` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/depth_to_space.cc#L63-L69). An attacker can craft a model such that `params->block_size` is 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. | {'CVE-2021-29595', 'GHSA-vf94-36g5-69v8'} | 2021-12-09T06:34:58.107477Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vf94-36g5-69v8', 'https://github.com/tensorflow/tensorflow/commit/106d8f4fb89335a2c52d7c895b7a7485465ca8d9'} | null | {'https://github.com/tensorflow/tensorflow/commit/106d8f4fb89335a2c52d7c895b7a7485465ca8d9'} | {'https://github.com/tensorflow/tensorflow/commit/106d8f4fb89335a2c52d7c895b7a7485465ca8d9'} |
PyPI | PYSEC-2014-22 | null | The (1) load_djpeg function in JpegImagePlugin.py, (2) Ghostscript function in EpsImagePlugin.py, (3) load function in IptcImagePlugin.py, and (4) _copy function in Image.py in Python Image Library (PIL) 1.1.7 and earlier and Pillow before 2.3.1 do not properly create temporary files, which allow local users to overwrite arbitrary files and obtain sensitive information via a symlink attack on the temporary file. | {'CVE-2014-1932'} | 2021-07-15T02:22:16.928376Z | 2014-04-17T14:55:00Z | null | null | null | {'http://www.securityfocus.com/bid/65511', 'http://www.openwall.com/lists/oss-security/2014/02/11/1', 'https://security.gentoo.org/glsa/201612-52', 'https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=737059', 'http://www.ubuntu.com/usn/USN-2168-1', 'http://lists.opensuse.org/opensuse-updates/2014-05/msg00002.html', 'https://github.com/python-imaging/Pillow/commit/4e9f367dfd3f04c8f5d23f7f759ec12782e10ee7'} | null | {'https://github.com/python-imaging/Pillow/commit/4e9f367dfd3f04c8f5d23f7f759ec12782e10ee7'} | {'https://github.com/python-imaging/Pillow/commit/4e9f367dfd3f04c8f5d23f7f759ec12782e10ee7'} |
PyPI | PYSEC-2020-173 | null | The pip package before 19.2 for Python allows Directory Traversal when a URL is given in an install command, because a Content-Disposition header can have ../ in a filename, as demonstrated by overwriting the /root/.ssh/authorized_keys file. This occurs in _download_http_url in _internal/download.py. | {'CVE-2019-20916', 'GHSA-gpvv-69j7-gwj8'} | 2021-03-15T16:18:00Z | 2020-09-04T20:15:00Z | null | null | null | {'https://lists.debian.org/debian-lts-announce/2020/09/msg00010.html', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00005.html', 'https://github.com/pypa/pip/issues/6413', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00010.html', 'https://github.com/gzpan123/pip/commit/a4c735b14a62f9cb864533808ac63936704f2ace', 'https://github.com/pypa/pip/compare/19.1.1...19.2', 'https://github.com/advisories/GHSA-gpvv-69j7-gwj8'} | null | {'https://github.com/gzpan123/pip/commit/a4c735b14a62f9cb864533808ac63936704f2ace'} | {'https://github.com/gzpan123/pip/commit/a4c735b14a62f9cb864533808ac63936704f2ace'} |
PyPI | GHSA-x7rp-74x2-mjf3 | Segfault in Tensorflow | ### Impact
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. Thus, the [following code](https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L248-L265
) sets up conditions to cause a heap buffer overflow:
```cc
auto per_batch_counts = BatchedMap<W>(num_batches);
int batch_idx = 0;
for (int idx = 0; idx < num_values; ++idx) {
while (idx >= splits_values(batch_idx)) {
batch_idx++;
}
const auto& value = values_values(idx);
if (value >= 0 && (maxlength_ <= 0 || value < maxlength_)) {
per_batch_counts[batch_idx - 1][value] = 1;
}
}
```
A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will never be 1, hence there will be no hashmap at index 0 in `per_batch_counts`. Trying to access that in the user code results in a segmentation fault.
### Patches
We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.
We recommend users to upgrade to TensorFlow 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 is a variant of [GHSA-p5f8-gfw5-33w4](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4) | {'CVE-2020-15200'} | 2021-08-26T15:13:38Z | 2020-09-25T18:28:32Z | MODERATE | null | {'CWE-20', 'CWE-787', 'CWE-122'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15200', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x7rp-74x2-mjf3', 'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow', '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 | GHSA-hggx-3h72-49ww | Moderate severity vulnerability that affects Pillow | Buffer overflow in the ImagingLibTiffDecode function in libImaging/TiffDecode.c in Pillow before 3.1.1 allows remote attackers to overwrite memory via a crafted TIFF file. | {'CVE-2016-0740'} | 2022-03-03T05:13:15.863124Z | 2018-07-24T20:03:51Z | MODERATE | null | {'CWE-119'} | {'https://nvd.nist.gov/vuln/detail/CVE-2016-0740', 'https://security.gentoo.org/glsa/201612-52', 'https://github.com/advisories/GHSA-hggx-3h72-49ww', 'https://github.com/python-pillow/Pillow/commit/6dcbf5bd96b717c58d7b642949da8d323099928e', 'https://github.com/python-pillow/Pillow/blob/c3cb690fed5d4bf0c45576759de55d054916c165/CHANGES.rst', 'http://www.debian.org/security/2016/dsa-3499', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/6dcbf5bd96b717c58d7b642949da8d323099928e'} | {'https://github.com/python-pillow/Pillow/commit/6dcbf5bd96b717c58d7b642949da8d323099928e'} |
PyPI | GHSA-hx2x-85gr-wrpq | Out of bounds access in tensorflow-lite | ### Impact
In TensorFlow Lite models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reference_ops.h#L2625-L2631
Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer.
This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced 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 all positive, 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 discovered from a variant analysis of [GHSA-p2cq-cprg-frvm](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2cq-cprg-frvm). | {'CVE-2020-15212'} | 2021-08-26T15:20:18Z | 2020-09-25T18:29:00Z | HIGH | null | {'CWE-787'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15212', 'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq', '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-xr2c-5w89-63pv | Untrusted Search Path in Poetry | Poetry prior to v1.1.9 was discovered to contain an untrusted search path which causes the application to behave in unexpected ways when users execute Poetry commands in a directory containing malicious content. This vulnerability occurs when the application is ran on Windows OS. | {'CVE-2022-26184'} | 2022-03-30T21:17:05.181660Z | 2022-03-23T00:00:24Z | CRITICAL | null | {'CWE-426'} | {'https://github.com/python-poetry/', 'https://github.com/python-poetry/poetry/releases/tag/1.1.9', 'https://nvd.nist.gov/vuln/detail/CVE-2022-26184', 'https://github.com/python-poetry/poetry-core/pull/205/commits/fa9cb6f358ae840885c700f954317f34838caba7'} | null | {'https://github.com/python-poetry/poetry-core/pull/205/commits/fa9cb6f358ae840885c700f954317f34838caba7'} | {'https://github.com/python-poetry/poetry-core/pull/205/commits/fa9cb6f358ae840885c700f954317f34838caba7'} |
PyPI | PYSEC-2021-807 | null | TensorFlow is an open source platform for machine learning. In affected versions 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. 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. 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-2021-41199', 'GHSA-5hx2-qx8j-qjqm'} | 2021-12-09T06:35:41.080668Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hx2-qx8j-qjqm', 'https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837', 'https://github.com/tensorflow/tensorflow/issues/46914'} | null | {'https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837'} | {'https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837'} |
PyPI | GHSA-43fq-w8qq-v88h | Out-of-bounds read in Pillow | In libImaging/SgiRleDecode.c in Pillow through 7.0.0, a number of out-of-bounds reads exist in the parsing of SGI image files, a different issue than CVE-2020-5311. | {'CVE-2020-11538'} | 2022-03-03T05:13:46.477221Z | 2020-07-27T21:52:36Z | HIGH | null | {'CWE-125'} | {'https://github.com/python-pillow/Pillow/pull/4504', 'https://nvd.nist.gov/vuln/detail/CVE-2020-11538', 'https://github.com/python-pillow/Pillow/commit/2ef59fdbaeb756bc512ab3f2ad15ac45665b303d', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/HOKHNWV2VS5GESY7IBD237E7C6T3I427/', 'https://pillow.readthedocs.io/en/stable/releasenotes/index.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/BEBCPE4F2VHTIT6EZA2YZQZLPVDEBJGD/', 'https://snyk.io/vuln/SNYK-PYTHON-PILLOW-574574', 'https://usn.ubuntu.com/4430-2/', 'https://github.com/python-pillow/Pillow/blob/master/docs/releasenotes/7.1.0.rst#security', 'https://pillow.readthedocs.io/en/stable/releasenotes/7.1.0.html', 'https://usn.ubuntu.com/4430-1/', 'https://github.com/python-pillow/Pillow/pull/4538', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/2ef59fdbaeb756bc512ab3f2ad15ac45665b303d'} | {'https://github.com/python-pillow/Pillow/commit/2ef59fdbaeb756bc512ab3f2ad15ac45665b303d'} |
PyPI | PYSEC-2021-554 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. 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-2021-37641', 'GHSA-9c8h-vvrj-w2p8'} | 2021-12-09T06:35:02.493921Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c8h-vvrj-w2p8', 'https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373'} | null | {'https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373'} | {'https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373'} |
PyPI | GHSA-mcgw-5fhw-3fq8 | Incorrect Authorization in saleor | Incorrect Authorization in saleor prior to 3.1.2. | {'CVE-2022-0932'} | 2022-03-25T19:31:58.123393Z | 2022-03-12T00:00:34Z | MODERATE | null | {'CWE-863'} | {'https://huntr.dev/bounties/88ae4cbc-c697-401b-8b04-7dc4e03ad8eb', 'https://nvd.nist.gov/vuln/detail/CVE-2022-0932', 'https://github.com/saleor/saleor/commit/521dfd6394f3926a77c60d8633c058e16d0f916d', 'https://github.com/saleor/saleor'} | null | {'https://github.com/saleor/saleor/commit/521dfd6394f3926a77c60d8633c058e16d0f916d'} | {'https://github.com/saleor/saleor/commit/521dfd6394f3926a77c60d8633c058e16d0f916d'} |
PyPI | GHSA-hj5v-574p-mj7c | Regular expression deinal of service in py | A denial of service via regular expression in the py.path.svnwc component of py (aka python-py) through 1.9.0 could be used by attackers to cause a compute-time denial of service attack by supplying malicious input to the blame functionality. | {'CVE-2020-29651'} | 2022-03-03T05:13:39.087574Z | 2021-04-20T16:39:57Z | HIGH | null | {'CWE-400'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-29651', 'https://github.com/pytest-dev/py/issues/256', 'https://github.com/pytest-dev/py/pull/257', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/AYWNYEV3FGDHPIHX4DDUDMFZ6NLCQRC4/', 'https://github.com/pytest-dev/py/pull/257/commits/4a9017dc6199d2a564b6e4b0aa39d6d8870e4144', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/CHDTINIBJZ67T3W74QTBIY5LPKAXEOGR/'} | null | {'https://github.com/pytest-dev/py/pull/257/commits/4a9017dc6199d2a564b6e4b0aa39d6d8870e4144'} | {'https://github.com/pytest-dev/py/pull/257/commits/4a9017dc6199d2a564b6e4b0aa39d6d8870e4144'} |
PyPI | PYSEC-2021-104 | null | Zope is an open-source web application server. This advisory extends the previous advisory at https://github.com/zopefoundation/Zope/security/advisories/GHSA-5pr9-v234-jw36 with additional cases of TAL expression traversal vulnerabilities. Most Python modules are not available for using in TAL expressions that you can add through-the-web, for example in Zope Page Templates. This restriction avoids file system access, for example via the 'os' module. But some of the untrusted modules are available indirectly through Python modules that are available for direct use. By default, you need to have the Manager role to add or edit Zope Page Templates through the web. Only sites that allow untrusted users to add/edit Zope Page Templates through the web are at risk. The problem has been fixed in Zope 5.21 and 4.6.1. The workaround is the same as for https://github.com/zopefoundation/Zope/security/advisories/GHSA-5pr9-v234-jw36: A site administrator can restrict adding/editing Zope Page Templates through the web using the standard Zope user/role permission mechanisms. Untrusted users should not be assigned the Zope Manager role and adding/editing Zope Page Templates through the web should be restricted to trusted users only. | {'GHSA-5pr9-v234-jw36', 'CVE-2021-32674', 'GHSA-rpcg-f9q6-2mq6', 'GHSA-5vq5-pg3r-9ph3'} | 2021-06-22T04:54:57.652841Z | 2021-06-08T18:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-5vq5-pg3r-9ph3', 'https://github.com/zopefoundation/Zope/commit/1d897910139e2c0b11984fc9b78c1da1365bec21', 'https://pypi.org/project/Zope/', 'https://github.com/zopefoundation/Zope/security/advisories/GHSA-5pr9-v234-jw36', 'https://github.com/zopefoundation/Zope/security/advisories/GHSA-rpcg-f9q6-2mq6'} | null | {'https://github.com/zopefoundation/Zope/commit/1d897910139e2c0b11984fc9b78c1da1365bec21'} | {'https://github.com/zopefoundation/Zope/commit/1d897910139e2c0b11984fc9b78c1da1365bec21'} |
PyPI | PYSEC-2021-411 | null | TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. 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-2021-41219', 'GHSA-4f99-p9c2-3j8x'} | 2021-11-13T06:52:45.099185Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x'} | null | {'https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae'} | {'https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae'} |
PyPI | PYSEC-2016-7 | null | Integer overflow in the ImagingResampleHorizontal function in libImaging/Resample.c in Pillow before 3.1.1 allows remote attackers to have unspecified impact via negative values of the new size, which triggers a heap-based buffer overflow. | {'CVE-2016-4009', 'GHSA-hvr8-466p-75rh'} | 2021-07-05T00:01:24.042207Z | 2016-04-13T16:59:00Z | null | null | null | {'https://security.gentoo.org/glsa/201612-52', 'https://github.com/advisories/GHSA-hvr8-466p-75rh', 'http://www.securityfocus.com/bid/86064', 'https://github.com/python-pillow/Pillow/pull/1714', 'https://github.com/python-pillow/Pillow/commit/4e0d9b0b9740d258ade40cce248c93777362ac1e', 'https://github.com/python-pillow/Pillow/blob/c3cb690fed5d4bf0c45576759de55d054916c165/CHANGES.rst'} | null | {'https://github.com/python-pillow/Pillow/commit/4e0d9b0b9740d258ade40cce248c93777362ac1e'} | {'https://github.com/python-pillow/Pillow/commit/4e0d9b0b9740d258ade40cce248c93777362ac1e'} |
PyPI | GHSA-2gfx-95x2-5v3x | Heap buffer overflow in `QuantizedReshape` | ### Impact
An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization:
```python
import tensorflow as tf
tensor = tf.constant([], dtype=tf.qint32)
shape = tf.constant([], dtype=tf.int32)
input_min = tf.constant([], dtype=tf.float32)
input_max = tf.constant([], dtype=tf.float32)
tf.raw_ops.QuantizedReshape(tensor=tensor, shape=shape, input_min=input_min, input_max=input_max)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly:
```cc
const auto& input_min_float_tensor = ctx->input(2);
...
const float input_min_float = input_min_float_tensor.flat<float>()(0);
const auto& input_max_float_tensor = ctx->input(3);
...
const float input_max_float = input_max_float_tensor.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 [a324ac84e573fba362a5e53d4e74d5de6729933e](https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e).
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-2021-29536'} | 2022-03-03T05:14:05.624783Z | 2021-05-21T14:22:32Z | LOW | null | {'CWE-787'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29536', 'https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2gfx-95x2-5v3x'} | null | {'https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e'} | {'https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e'} |
PyPI | PYSEC-2018-99 | null | pyro before 3.15 unsafely handles pid files in temporary directory locations and opening the pid file as root. An attacker can use this flaw to overwrite arbitrary files via symlinks. | {'GHSA-xrr4-74mc-rpjc', 'CVE-2011-2765'} | 2021-08-27T03:22:17.581707Z | 2018-08-20T13:29:00Z | null | null | null | {'https://github.com/advisories/GHSA-xrr4-74mc-rpjc', 'https://pythonhosted.org/Pyro/12-changes.html', 'https://github.com/irmen/Pyro3/commit/554e095a62c4412c91f981e72fd34a936ac2bf1e', 'https://bugs.debian.org/631912'} | null | {'https://github.com/irmen/Pyro3/commit/554e095a62c4412c91f981e72fd34a936ac2bf1e'} | {'https://github.com/irmen/Pyro3/commit/554e095a62c4412c91f981e72fd34a936ac2bf1e'} |
PyPI | PYSEC-2022-22 | null | Improper Access Control in Pypi calibreweb prior to 0.6.16. | {'CVE-2022-0273', 'GHSA-vgmw-9cww-qq99'} | 2022-02-04T19:19:26.662601Z | 2022-01-30T14:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-vgmw-9cww-qq99', 'https://huntr.dev/bounties/8f27686f-d698-4ab6-8ef0-899125792f13', 'https://github.com/janeczku/calibre-web/commit/0c0313f375bed7b035c8c0482bbb09599e16bfcf'} | null | {'https://github.com/janeczku/calibre-web/commit/0c0313f375bed7b035c8c0482bbb09599e16bfcf'} | {'https://github.com/janeczku/calibre-web/commit/0c0313f375bed7b035c8c0482bbb09599e16bfcf'} |
PyPI | PYSEC-2021-368 | null | Zope is an open-source web application server. Zope versions prior to versions 4.6.3 and 5.3 have a remote code execution security issue. In order to be affected, one must use Python 3 for one's Zope deployment, run Zope 4 below version 4.6.3 or Zope 5 below version 5.3, and have the optional `Products.PythonScripts` add-on package installed. By default, one must have the admin-level Zope "Manager" role to add or edit Script (Python) objects through the web. Only sites that allow untrusted users to add/edit these scripts through the web are at risk. Zope releases 4.6.3 and 5.3 are not vulnerable. As a workaround, a site administrator can restrict adding/editing Script (Python) objects through the web using the standard Zope user/role permission mechanisms. Untrusted users should not be assigned the Zope Manager role and adding/editing these scripts through the web should be restricted to trusted users only. This is the default configuration in Zope. | {'GHSA-g4gq-j4p2-j8fr', 'CVE-2021-32811', 'GHSA-qcx9-j53g-ccgf'} | 2021-10-12T02:55:36.218662Z | 2021-08-02T22:15:00Z | null | null | null | {'https://github.com/zopefoundation/Zope/security/advisories/GHSA-g4gq-j4p2-j8fr', 'https://github.com/zopefoundation/Zope/commit/f72a18dda8e9bf2aedb46168761668464a4be988', 'https://github.com/zopefoundation/AccessControl/security/advisories/GHSA-qcx9-j53g-ccgf'} | null | {'https://github.com/zopefoundation/Zope/commit/f72a18dda8e9bf2aedb46168761668464a4be988'} | {'https://github.com/zopefoundation/Zope/commit/f72a18dda8e9bf2aedb46168761668464a4be988'} |
PyPI | PYSEC-2021-692 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`. 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-pvrc-hg3f-58r6', 'CVE-2021-29566'} | 2021-12-09T06:35:26.175336Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pvrc-hg3f-58r6', 'https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f'} | null | {'https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f'} | {'https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f'} |
PyPI | PYSEC-2021-738 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`. The implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty. Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen in this op's implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. 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-2021-29612', 'GHSA-2xgj-xhgf-ggjv'} | 2021-12-09T06:35:34.038446Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2xgj-xhgf-ggjv', 'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2', 'https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0'} | null | {'https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0', 'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2'} | {'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2', 'https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0'} |
PyPI | PYSEC-2017-45 | null | Cross-site scripting (XSS) vulnerability in IPython 3.x before 3.2 allows remote attackers to inject arbitrary web script or HTML via vectors involving JSON error messages and the /api/contents path. | {'CVE-2015-4706'} | 2021-07-15T02:22:14.767338Z | 2017-09-21T14:29:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=1235688', 'http://www.securityfocus.com/bid/75328', 'https://github.com/ipython/ipython/commit/7222bd53ad089a65fd610fab4626f9d0ab47dfce', 'https://ipython.org/ipython-doc/3/whatsnew/version3.html', 'http://www.openwall.com/lists/oss-security/2015/06/22/7', 'https://github.com/ipython/ipython/commit/c2078a53543ed502efd968649fee1125e0eb549c'} | null | {'https://github.com/ipython/ipython/commit/7222bd53ad089a65fd610fab4626f9d0ab47dfce', 'https://github.com/ipython/ipython/commit/c2078a53543ed502efd968649fee1125e0eb549c'} | {'https://github.com/ipython/ipython/commit/7222bd53ad089a65fd610fab4626f9d0ab47dfce', 'https://github.com/ipython/ipython/commit/c2078a53543ed502efd968649fee1125e0eb549c'} |
PyPI | GHSA-crch-j389-5f84 | Heap OOB write in TFLite | ### Impact
A specially crafted TFLite model could trigger an OOB write on heap in the TFLite implementation of [`ArgMin`/`ArgMax`](https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/arg_min_max.cc#L52-L59):
```cc
TfLiteIntArray* output_dims = TfLiteIntArrayCreate(NumDimensions(input) - 1);
int j = 0;
for (int i = 0; i < NumDimensions(input); ++i) {
if (i != axis_value) {
output_dims->data[j] = SizeOfDimension(input, i);
++j;
}
}
```
If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the condition in the `if` is never true, so code writes past the last valid element of `output_dims->data`.
### Patches
We have patched the issue in GitHub commit [c59c37e7b2d563967da813fa50fe20b21f4da683](https://github.com/tensorflow/tensorflow/commit/c59c37e7b2d563967da813fa50fe20b21f4da683).
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 members of the Aivul Team from Qihoo 360. | {'CVE-2021-29603'} | 2022-03-03T05:13:27.649387Z | 2021-05-21T14:28:15Z | LOW | null | {'CWE-787'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29603', 'https://github.com/tensorflow/tensorflow/commit/c59c37e7b2d563967da813fa50fe20b21f4da683', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-crch-j389-5f84'} | null | {'https://github.com/tensorflow/tensorflow/commit/c59c37e7b2d563967da813fa50fe20b21f4da683'} | {'https://github.com/tensorflow/tensorflow/commit/c59c37e7b2d563967da813fa50fe20b21f4da683'} |
PyPI | GHSA-246w-56m2-5899 | Cross-site scripting (XSS) vulnerability in the password reset endpoint | ### Impact
The password reset endpoint served via Synapse was vulnerable to cross-site scripting (XSS) attacks. The impact depends on the configuration of the domain that Synapse is deployed on, but may allow access to cookies and other browser data, CSRF vulnerabilities, and access to other resources served on the same domain or parent domains.
### Patches
This is fixed in #9200.
### Workarounds
Depending on the needs and configuration of the homeserver a few options are available:
1. Password resets can be disabled by delegating email to a third-party service (via the `account_threepid_delegates.email` setting) or disabling email (by not configuring the `email` setting).
2. If the homeserver is not configured to use passwords (via the `password_config.enabled` setting) then the affected endpoint can be blocked at a reverse proxy:
* `/_synapse/client/password_reset/email/submit_token`
3. The `password_reset_confirmation.html` template can be overridden with a custom template that manually escapes the variables using [JInja2's `escape` filter](https://jinja.palletsprojects.com/en/2.11.x/templates/#escape). See the `email.template_dir` setting. | {'CVE-2021-21332'} | 2022-03-03T05:12:57.224765Z | 2021-03-26T19:52:54Z | MODERATE | null | {'CWE-79'} | {'https://github.com/matrix-org/synapse/commit/e54746bdf7d5c831eabe4dcea76a7626f1de73df', 'https://github.com/matrix-org/synapse', 'https://github.com/matrix-org/synapse/releases/tag/v1.27.0', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21332', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TNNAJOZNMVMXM6AS7RFFKB4QLUJ4IFEY/', 'https://github.com/matrix-org/synapse/pull/9200', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-246w-56m2-5899'} | null | {'https://github.com/matrix-org/synapse/commit/e54746bdf7d5c831eabe4dcea76a7626f1de73df'} | {'https://github.com/matrix-org/synapse/commit/e54746bdf7d5c831eabe4dcea76a7626f1de73df'} |
PyPI | GHSA-qf7v-8hj3-4xw7 | Improper Verification of Cryptographic Signature in PySAML2 | PySAML2 before 5.0.0 does not check that the signature in a SAML document is enveloped and thus signature wrapping is effective, i.e., it is affected by XML Signature Wrapping (XSW). The signature information and the node/object that is signed can be in different places and thus the signature verification will succeed, but the wrong data will be used. This specifically affects the verification of assertion that have been signed. | {'CVE-2020-5390'} | 2022-03-03T05:14:15.972813Z | 2020-05-06T19:41:29Z | HIGH | null | {'CWE-347'} | {'https://github.com/IdentityPython/pysaml2/commit/f27c7e7a7010f83380566a219fd6a290a00f2b6e', 'https://lists.debian.org/debian-lts-announce/2020/02/msg00025.html', 'https://usn.ubuntu.com/4245-1/', 'https://github.com/IdentityPython/pysaml2/releases/tag/v5.0.0', 'https://pypi.org/project/pysaml2/5.0.0/', 'https://github.com/IdentityPython/pysaml2/releases', 'https://github.com/IdentityPython/pysaml2/blob/master/CHANGELOG.md#500-2020-01-13', 'https://nvd.nist.gov/vuln/detail/CVE-2020-5390', 'https://github.com/IdentityPython/pysaml2/commit/5e9d5acbcd8ae45c4e736ac521fd2df5b1c62e25', 'https://www.debian.org/security/2020/dsa-4630'} | null | {'https://github.com/IdentityPython/pysaml2/commit/f27c7e7a7010f83380566a219fd6a290a00f2b6e', 'https://github.com/IdentityPython/pysaml2/commit/5e9d5acbcd8ae45c4e736ac521fd2df5b1c62e25'} | {'https://github.com/IdentityPython/pysaml2/commit/5e9d5acbcd8ae45c4e736ac521fd2df5b1c62e25', 'https://github.com/IdentityPython/pysaml2/commit/f27c7e7a7010f83380566a219fd6a290a00f2b6e'} |
PyPI | PYSEC-2021-32 | null | OMERO.web is open source Django-based software for managing microscopy imaging. OMERO.web before version 5.9.0 supports redirection to a given URL after performing login or switching the group context. These URLs are not validated, allowing redirection to untrusted sites. OMERO.web 5.9.0 adds URL validation before redirecting. External URLs are not considered valid, unless specified in the omero.web.redirect_allowed_hosts setting. | {'GHSA-g4rf-pc26-6hmr', 'CVE-2021-21377'} | 2021-03-27T02:20:00Z | 2021-03-23T16:15:00Z | null | null | null | {'https://github.com/ome/omero-web/commit/952f8e5d28532fbb14fb665982211329d137908c', 'https://pypi.org/project/omero-web/', 'https://github.com/ome/omero-web/security/advisories/GHSA-g4rf-pc26-6hmr', 'https://github.com/ome/omero-web/blob/master/CHANGELOG.md#590-march-2021', 'https://www.openmicroscopy.org/security/advisories/2021-SV2/'} | null | {'https://github.com/ome/omero-web/commit/952f8e5d28532fbb14fb665982211329d137908c'} | {'https://github.com/ome/omero-web/commit/952f8e5d28532fbb14fb665982211329d137908c'} |
PyPI | PYSEC-2021-153 | null | TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.RaggedTensorToVariant` with arguments specifying an invalid ragged tensor results in a null pointer dereference. The implementation of `RaggedTensorToVariant` operations(https://github.com/tensorflow/tensorflow/blob/904b3926ed1c6c70380d5313d282d248a776baa1/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L39-L40) does not validate that the ragged tensor argument is non-empty. Since `batched_ragged` contains no elements, `batched_ragged.splits` is a null vector, thus `batched_ragged.splits(0)` will result in dereferencing `nullptr`. 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-84mw-34w6-2q43', 'CVE-2021-29516'} | 2021-08-27T03:22:24.214869Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/b055b9c474cd376259dde8779908f9eeaf097d93', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-84mw-34w6-2q43'} | null | {'https://github.com/tensorflow/tensorflow/commit/b055b9c474cd376259dde8779908f9eeaf097d93'} | {'https://github.com/tensorflow/tensorflow/commit/b055b9c474cd376259dde8779908f9eeaf097d93'} |
PyPI | GHSA-v6rh-hp5x-86rv | Potential bypass of an upstream access control based on URL paths in Django | In Django 2.2 before 2.2.25, 3.1 before 3.1.14, and 3.2 before 3.2.10, HTTP requests for URLs with trailing newlines could bypass upstream access control based on URL paths. This issue has low severity, according to the Django security policy. | {'CVE-2021-44420'} | 2022-03-03T05:12:27.798936Z | 2021-12-09T19:09:37Z | HIGH | null | {'CWE-287'} | {'https://security.netapp.com/advisory/ntap-20211229-0006/', 'https://www.djangoproject.com/weblog/2021/dec/07/security-releases/', 'https://github.com/django/django/commit/d4dcd5b9dd9e462fec8220e33e3e6c822b7e88a6', 'https://groups.google.com/forum/#!forum/django-announce', 'https://www.openwall.com/lists/oss-security/2021/12/07/1', 'https://github.com/django/django', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/B4SQG2EAF4WCI2SLRL6XRDJ3RPK3ZRDV/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-44420', 'https://docs.djangoproject.com/en/3.2/releases/security/'} | null | {'https://github.com/django/django/commit/d4dcd5b9dd9e462fec8220e33e3e6c822b7e88a6'} | {'https://github.com/django/django/commit/d4dcd5b9dd9e462fec8220e33e3e6c822b7e88a6'} |
PyPI | PYSEC-2021-503 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.ReverseSequence` allows for stack overflow and/or `CHECK`-fail based denial of service. The implementation(https://github.com/tensorflow/tensorflow/blob/5b3b071975e01f0d250c928b2a8f901cd53b90a7/tensorflow/core/kernels/reverse_sequence_op.cc#L114-L118) fails to validate that `seq_dim` and `batch_dim` arguments are valid. Negative values for `seq_dim` can result in stack overflow or `CHECK`-failure, depending on the version of Eigen code used to implement the operation. Similar behavior can be exhibited by invalid values of `batch_dim`. 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-6qgm-fv6v-rfpv', 'CVE-2021-29575'} | 2021-12-09T06:34:55.002066Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6qgm-fv6v-rfpv', 'https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23'} | null | {'https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23'} | {'https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23'} |
PyPI | PYSEC-2021-850 | null | In Django-wiki, versions 0.0.20 to 0.7.8 are vulnerable to Stored Cross-Site Scripting (XSS) in Notifications Section. An attacker who has access to edit pages can inject JavaScript payload in the title field. When a victim gets a notification regarding the changes made in the application, the payload in the notification panel renders and loads external JavaScript. | {'GHSA-3m3h-v9hv-9j4h', 'CVE-2021-25986'} | 2021-12-14T08:21:59.357980Z | 2021-11-23T20:15:00Z | null | null | null | {'https://pypi.org/project/wiki', 'https://github.com/advisories/GHSA-3m3h-v9hv-9j4h', 'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25986', 'https://github.com/django-wiki/django-wiki/commit/9eaccc7519e4206a4d2f22640882f0737b2da9c5', 'https://nvd.nist.gov/vuln/detail/CVE-2021-25986'} | null | {'https://github.com/django-wiki/django-wiki/commit/9eaccc7519e4206a4d2f22640882f0737b2da9c5'} | {'https://github.com/django-wiki/django-wiki/commit/9eaccc7519e4206a4d2f22640882f0737b2da9c5'} |
PyPI | PYSEC-2020-48 | null | Hyperledger Indy Node is the server portion of a distributed ledger purpose-built for decentralized identity. In Hyperledger Indy before version 1.12.4, there is lack of signature verification on a specific transaction which enables an attacker to make certain unauthorized alterations to the ledger. Updating a DID with a nym transaction will be written to the ledger if neither ROLE or VERKEY are being changed, regardless of sender. A malicious DID with no particular role can ask an update for another DID (but cannot modify its verkey or role). This is bad because 1) Any DID can write a nym transaction to the ledger (i.e., any DID can spam the ledger with nym transactions), 2) Any DID can change any other DID's alias, 3) The update transaction modifies the ledger metadata associated with a DID. | {'GHSA-wh2w-39f4-rpv2', 'CVE-2020-11093'} | 2020-12-31T19:25:00Z | 2020-12-24T20:15:00Z | null | null | null | {'https://github.com/hyperledger/indy-node/blob/master/CHANGELOG.md#1124', 'https://github.com/hyperledger/indy-node/blob/master/docs/source/auth_rules.md', 'https://github.com/hyperledger/indy-node/commit/55056f22c83b7c3520488b615e1577e0f895d75a', 'https://github.com/hyperledger/indy-node/security/advisories/GHSA-wh2w-39f4-rpv2'} | null | {'https://github.com/hyperledger/indy-node/commit/55056f22c83b7c3520488b615e1577e0f895d75a'} | {'https://github.com/hyperledger/indy-node/commit/55056f22c83b7c3520488b615e1577e0f895d75a'} |
PyPI | PYSEC-2022-75 | null | Tensorflow is an Open Source Machine Learning Framework. TensorFlow is vulnerable to a heap OOB write in `Grappler`. The `set_output` function writes to an array at the specified index. Hence, this gives a malicious user a write primitive. 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. | {'CVE-2022-23566', 'GHSA-5qw5-89mw-wcg2'} | 2022-03-09T00:17:33.179728Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/graph_properties.cc#L1132-L1141', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.h#L394', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5qw5-89mw-wcg2', 'https://github.com/tensorflow/tensorflow/commit/97282c6d0d34476b6ba033f961590b783fa184cd'} | null | {'https://github.com/tensorflow/tensorflow/commit/97282c6d0d34476b6ba033f961590b783fa184cd'} | {'https://github.com/tensorflow/tensorflow/commit/97282c6d0d34476b6ba033f961590b783fa184cd'} |
PyPI | PYSEC-2021-446 | 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'} | 2021-12-09T06:34:46.214930Z | 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 | PYSEC-2020-295 | 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'} | 2021-12-09T06:34:43.980213Z | 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-2021-295 | 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 `CHECK`-fail in `tf.raw_ops.MapStage`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L513) does not check that the `key` input is a valid non-empty tensor. We have patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. 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-2021-37673', 'GHSA-278g-rq84-9hmg'} | 2021-08-27T03:22:46.123018Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/d7de67733925de196ec8863a33445b73f9562d1d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-278g-rq84-9hmg'} | null | {'https://github.com/tensorflow/tensorflow/commit/d7de67733925de196ec8863a33445b73f9562d1d'} | {'https://github.com/tensorflow/tensorflow/commit/d7de67733925de196ec8863a33445b73f9562d1d'} |
PyPI | GHSA-5p3x-r448-pc62 | Improper Verification of Cryptographic Signature in PySAML2 | ### Impact
All users of pysaml2 that use the default `CryptoBackendXmlSec1` backend and need to verify signed SAML documents are impacted. `pysaml2 <= 6.4.1` does not ensure that a signed SAML document is correctly signed. The default `CryptoBackendXmlSec1` backend is using the `xmlsec1` binary to verify the signature of signed SAML documents, but by default, `xmlsec1` accepts any type of key found within the given document. `xmlsec1` needs to be configured explicitly to only use only _x509 certificates_ for the verification process of the SAML document signature.
### Patches
Users should upgrade to pysaml2 `v6.5.0`.
### Workarounds
No workaround provided at this point.
### References
This issue has been reported in the past at the xmlsec1 mailing list:
https://www.aleksey.com/pipermail/xmlsec/2013/009717.html
### Credits
- Brian Wolff
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [pysaml2](https://github.com/IdentityPython/pysaml2)
* Email us at [the incident-response address](mailto:incident-response@idpy.org) | {'CVE-2021-21239'} | 2022-03-03T05:12:33.192423Z | 2021-01-21T14:12:18Z | LOW | null | {'CWE-347'} | {'https://github.com/IdentityPython/pysaml2/commit/46578df0695269a16f1c94171f1429873f90ed99', 'https://github.com/IdentityPython/pysaml2/security/advisories/GHSA-5p3x-r448-pc62', 'https://pypi.org/project/pysaml2', 'https://www.aleksey.com/pipermail/xmlsec/2013/009717.html', 'https://github.com/IdentityPython/pysaml2/releases/tag/v6.5.0', 'https://lists.debian.org/debian-lts-announce/2021/02/msg00038.html', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21239'} | null | {'https://github.com/IdentityPython/pysaml2/commit/46578df0695269a16f1c94171f1429873f90ed99'} | {'https://github.com/IdentityPython/pysaml2/commit/46578df0695269a16f1c94171f1429873f90ed99'} |
PyPI | PYSEC-2021-780 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a `std::vector`. However, as `std::vector::resize` takes the size argument as a `size_t` and `output_size` is an `int`, there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in `CombinedNonMaxSuppression`. We have patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58. 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-vmjw-c2vp-p33c', 'CVE-2021-37669'} | 2021-12-09T06:35:38.342648Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vmjw-c2vp-p33c', 'https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58', 'https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d'} | null | {'https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d', 'https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58'} | {'https://github.com/tensorflow/tensorflow/commit/b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58', 'https://github.com/tensorflow/tensorflow/commit/3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d'} |
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