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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | PYSEC-2021-97 | null | The thefuck (aka The Fuck) package before 3.31 for Python allows Path Traversal that leads to arbitrary file deletion via the "undo archive operation" feature. | {'GHSA-8wwf-2644-f8x4', 'CVE-2021-34363'} | 2021-06-16T00:03:24.982635Z | 2021-06-10T11:15:00Z | null | null | null | {'https://vuln.ryotak.me/advisories/48', 'https://github.com/nvbn/thefuck/releases/tag/3.31', 'https://github.com/nvbn/thefuck/commit/e343c577cd7da4d304b837d4a07ab4df1e023092', 'https://github.com/advisories/GHSA-8wwf-2644-f8x4'} | null | {'https://github.com/nvbn/thefuck/commit/e343c577cd7da4d304b837d4a07ab4df1e023092'} | {'https://github.com/nvbn/thefuck/commit/e343c577cd7da4d304b837d4a07ab4df1e023092'} |
PyPI | PYSEC-2021-322 | null | Wasmtime is an open source runtime for WebAssembly & WASI. Wasmtime before version 0.30.0 is affected by a type confusion vulnerability. As a Rust library the `wasmtime` crate clearly marks which functions are safe and which are `unsafe`, guaranteeing that if consumers never use `unsafe` then it should not be possible to have memory unsafety issues in their embeddings of Wasmtime. An issue was discovered in the safe API of `Linker::func_*` APIs. These APIs were previously not sound when one `Engine` was used to create the `Linker` and then a different `Engine` was used to create a `Store` and then the `Linker` was used to instantiate a module into that `Store`. Cross-`Engine` usage of functions is not supported in Wasmtime and this can result in type confusion of function pointers, resulting in being able to safely call a function with the wrong type. Triggering this bug requires using at least two `Engine` values in an embedding and then additionally using two different values with a `Linker` (one at the creation time of the `Linker` and another when instantiating a module with the `Linker`). It's expected that usage of more-than-one `Engine` in an embedding is relatively rare since an `Engine` is intended to be a globally shared resource, so the expectation is that the impact of this issue is relatively small. The fix implemented is to change this behavior to `panic!()` in Rust instead of silently allowing it. Using different `Engine` instances with a `Linker` is a programmer bug that `wasmtime` catches at runtime. This bug has been patched and users should upgrade to Wasmtime version 0.30.0. If you cannot upgrade Wasmtime and are using more than one `Engine` in your embedding it's recommended to instead use only one `Engine` for the entire program if possible. An `Engine` is designed to be a globally shared resource that is suitable to have only one for the lifetime of an entire process. If using multiple `Engine`s is required then code should be audited to ensure that `Linker` is only used with one `Engine`. | {'CVE-2021-39219', 'GHSA-q879-9g95-56mx'} | 2021-09-17T22:30:49.947373Z | 2021-09-17T20:15:00Z | null | null | null | {'https://github.com/bytecodealliance/wasmtime/security/advisories/GHSA-q879-9g95-56mx', 'https://github.com/bytecodealliance/wasmtime/commit/b39f087414f27ae40c44449ed5d1154e03449bff', 'https://crates.io/crates/wasmtime'} | null | {'https://github.com/bytecodealliance/wasmtime/commit/b39f087414f27ae40c44449ed5d1154e03449bff'} | {'https://github.com/bytecodealliance/wasmtime/commit/b39f087414f27ae40c44449ed5d1154e03449bff'} |
PyPI | PYSEC-2020-288 | null | In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-mxjj-953w-2c2v', 'CVE-2020-15208'} | 2021-12-09T06:34:43.041754Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mxjj-953w-2c2v', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157d', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157d'} | {'https://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157d'} |
PyPI | PYSEC-2020-322 | null | In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, to mimic Python's indexing with negative values, TFLite uses `ResolveAxis` to convert negative values to positive indices. However, the only check that the converted index is now valid is only present in debug builds. If the `DCHECK` does not trigger, then code execution moves ahead with a negative index. This, in turn, results in accessing data out of bounds which results in segfaults and/or data corruption. The issue is patched in commit 2d88f470dea2671b430884260f3626b1fe99830a, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15207', 'GHSA-q4qf-3fc6-8x34'} | 2021-12-09T06:35:14.570909Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/2d88f470dea2671b430884260f3626b1fe99830a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q4qf-3fc6-8x34', '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/2d88f470dea2671b430884260f3626b1fe99830a'} | {'https://github.com/tensorflow/tensorflow/commit/2d88f470dea2671b430884260f3626b1fe99830a'} |
PyPI | PYSEC-2020-267 | null | Red Discord Bot before version 3.4.1 has an unauthorized privilege escalation exploit in the Mod module. This exploit allows Discord users with a high privilege level within the guild to bypass hierarchy checks when the application is in a specific condition that is beyond that user's control. By abusing this exploit, it is possible to perform destructive actions within the guild the user has high privileges in. This exploit has been fixed in version 3.4.1. As a workaround, unloading the Mod module with unload mod or, disabling the massban command with command disable global massban can render this exploit not accessible. We still highly recommend updating to 3.4.1 to completely patch this issue. | {'CVE-2020-15278', 'GHSA-mp9m-g7qj-6vqr'} | 2021-11-16T03:58:45.518170Z | 2020-10-28T17:15:00Z | null | null | null | {'https://github.com/Cog-Creators/Red-DiscordBot/releases/tag/3.4.1', 'https://github.com/Cog-Creators/Red-DiscordBot/security/advisories/GHSA-mp9m-g7qj-6vqr', 'https://github.com/Cog-Creators/Red-DiscordBot/commit/726bfd38adfdfaef760412a68e01447b470f438b'} | null | {'https://github.com/Cog-Creators/Red-DiscordBot/commit/726bfd38adfdfaef760412a68e01447b470f438b'} | {'https://github.com/Cog-Creators/Red-DiscordBot/commit/726bfd38adfdfaef760412a68e01447b470f438b'} |
PyPI | PYSEC-2021-637 | null | TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's `saved_model_cli` tool is vulnerable to a code injection as it calls `eval` on user supplied strings. This can be used by attackers to run arbitrary code on the plaform where the CLI tool runs. However, given that the tool is always run manually, the impact of this is not severe. We have patched this by adding a `safe` flag which defaults to `True` and an explicit warning for users. 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-3rcw-9p9x-582v', 'CVE-2021-41228'} | 2021-12-09T06:35:11.562556Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3rcw-9p9x-582v'} | null | {'https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7'} | {'https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7'} |
PyPI | PYSEC-2021-267 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.QuantizeAndDequantizeV4Grad` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L126) uses the `axis` value as the size argument to `absl::InlinedVector` constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer. We have patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, and TensorFlow 2.4.3, as these are also affected and still in supported range. | {'CVE-2021-37645', 'GHSA-9w2p-5mgw-p94c'} | 2021-08-27T03:22:43.539250Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/96f364a1ca3009f98980021c4b32be5fdcca33a1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9w2p-5mgw-p94c'} | null | {'https://github.com/tensorflow/tensorflow/commit/96f364a1ca3009f98980021c4b32be5fdcca33a1'} | {'https://github.com/tensorflow/tensorflow/commit/96f364a1ca3009f98980021c4b32be5fdcca33a1'} |
PyPI | PYSEC-2021-508 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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-2021-29580', 'GHSA-x8h6-xgqx-jqgp'} | 2021-12-09T06:34:55.778346Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp'} | null | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925'} | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925'} |
PyPI | PYSEC-2021-158 | null | TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., `std::vector<absl::flat_hash_map<int64,T>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3. | {'CVE-2021-29521', 'GHSA-hr84-fqvp-48mm'} | 2021-08-27T03:22:25.027733Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm'} | null | {'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5'} | {'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5'} |
PyPI | PYSEC-2022-91 | null | Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that `TensorByteSize` would trigger `CHECK` failures. `TensorShape` constructor throws a `CHECK`-fail if shape is partial or has a number of elements that would overflow the size of an `int`. The `PartialTensorShape` constructor instead does not cause a `CHECK`-abort if the shape is partial, which is exactly what this function needs to be able to return `-1`. 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-2022-23582', 'GHSA-4j82-5ccr-4r8v'} | 2022-03-09T00:17:35.169396Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4j82-5ccr-4r8v', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/attr_value_util.cc#L46-L50', 'https://github.com/tensorflow/tensorflow/commit/c2426bba00a01de6913738df8fa78e0215fcce02'} | null | {'https://github.com/tensorflow/tensorflow/commit/c2426bba00a01de6913738df8fa78e0215fcce02'} | {'https://github.com/tensorflow/tensorflow/commit/c2426bba00a01de6913738df8fa78e0215fcce02'} |
PyPI | GHSA-968f-66r5-5v74 | HTTP Request Smuggling in Waitress: Invalid whitespace characters in headers (Follow-up) | ### Impact
The patches introduced to fix https://github.com/Pylons/waitress/security/advisories/GHSA-m5ff-3wj3-8ph4 were not complete and still would allow an attacker to smuggle requests/split a HTTP request with invalid data.
This updates the existing CVE with ID: CVE-2019-16789
### Patches
Waitress version 1.4.2 has been updated to now validate HTTP headers better to avoid the issue, completely fixing all known issues with whitespace.
### Workarounds
There are no work-arounds, upgrading to Waitress 1.4.2 is highly recommended.
### References
See https://github.com/Pylons/waitress/security/advisories/GHSA-m5ff-3wj3-8ph4 for more information on the security issue.
### For more information
If you have any questions or comments about this advisory:
* open an issue at https://github.com/Pylons/waitress/issues (if not sensitive or security related)
* email the Pylons Security mailing list: pylons-project-security@googlegroups.com (if security related) | {'CVE-2019-16789'} | 2022-04-25T23:16:55.739172Z | 2020-01-06T18:44:21Z | HIGH | null | {'CWE-444'} | {'https://github.com/Pylons/waitress/security/advisories/GHSA-968f-66r5-5v74', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/LYEOTGWJZVKPRXX2HBNVIYWCX73QYPM5/', 'https://access.redhat.com/errata/RHSA-2020:0720', 'https://www.oracle.com/security-alerts/cpuapr2022.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/GVDHR2DNKCNQ7YQXISJ45NT4IQDX3LJ7/', 'https://github.com/github/advisory-review/pull/14604', 'https://nvd.nist.gov/vuln/detail/CVE-2019-16789', 'https://docs.pylonsproject.org/projects/waitress/en/latest/#security-fixes', 'https://github.com/Pylons/waitress/commit/11d9e138125ad46e951027184b13242a3c1de017'} | null | {'https://github.com/Pylons/waitress/commit/11d9e138125ad46e951027184b13242a3c1de017'} | {'https://github.com/Pylons/waitress/commit/11d9e138125ad46e951027184b13242a3c1de017'} |
PyPI | PYSEC-2020-158 | null | In xmpp-http-upload before version 0.4.0, when the GET method is attacked, attackers can read files which have a `.data` suffix and which are accompanied by a JSON file with the `.meta` suffix. This can lead to Information Disclosure and in some shared-hosting scenarios also to circumvention of authentication or other limitations on the outbound (GET) traffic. For example, in a scenario where a single server has multiple instances of the application running (with separate DATA_ROOT settings), an attacker who has knowledge about the directory structure is able to read files from any other instance to which the process has read access. If instances have individual authentication (for example, HTTP authentication via a reverse proxy, source IP based filtering) or other restrictions (such as quotas), attackers may circumvent those limits in such a scenario by using the Directory Traversal to retrieve data from the other instances. If the associated XMPP server (or anyone knowing the SECRET_KEY) is malicious, they can write files outside the DATA_ROOT. The files which are written are constrained to have the `.meta` and the `.data` suffixes; the `.meta` file will contain the JSON with the Content-Type of the original request and the `.data` file will contain the payload. The issue is patched in version 0.4.0. | {'GHSA-hwv5-w8gm-fq9f', 'CVE-2020-15239'} | 2020-10-23T03:09:00Z | 2020-10-06T19:15:00Z | null | null | null | {'https://github.com/horazont/xmpp-http-upload/security/advisories/GHSA-hwv5-w8gm-fq9f', 'https://pypi.org/project/xmpp-http-upload/#history', 'https://github.com/horazont/xmpp-http-upload/pull/12', 'https://github.com/horazont/xmpp-http-upload/commit/82056540191e89f0cd697c81f57714c00962ed75'} | null | {'https://github.com/horazont/xmpp-http-upload/commit/82056540191e89f0cd697c81f57714c00962ed75'} | {'https://github.com/horazont/xmpp-http-upload/commit/82056540191e89f0cd697c81f57714c00962ed75'} |
PyPI | GHSA-vf94-36g5-69v8 | Division by zero in TFLite's implementation of `DepthToSpace` | ### Impact
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):
```cc
const int block_size = params->block_size;
...
const int input_channels = input->dims->data[3];
...
int output_channels = input_channels / block_size / block_size;
```
An attacker can craft a model such that `params->block_size` is 0.
### Patches
We have patched the issue in GitHub commit [106d8f4fb89335a2c52d7c895b7a7485465ca8d9](https://github.com/tensorflow/tensorflow/commit/106d8f4fb89335a2c52d7c895b7a7485465ca8d9).
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-2021-29595'} | 2022-03-03T05:14:04.139271Z | 2021-05-21T14:27:49Z | LOW | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29595', '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-2022-151 | null | Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow is vulnerable to an integer overflow during cost estimation for crop and resize. Since the cropping parameters are user controlled, a malicious person can trigger undefined behavior. 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-2022-23587', 'GHSA-8jj7-5vxc-pg2q'} | 2022-03-09T00:18:29.453433Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8jj7-5vxc-pg2q', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L2621-L2689', 'https://github.com/tensorflow/tensorflow/commit/0aaaae6eca5a7175a193696383f582f53adab23f'} | null | {'https://github.com/tensorflow/tensorflow/commit/0aaaae6eca5a7175a193696383f582f53adab23f'} | {'https://github.com/tensorflow/tensorflow/commit/0aaaae6eca5a7175a193696383f582f53adab23f'} |
PyPI | PYSEC-2020-334 | null | In affected versions of TensorFlow the tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area. If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault. This is because the allocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overridden. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | {'CVE-2020-26268', 'GHSA-hhvc-g5hv-48c6'} | 2021-12-09T06:35:16.406292Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hhvc-g5hv-48c6', 'https://github.com/tensorflow/tensorflow/commit/c1e1fc899ad5f8c725dcbb6470069890b5060bc7'} | null | {'https://github.com/tensorflow/tensorflow/commit/c1e1fc899ad5f8c725dcbb6470069890b5060bc7'} | {'https://github.com/tensorflow/tensorflow/commit/c1e1fc899ad5f8c725dcbb6470069890b5060bc7'} |
PyPI | PYSEC-2017-19 | null | An exploitable vulnerability exists in the YAML parsing functionality in the parse_yaml_query method in parser.py in MLAlchemy before 0.2.2. When processing YAML-Based queries for data, a YAML parser can execute arbitrary Python commands resulting in command execution because load is used where safe_load should have been used. An attacker can insert Python into loaded YAML to trigger this vulnerability. | {'CVE-2017-16615', 'GHSA-xpm8-98mx-h4c5'} | 2021-07-05T00:01:22.762966Z | 2017-11-08T03:29:00Z | null | null | null | {'https://github.com/advisories/GHSA-xpm8-98mx-h4c5', 'https://github.com/thanethomson/MLAlchemy/commit/bc795757febdcce430d89f9d08f75c32d6989d3c', 'https://github.com/thanethomson/MLAlchemy/issues/1', 'https://joel-malwarebenchmark.github.io/blog/2017/11/08/cve-2017-16615-critical-restful-web-applications-vulnerability/'} | null | {'https://github.com/thanethomson/MLAlchemy/commit/bc795757febdcce430d89f9d08f75c32d6989d3c'} | {'https://github.com/thanethomson/MLAlchemy/commit/bc795757febdcce430d89f9d08f75c32d6989d3c'} |
PyPI | PYSEC-2015-24 | null | Cross-site scripting (XSS) vulnerability in the file browser in notebook/notebookapp.py in IPython Notebook before 3.2.2 and Jupyter Notebook 4.0.x before 4.0.5 allows remote attackers to inject arbitrary web script or HTML via a folder name. NOTE: this was originally reported as a cross-site request forgery (CSRF) vulnerability, but this may be inaccurate. | {'CVE-2015-6938'} | 2021-07-15T02:22:14.906376Z | 2015-09-21T19:59:00Z | null | null | null | {'http://lists.fedoraproject.org/pipermail/package-announce/2015-September/166460.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-September/166471.html', 'https://github.com/jupyter/notebook/commit/dd9876381f0ef09873d8c5f6f2063269172331e3', 'http://lists.opensuse.org/opensuse-updates/2015-10/msg00016.html', 'http://seclists.org/oss-sec/2015/q3/474', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-September/167670.html', 'https://github.com/jupyter/notebook/commit/35f32dd2da804d108a3a3585b69ec3295b2677ed', 'https://bugzilla.redhat.com/show_bug.cgi?id=1259405', 'https://github.com/ipython/ipython/commit/3ab41641cf6fce3860c73d5cf4645aa12e1e5892', 'http://seclists.org/oss-sec/2015/q3/544'} | null | {'https://github.com/ipython/ipython/commit/3ab41641cf6fce3860c73d5cf4645aa12e1e5892', 'https://github.com/jupyter/notebook/commit/35f32dd2da804d108a3a3585b69ec3295b2677ed', 'https://github.com/jupyter/notebook/commit/dd9876381f0ef09873d8c5f6f2063269172331e3'} | {'https://github.com/ipython/ipython/commit/3ab41641cf6fce3860c73d5cf4645aa12e1e5892', 'https://github.com/jupyter/notebook/commit/dd9876381f0ef09873d8c5f6f2063269172331e3', 'https://github.com/jupyter/notebook/commit/35f32dd2da804d108a3a3585b69ec3295b2677ed'} |
PyPI | PYSEC-2021-334 | null | parlai is a framework for training and evaluating AI models on a variety of openly available dialogue datasets. In affected versions the package is vulnerable to YAML deserialization attack caused by unsafe loading which leads to Arbitary code execution. This security bug is patched by avoiding unsafe loader users should update to version above v1.1.0. If upgrading is not possible then users can change the Loader used to SafeLoader as a workaround. See commit 507d066ef432ea27d3e201da08009872a2f37725 for details. | {'CVE-2021-39207', 'GHSA-m87f-9fvv-2mgg'} | 2021-09-23T16:57:40.954858Z | 2021-09-10T23:15:00Z | null | null | null | {'https://github.com/facebookresearch/ParlAI/commit/507d066ef432ea27d3e201da08009872a2f37725', 'https://github.com/facebookresearch/ParlAI/security/advisories/GHSA-m87f-9fvv-2mgg', 'https://github.com/facebookresearch/ParlAI/commit/4374fa2aba383db6526ab36e939eb1cf8ef99879'} | null | {'https://github.com/facebookresearch/ParlAI/commit/507d066ef432ea27d3e201da08009872a2f37725', 'https://github.com/facebookresearch/ParlAI/commit/4374fa2aba383db6526ab36e939eb1cf8ef99879'} | {'https://github.com/facebookresearch/ParlAI/commit/507d066ef432ea27d3e201da08009872a2f37725', 'https://github.com/facebookresearch/ParlAI/commit/4374fa2aba383db6526ab36e939eb1cf8ef99879'} |
PyPI | PYSEC-2021-764 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a crash via a floating point exception in `tf.raw_ops.ResourceGather`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L725-L731) computes the value of a value, `batch_size`, and then divides by it without checking that this value is not 0. We have patched the issue in GitHub commit ac117ee8a8ea57b73d34665cdf00ef3303bc0b11. 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-qjj8-32p7-h289', 'CVE-2021-37653'} | 2021-12-09T06:35:36.903192Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qjj8-32p7-h289', 'https://github.com/tensorflow/tensorflow/commit/ac117ee8a8ea57b73d34665cdf00ef3303bc0b11'} | null | {'https://github.com/tensorflow/tensorflow/commit/ac117ee8a8ea57b73d34665cdf00ef3303bc0b11'} | {'https://github.com/tensorflow/tensorflow/commit/ac117ee8a8ea57b73d34665cdf00ef3303bc0b11'} |
PyPI | GHSA-xpfp-f569-q3p2 | SQL Injection in Django | Django 3.1.x before 3.1.13 and 3.2.x before 3.2.5 allows QuerySet.order_by SQL injection if order_by is untrusted input from a client of a web application. | {'CVE-2021-35042'} | 2022-03-07T20:46:57.413338Z | 2021-09-22T17:34:49Z | CRITICAL | null | {'CWE-89'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/SS6NJTBYWOX6J7G4U3LUOILARJKWPQ5Y/', 'https://github.com/django/django/commit/0bd57a879a0d54920bb9038a732645fb917040e9', 'https://groups.google.com/forum/#!forum/django-announce', 'https://www.djangoproject.com/weblog/2021/jul/01/security-releases/', 'https://www.openwall.com/lists/oss-security/2021/07/02/2', 'https://github.com/django/django', 'https://nvd.nist.gov/vuln/detail/CVE-2021-35042', 'https://docs.djangoproject.com/en/3.2/releases/security/', 'https://security.netapp.com/advisory/ntap-20210805-0008/'} | null | {'https://github.com/django/django/commit/0bd57a879a0d54920bb9038a732645fb917040e9'} | {'https://github.com/django/django/commit/0bd57a879a0d54920bb9038a732645fb917040e9'} |
PyPI | GHSA-5hmm-x8q8-w5jh | LDAP authentication bypass with empty password | ### Impact
Users may be able to bypass LDAP authentication if they provide an empty password when Alerta server is configure to use LDAP as the authorization provider.
Only deployments where LDAP servers are configured to allow unauthenticated binds (eg. default on Active Directory) are affected.
### Patches
A fix has been implemented that returns HTTP 401 Unauthorized response for any authentication attempts where the password field is empty. See https://github.com/alerta/alerta/pull/1345
### Workarounds
LDAP administrators can disallow unauthenticated bind requests by clients.
### References
https://tools.ietf.org/html/rfc4513#section-5.1.2
https://pypi.org/project/alerta-server/8.1.0/
### For more information
If you have any questions or comments about this advisory:
* Add a comment to the issue [#1277](https://github.com/alerta/alerta/issues/1277)
* Email us at [admin@alerta.dev](mailto:admin@alerta.dev) | {'CVE-2020-26214'} | 2022-03-03T05:13:21.868248Z | 2020-11-06T17:35:49Z | HIGH | null | {'CWE-287'} | {'https://github.com/alerta/alerta/issues/1277', 'https://github.com/alerta/alerta/security/advisories/GHSA-5hmm-x8q8-w5jh', 'https://github.com/alerta/alerta/commit/2bfa31779a4c9df2fa68fa4d0c5c909698c5ef65', 'https://github.com/alerta/alerta/pull/1345', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26214', 'https://pypi.org/project/alerta-server/8.1.0/', 'https://tools.ietf.org/html/rfc4513#section-5.1.2'} | null | {'https://github.com/alerta/alerta/commit/2bfa31779a4c9df2fa68fa4d0c5c909698c5ef65'} | {'https://github.com/alerta/alerta/commit/2bfa31779a4c9df2fa68fa4d0c5c909698c5ef65'} |
PyPI | PYSEC-2021-271 | 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'} | 2021-08-27T03:22:43.879548Z | 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 | GHSA-fr77-rrx3-cp7g | Heap OOB read in `tf.ragged.cross` | ### Impact
The [shape inference code for `tf.ragged.cross`](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/ops/ragged_array_ops.cc#L64) can trigger a read outside of bounds of heap allocated array:
```python
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.RaggedCross(ragged_values=[],
ragged_row_splits=[],
sparse_indices=[[5]],
sparse_values=[],
sparse_shape=[5],
dense_inputs=[['a']],
input_order='RD',
hashed_output=False,
num_buckets=5,
hash_key=2,
out_values_type=tf.string,
out_row_splits_type=tf.int64)
return y
test()
```
### Patches
We have patched the issue in GitHub commit [fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8](https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8).
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 members of the Aivul Team from Qihoo 360. | {'CVE-2021-41212'} | 2022-03-03T05:13:40.961532Z | 2021-11-10T19:00:31Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41212', 'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} | null | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} |
PyPI | PYSEC-2021-621 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-fr77-rrx3-cp7g', 'CVE-2021-41212'} | 2021-12-09T06:35:09.206142Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g', 'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} | null | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} |
PyPI | PYSEC-2020-271 | null | In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes an invalid argument to `dlpack.to_dlpack` the expected validations will cause variables to bind to `nullptr` while setting a `status` variable to the error condition. However, this `status` argument is not properly checked. Hence, code following these methods will bind references to null pointers. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1. | {'GHSA-q8qj-fc9q-cphr', 'CVE-2020-15191'} | 2021-12-09T06:34:40.816458Z | 2020-09-25T19:15:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8qj-fc9q-cphr', 'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} |
PyPI | PYSEC-2022-106 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. 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-2022-21727', 'GHSA-c6fh-56w7-fvjw'} | 2022-03-09T00:18:23.259781Z | 2022-02-03T11:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c6fh-56w7-fvjw', 'https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L3001-L3034'} | null | {'https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b'} | {'https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b'} |
PyPI | GHSA-8p5c-f328-9fvv | Diffoscope may write to arbitrary locations due to an untrusted archive | diffoscope before 76 writes to arbitrary locations on disk based on the contents of an untrusted archive. | {'CVE-2017-0359'} | 2022-04-26T18:33:06.821118Z | 2018-07-13T16:01:21Z | CRITICAL | null | {'CWE-22'} | {'https://github.com/anthraxx/diffoscope', 'https://github.com/anthraxx/diffoscope/commit/632a40828a54b399787c25e7fa243f732aef7e05', 'https://nvd.nist.gov/vuln/detail/CVE-2017-0359', 'https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=854723', 'https://security-tracker.debian.org/tracker/CVE-2017-0359'} | null | {'https://github.com/anthraxx/diffoscope/commit/632a40828a54b399787c25e7fa243f732aef7e05'} | {'https://github.com/anthraxx/diffoscope/commit/632a40828a54b399787c25e7fa243f732aef7e05'} |
PyPI | PYSEC-2021-733 | 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'} | 2021-12-09T06:35:33.208696Z | 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-2021-363 | null | Scrapy is a high-level web crawling and scraping framework for Python. If you use `HttpAuthMiddleware` (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. 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. If you cannot upgrade, set your HTTP authentication credentials on a per-request basis, using for example the `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`. | {'GHSA-jwqp-28gf-p498', 'CVE-2021-41125'} | 2021-10-11T01:16:42.905582Z | 2021-10-06T18:15:00Z | null | null | null | {'http://doc.scrapy.org/en/latest/topics/downloader-middleware.html#module-scrapy.downloadermiddlewares.httpauth', 'https://w3lib.readthedocs.io/en/latest/w3lib.html#w3lib.http.basic_auth_header', 'https://github.com/scrapy/scrapy/commit/b01d69a1bf48060daec8f751368622352d8b85a6', 'https://github.com/scrapy/scrapy/security/advisories/GHSA-jwqp-28gf-p498'} | null | {'https://github.com/scrapy/scrapy/commit/b01d69a1bf48060daec8f751368622352d8b85a6'} | {'https://github.com/scrapy/scrapy/commit/b01d69a1bf48060daec8f751368622352d8b85a6'} |
PyPI | PYSEC-2021-699 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` is vulnerable to a division by 0. The implementation(https://github.com/tensorflow/tensorflow/blob/279bab6efa22752a2827621b7edb56a730233bd8/tensorflow/core/kernels/maxpooling_op.cc#L1033-L1034) fails to validate that the batch dimension of the tensor is non-zero, before dividing by this quantity. 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-9vpm-rcf4-9wqw', 'CVE-2021-29573'} | 2021-12-09T06:35:27.365628Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vpm-rcf4-9wqw', 'https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d'} | null | {'https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d'} | {'https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d'} |
PyPI | GHSA-3x4c-pq33-4w3q | Improper authorisation of members discloses room membership to non-members | ### Impact
Unauthorised users can access the membership (list of members, with their display names) of a room if they know the ID of the room. The vulnerability is limited to rooms with `shared` history visibility. Furthermore, the unauthorised user must be using an account on a vulnerable homeserver that is in the room.
### Patches
Server administrators should upgrade to 1.41.1 or later.
### Workarounds
Administrators of servers that use a reverse proxy could, with potentially unacceptable loss of functionality, block the following endpoints:
* `/_matrix/client/r0/rooms/{room_id}/members` with `at` query parameter
* `/_matrix/client/unstable/rooms/{room_id}/members` with `at` query parameter
### References
n/a
### For more information
If you have any questions or comments about this advisory, e-mail us at security@matrix.org. | {'CVE-2021-39164'} | 2022-03-03T05:12:37.503297Z | 2021-09-01T18:25:27Z | LOW | null | {'CWE-200'} | {'https://github.com/matrix-org/synapse/commit/cb35df940a', 'https://github.com/matrix-org/synapse', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/PXT7ID7DNBRN2TVTETU3SYQHJKEG6PXN/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-39164', 'https://github.com/matrix-org/synapse/releases/tag/v1.41.1', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-3x4c-pq33-4w3q', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2VHDEPCZ22GJFMZCWA2XZAGPOEV72POF/'} | null | {'https://github.com/matrix-org/synapse/commit/cb35df940a'} | {'https://github.com/matrix-org/synapse/commit/cb35df940a'} |
PyPI | GHSA-mqh2-9wrp-vx84 | Heap buffer overflow in `SparseSplit` | ### Impact
An attacker can cause a heap buffer overflow in `tf.raw_ops.SparseSplit`:
```python
import tensorflow as tf
shape_dims = tf.constant(0, dtype=tf.int64)
indices = tf.ones([1, 1], dtype=tf.int64)
values = tf.ones([1], dtype=tf.int64)
shape = tf.ones([1], dtype=tf.int64)
tf.raw_ops.SparseSplit(
split_dim=shape_dims, indices=indices, values=values,
shape=shape, num_split=1)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/699bff5d961f0abfde8fa3f876e6d241681fbef8/tensorflow/core/util/sparse/sparse_tensor.h#L528-L530) accesses an array element based on a user controlled offset:
```cc
const int dim = input_tensor.indices().matrix<int64>()(i, split_dim);
int slice_index = GetSliceIndex(dim, split_size, residual);
num_values[slice_index]++;
```
This results in overriding values on the heap.
### Patches
We have patched the issue in GitHub commit [8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31](https://github.com/tensorflow/tensorflow/commit/8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31).
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-2021-29558'} | 2022-03-03T05:13:00.168493Z | 2021-05-21T14:24:51Z | LOW | null | {'CWE-787'} | {'https://github.com/tensorflow/tensorflow/commit/8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29558', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mqh2-9wrp-vx84'} | null | {'https://github.com/tensorflow/tensorflow/commit/8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31'} | {'https://github.com/tensorflow/tensorflow/commit/8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31'} |
PyPI | GHSA-98p5-x8x4-c9m5 | Integer overflow in TFLite | ### Impact
An attacker can craft a TFLite model that would cause an integer overflow [in embedding lookup operations](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189):
```cc
int embedding_size = 1;
int lookup_size = 1;
for (int i = 0; i < lookup_rank - 1; i++, k++) {
const int dim = dense_shape->data.i32[i];
lookup_size *= dim;
output_shape->data[k] = dim;
}
for (int i = 1; i < embedding_rank; i++, k++) {
const int dim = SizeOfDimension(value, i);
embedding_size *= dim;
output_shape->data[k] = dim;
}
```
Both `embedding_size` and `lookup_size` are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication.
In certain scenarios, this can then result in heap OOB read/write.
### Patches
We have patched the issue in GitHub commits [f19be71717c497723ba0cea0379e84f061a75e01](https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01), [1de49725a5fc4e48f1a3b902ec3599ee99283043](https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043) and [a4e401da71458d253b05e41f28637b65baf64be4](https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4).
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 Wang Xuan of Qihoo 360 AIVul Team. | {'CVE-2022-23559'} | 2022-03-03T05:13:07.271919Z | 2022-02-09T23:52:51Z | HIGH | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23559', 'https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4', 'https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5'} | null | {'https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01', 'https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043', 'https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4'} | {'https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01', 'https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043', 'https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4'} |
PyPI | PYSEC-2021-676 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.FractionalAvgPool`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L85-L89) computes a divisor quantity by dividing two user controlled values. The user controls the values of `input_size[i]` and `pooling_ratio_[i]` (via the `value.shape()` and `pooling_ratio` arguments). If the value in `input_size[i]` is smaller than the `pooling_ratio_[i]`, then the floor operation results in `output_size[i]` being 0. The `DCHECK_GT` line is a no-op outside of debug mode, so in released versions of TF this does not trigger. Later, these computed values are used as arguments(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_avg_pool_op.cc#L96-L99) to `GeneratePoolingSequence`(https://github.com/tensorflow/tensorflow/blob/acc8ee69f5f46f92a3f1f11230f49c6ac266f10c/tensorflow/core/kernels/fractional_pool_common.cc#L100-L108). There, the first computation is a division in a modulo operation. Since `output_length` can be 0, this results in runtime crashing. 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-f78g-q7r4-9wcv', 'CVE-2021-29550'} | 2021-12-09T06:35:23.467562Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f78g-q7r4-9wcv'} | null | {'https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96'} | {'https://github.com/tensorflow/tensorflow/commit/548b5eaf23685d86f722233d8fbc21d0a4aecb96'} |
PyPI | GHSA-g4h2-gqm3-c9wq | Segfault in tf.raw_ops.ImmutableConst | ### Impact
Calling [`tf.raw_ops.ImmutableConst`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/ImmutableConst) with a `dtype` of `tf.resource` or `tf.variant` results in a segfault in the implementation as code assumes that the tensor contents are pure scalars.
```python
>>> import tensorflow as tf
>>> tf.raw_ops.ImmutableConst(dtype=tf.resource, shape=[], memory_region_name="/tmp/test.txt")
...
Segmentation fault
```
### Patches
We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
### Workarounds
If using `tf.raw_ops.ImmutableConst` in code, you can prevent the segfault by inserting a filter for the `dtype` argument.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2021-29539'} | 2022-03-03T05:13:39.768588Z | 2021-05-21T14:23:05Z | LOW | null | {'CWE-681'} | {'https://github.com/tensorflow/tensorflow/commit/4f663d4b8f0bec1b48da6fa091a7d29609980fa4', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g4h2-gqm3-c9wq', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29539'} | null | {'https://github.com/tensorflow/tensorflow/commit/4f663d4b8f0bec1b48da6fa091a7d29609980fa4'} | {'https://github.com/tensorflow/tensorflow/commit/4f663d4b8f0bec1b48da6fa091a7d29609980fa4'} |
PyPI | PYSEC-2021-226 | null | TensorFlow is an end-to-end open source platform for machine learning. The reference implementation of the `GatherNd` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/reference_ops.h#L966). An attacker can craft a model such that `params` input would be an empty tensor. In turn, `params_shape.Dims(.)` would be zero, in at least one dimension. 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-3w67-q784-6w7c', 'CVE-2021-29589'} | 2021-08-27T03:22:37.235055Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3w67-q784-6w7c', 'https://github.com/tensorflow/tensorflow/commit/8e45822aa0b9f5df4b4c64f221e64dc930a70a9d'} | null | {'https://github.com/tensorflow/tensorflow/commit/8e45822aa0b9f5df4b4c64f221e64dc930a70a9d'} | {'https://github.com/tensorflow/tensorflow/commit/8e45822aa0b9f5df4b4c64f221e64dc930a70a9d'} |
PyPI | PYSEC-2021-251 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the `fixed_length` value to the size of the type argument. The `fixed_length` argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the `out_data` pointer by `fixed_length * sizeof(T)` bytes whereas it only copied at most `fixed_length` bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory. 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-8pmx-p244-g88h', 'CVE-2021-29614'} | 2021-08-27T03:22:41.712204Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8pmx-p244-g88h'} | null | {'https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d'} | {'https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d'} |
PyPI | PYSEC-2021-601 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. We have patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c. 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-vcjj-9vg7-vf68', 'CVE-2021-37688'} | 2021-12-09T06:35:06.517637Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vcjj-9vg7-vf68', 'https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c'} | null | {'https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c'} | {'https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c'} |
PyPI | GHSA-mv78-g7wq-mhp4 | Division by zero in padding computation in TFLite | ### Impact
The TFLite computation for size of output after padding, [`ComputeOutSize`](https://github.com/tensorflow/tensorflow/blob/0c9692ae7b1671c983569e5d3de5565843d500cf/tensorflow/lite/kernels/padding.h#L43-L55), does not check that the `stride` argument is not 0 before doing the division.
```cc
inline int ComputeOutSize(TfLitePadding padding, int image_size,
int filter_size, int stride, int dilation_rate = 1) {
int effective_filter_size = (filter_size - 1) * dilation_rate + 1;
switch (padding) {
case kTfLitePaddingSame:
return (image_size + stride - 1) / stride;
case kTfLitePaddingValid:
return (image_size + stride - effective_filter_size) / stride;
default:
return 0;
}
}
```
Users can craft special models such that `ComputeOutSize` is called with `stride` set to 0.
### Patches
We have patched the issue in GitHub commit [49847ae69a4e1a97ae7f2db5e217c77721e37948](https://github.com/tensorflow/tensorflow/commit/49847ae69a4e1a97ae7f2db5e217c77721e37948).
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-2021-29585'} | 2022-03-03T05:12:56.224293Z | 2021-05-21T14:26:41Z | LOW | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29585', 'https://github.com/tensorflow/tensorflow/commit/49847ae69a4e1a97ae7f2db5e217c77721e37948', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv78-g7wq-mhp4'} | null | {'https://github.com/tensorflow/tensorflow/commit/49847ae69a4e1a97ae7f2db5e217c77721e37948'} | {'https://github.com/tensorflow/tensorflow/commit/49847ae69a4e1a97ae7f2db5e217c77721e37948'} |
PyPI | GHSA-9vg3-cf92-h2h7 | Insufficient Verification of Data Authenticity in python-keystoneclient | python-keystoneclient version 0.2.3 to 0.2.5 has middleware memcache signing bypass | {'CVE-2013-2167'} | 2022-03-23T20:00:10.830873Z | 2020-03-10T20:39:05Z | CRITICAL | null | {'CWE-345'} | {'https://security-tracker.debian.org/tracker/CVE-2013-2167', 'http://www.openwall.com/lists/oss-security/2013/06/19/5', 'https://nvd.nist.gov/vuln/detail/CVE-2013-2167', 'http://lists.fedoraproject.org/pipermail/package-announce/2013-August/113944.html', 'https://bugs.gentoo.org/show_bug.cgi?id=CVE-2013-2167', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/85492', 'http://www.securityfocus.com/bid/60680', 'https://github.com/openstack/python-keystoneclient/commits/0.3.0', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2013-2167', 'http://rhn.redhat.com/errata/RHSA-2013-0992.html', 'https://github.com/openstack/python-keystoneclient/commit/eeefb784f24c37d5f56a421e1ccc911cace9385e', 'https://access.redhat.com/security/cve/cve-2013-2167'} | null | {'https://github.com/openstack/python-keystoneclient/commit/eeefb784f24c37d5f56a421e1ccc911cace9385e'} | {'https://github.com/openstack/python-keystoneclient/commit/eeefb784f24c37d5f56a421e1ccc911cace9385e'} |
PyPI | PYSEC-2020-314 | null | In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the `splits` tensor has the minimum required number of elements. Code uses this quantity to initialize a different data structure. Since `BatchedMap` is equivalent to a vector, it needs to have at least one element to not be `nullptr`. If user passes a `splits` tensor that is empty or has exactly one element, we get a `SIGABRT` signal raised by the operating system. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'GHSA-x5cp-9pcf-pp3h', 'CVE-2020-15199'} | 2021-12-09T06:35:13.149576Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x5cp-9pcf-pp3h', 'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', '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-86vp-x3pr-79rx | Cross-site scripting in Apache airflow | The "origin" parameter passed to some of the endpoints like '/trigger' was vulnerable to XSS exploit. This issue affects Apache Airflow versions prior to 1.10.14. This is same as CVE-2020-13944 but the implemented fix in Airflow 1.10.13 did not fix the issue completely. | {'CVE-2020-17515'} | 2022-03-03T05:12:26.343329Z | 2021-04-20T16:40:14Z | MODERATE | null | {'CWE-79'} | {'https://github.com/apache/airflow/commit/409c249121bd9c8902fc2ba551b21873ab41f953', 'https://lists.apache.org/thread.html/r2892ef594dbbf54d0939b808626f52f7c2d1584f8aa1d81570847d2a@%3Cannounce.apache.org%3E', 'https://github.com/apache/airflow/releases/tag/2.0.2', 'https://lists.apache.org/thread.html/r4656959c8ed06c1f6202d89aa4e67b35ad7bdba5a666caff3fea888e%40%3Cusers.airflow.apache.org%3E', 'https://github.com/apache/airflow/pull/14738', 'http://www.openwall.com/lists/oss-security/2021/05/01/2', 'https://lists.apache.org/thread.html/ra8ce70088ba291f358e077cafdb14d174b7a1ce9a9d86d1b332d6367@%3Cusers.airflow.apache.org%3E', 'http://www.openwall.com/lists/oss-security/2020/12/11/2', 'https://lists.apache.org/thread.html/r2892ef594dbbf54d0939b808626f52f7c2d1584f8aa1d81570847d2a@%3Cdev.airflow.apache.org%3E', 'https://lists.apache.org/thread.html/rc005f4de9d9b0ba943ceb8ff5a21a5c6ff8a9df52632476698d99432@%3Cannounce.apache.org%3E', 'https://lists.apache.org/thread.html/r2892ef594dbbf54d0939b808626f52f7c2d1584f8aa1d81570847d2a@%3Cusers.airflow.apache.org%3E', 'https://nvd.nist.gov/vuln/detail/CVE-2020-17515', 'https://pypi.org/project/apache-airflow', 'https://lists.apache.org/thread.html/r4656959c8ed06c1f6202d89aa4e67b35ad7bdba5a666caff3fea888e@%3Cusers.airflow.apache.org%3E', 'https://github.com/apache/airflow/releases/tag/1.10.15'} | null | {'https://github.com/apache/airflow/commit/409c249121bd9c8902fc2ba551b21873ab41f953'} | {'https://github.com/apache/airflow/commit/409c249121bd9c8902fc2ba551b21873ab41f953'} |
PyPI | GHSA-6m9g-jr8c-cqw3 | Depth counting error in guard() leading to multiple potential security issues in aioxmpp | ### Impact
Possible remote Denial of Service or Data Injection.
### Patches
Patches are available in https://github.com/horazont/aioxmpp/pull/268. They have been backported to the 0.10 release series and 0.10.3 is the first release to contain the fix.
### Workarounds
To make the bug exploitable, an error suppressing ``xso_error_handler`` is required. By not using ``xso_error_handlers`` or not using the suppression function, the vulnerability can be mitigated completely (to our knowledge).
### References
The pull request contains a detailed description: https://github.com/horazont/aioxmpp/pull/268
### For more information
If you have any questions or comments about this advisory:
* [Join our chat](xmpp:aioxmpp@conference.zombofant.net?join)
* Email the maintainer [Jonas Schäfer](mailto:jonas@wielicki.name) | {'CVE-2019-1000007'} | 2022-03-03T05:14:02.836006Z | 2020-04-29T17:12:39Z | HIGH | null | {'CWE-237'} | {'https://github.com/horazont/aioxmpp/commit/29ff0838a40f58efe30a4bbcea95aa8dab7da475', 'https://github.com/horazont/aioxmpp/pull/268', 'https://nvd.nist.gov/vuln/detail/CVE-2019-1000007', 'https://github.com/horazont/aioxmpp/commit/f151f920f439d97d4103fc11057ed6dc34fe98be', 'https://github.com/horazont/aioxmpp/security/advisories/GHSA-6m9g-jr8c-cqw3'} | null | {'https://github.com/horazont/aioxmpp/commit/29ff0838a40f58efe30a4bbcea95aa8dab7da475', 'https://github.com/horazont/aioxmpp/commit/f151f920f439d97d4103fc11057ed6dc34fe98be'} | {'https://github.com/horazont/aioxmpp/commit/29ff0838a40f58efe30a4bbcea95aa8dab7da475', 'https://github.com/horazont/aioxmpp/commit/f151f920f439d97d4103fc11057ed6dc34fe98be'} |
PyPI | PYSEC-2021-314 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions under certain conditions, Go code can trigger a segfault in string deallocation. For string tensors, `C.TF_TString_Dealloc` is called during garbage collection within a finalizer function. However, tensor structure isn't checked until encoding to avoid a performance penalty. The current method for dealloc assumes that encoding succeeded, but segfaults when a string tensor is garbage collected whose encoding failed (e.g., due to mismatched dimensions). To fix this, the call to set the finalizer function is deferred until `NewTensor` returns and, if encoding failed for a string tensor, deallocs are determined based on bytes written. We have patched the issue in GitHub commit 8721ba96e5760c229217b594f6d2ba332beedf22. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, which is the other affected version. | {'CVE-2021-37692', 'GHSA-cmgw-8vpc-rc59'} | 2021-08-27T03:22:47.865620Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cmgw-8vpc-rc59', 'https://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22', 'https://github.com/tensorflow/tensorflow/pull/50508'} | null | {'https://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22'} | {'https://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22'} |
PyPI | PYSEC-2021-744 | null | TensorFlow is an end-to-end open source platform for machine learning. Passing a complex argument to `tf.transpose` at the same time as passing `conjugate=True` argument results in a crash. 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-xqfj-cr6q-pc8w', 'CVE-2021-29618'} | 2021-12-09T06:35:35.049527Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/issues/46973', 'https://github.com/tensorflow/tensorflow/commit/1dc6a7ce6e0b3e27a7ae650bfc05b195ca793f88', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xqfj-cr6q-pc8w', 'https://github.com/tensorflow/issues/42105'} | null | {'https://github.com/tensorflow/tensorflow/commit/1dc6a7ce6e0b3e27a7ae650bfc05b195ca793f88'} | {'https://github.com/tensorflow/tensorflow/commit/1dc6a7ce6e0b3e27a7ae650bfc05b195ca793f88'} |
PyPI | PYSEC-2021-197 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel. Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when `parent_output_index` is shorter than `row_split`. 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-8gv3-57p6-g35r', 'CVE-2021-29560'} | 2021-08-27T03:22:32.127822Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8gv3-57p6-g35r'} | null | {'https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121'} | {'https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121'} |
PyPI | GHSA-3c5c-7235-994j | Moderate severity vulnerability that affects Pillow | Buffer overflow in the ImagingPcdDecode function in PcdDecode.c in Pillow before 3.1.1 and Python Imaging Library (PIL) 1.1.7 and earlier allows remote attackers to cause a denial of service (crash) via a crafted PhotoCD file. | {'CVE-2016-2533'} | 2022-03-03T05:14:10.628337Z | 2018-07-24T20:15:13Z | MODERATE | null | {'CWE-119'} | {'https://github.com/python-pillow/Pillow/commit/5bdf54b5a76b54fb00bd05f2d733e0a4173eefc9#diff-8ff6909c159597e22288ad818938fd6b', 'https://github.com/python-pillow/Pillow', 'https://github.com/advisories/GHSA-3c5c-7235-994j', 'https://github.com/python-pillow/Pillow/pull/1706', 'http://www.oracle.com/technetwork/topics/security/bulletinjan2016-2867206.html', 'https://nvd.nist.gov/vuln/detail/CVE-2016-2533', 'http://www.openwall.com/lists/oss-security/2016/02/02/5', 'https://security.gentoo.org/glsa/201612-52', 'https://github.com/python-pillow/Pillow/commit/ae453aa18b66af54e7ff716f4ccb33adca60afd4#diff-8ff6909c159597e22288ad818938fd6b', 'https://github.com/python-pillow/Pillow/blob/c3cb690fed5d4bf0c45576759de55d054916c165/CHANGES.rst', 'http://www.debian.org/security/2016/dsa-3499', 'http://www.openwall.com/lists/oss-security/2016/02/22/2'} | null | {'https://github.com/python-pillow/Pillow/commit/5bdf54b5a76b54fb00bd05f2d733e0a4173eefc9#diff-8ff6909c159597e22288ad818938fd6b', 'https://github.com/python-pillow/Pillow/commit/ae453aa18b66af54e7ff716f4ccb33adca60afd4#diff-8ff6909c159597e22288ad818938fd6b'} | {'https://github.com/python-pillow/Pillow/commit/ae453aa18b66af54e7ff716f4ccb33adca60afd4#diff-8ff6909c159597e22288ad818938fd6b', 'https://github.com/python-pillow/Pillow/commit/5bdf54b5a76b54fb00bd05f2d733e0a4173eefc9#diff-8ff6909c159597e22288ad818938fd6b'} |
PyPI | PYSEC-2021-528 | 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'} | 2021-12-09T06:34:58.930299Z | 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 | PYSEC-2021-178 | 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'} | 2021-08-27T03:22:28.768951Z | 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 | PYSEC-2021-482 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division. Since `data` is given by the `values` argument, `num_batch_elements` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected. | {'GHSA-qg48-85hg-mqc5', 'CVE-2021-29554'} | 2021-12-09T06:34:51.766391Z | 2021-05-14T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qg48-85hg-mqc5', 'https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f'} | null | {'https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f'} | {'https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f'} |
PyPI | PYSEC-2020-178 | null | Waitress through version 1.3.1 allows request smuggling by sending the Content-Length header twice. Waitress would header fold a double Content-Length header and due to being unable to cast the now comma separated value to an integer would set the Content-Length to 0 internally. If two Content-Length headers are sent in a single request, Waitress would treat the request as having no body, thereby treating the body of the request as a new request in HTTP pipelining. This issue is fixed in Waitress 1.4.0. | {'CVE-2019-16792', 'GHSA-4ppp-gpcr-7qf6'} | 2020-01-30T17:07:00Z | 2020-01-22T19:15:00Z | null | null | null | {'https://github.com/Pylons/waitress/commit/575994cd42e83fd772a5f7ec98b2c56751bd3f65', 'https://github.com/Pylons/waitress/security/advisories/GHSA-4ppp-gpcr-7qf6', 'https://docs.pylonsproject.org/projects/waitress/en/latest/#security-fixes'} | null | {'https://github.com/Pylons/waitress/commit/575994cd42e83fd772a5f7ec98b2c56751bd3f65'} | {'https://github.com/Pylons/waitress/commit/575994cd42e83fd772a5f7ec98b2c56751bd3f65'} |
PyPI | PYSEC-2021-19 | null | An XSS vulnerability was discovered in python-lxml's clean module versions before 4.6.3. When disabling the safe_attrs_only and forms arguments, the Cleaner class does not remove the formaction attribute allowing for JS to bypass the sanitizer. A remote attacker could exploit this flaw to run arbitrary JS code on users who interact with incorrectly sanitized HTML. This issue is patched in lxml 4.6.3. | {'CVE-2021-28957', 'GHSA-jq4v-f5q6-mjqq'} | 2021-03-30T18:47:00Z | 2021-03-21T05:15:00Z | null | null | null | {'https://lists.debian.org/debian-lts-announce/2021/03/msg00031.html', 'https://bugs.launchpad.net/lxml/+bug/1888153', 'https://github.com/lxml/lxml/pull/316/commits/10ec1b4e9f93713513a3264ed6158af22492f270', 'https://github.com/advisories/GHSA-jq4v-f5q6-mjqq', 'https://www.debian.org/security/2021/dsa-4880', 'https://github.com/lxml/lxml/commit/a5f9cb52079dc57477c460dbe6ba0f775e14a999'} | null | {'https://github.com/lxml/lxml/commit/a5f9cb52079dc57477c460dbe6ba0f775e14a999', 'https://github.com/lxml/lxml/pull/316/commits/10ec1b4e9f93713513a3264ed6158af22492f270'} | {'https://github.com/lxml/lxml/commit/a5f9cb52079dc57477c460dbe6ba0f775e14a999', 'https://github.com/lxml/lxml/pull/316/commits/10ec1b4e9f93713513a3264ed6158af22492f270'} |
PyPI | PYSEC-2021-656 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments. Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since `OP_REQUIRES`(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function. Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`. 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-xcwj-wfcm-m23c', 'CVE-2021-29530'} | 2021-12-09T06:35:19.918878Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xcwj-wfcm-m23c', 'https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd'} | null | {'https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd'} | {'https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd'} |
PyPI | PYSEC-2021-206 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/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. 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. 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-2021-29569', 'GHSA-3h8m-483j-7xxm'} | 2021-08-27T03:22:33.683964Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm', 'https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e'} | null | {'https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e'} | {'https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e'} |
PyPI | GHSA-rmp7-f2vp-3rq4 | Cross-site scripting in SiCKRAGE | in SiCKRAGE, versions 4.2.0 to 10.0.11.dev1 are vulnerable to Stored Cross-Site-Scripting (XSS) due to user input not being validated properly when processed by the server. Therefore, an attacker can inject arbitrary JavaScript code inside the application, and possibly steal a user’s sensitive information. | {'CVE-2021-25925'} | 2022-03-03T05:13:35.635168Z | 2021-04-20T16:31:54Z | MODERATE | null | {'CWE-79'} | {'https://github.com/SiCKRAGE/SiCKRAGE/commit/9f42426727e16609ad3d1337f6637588b8ed28e4', 'https://nvd.nist.gov/vuln/detail/CVE-2021-25925', 'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25925'} | null | {'https://github.com/SiCKRAGE/SiCKRAGE/commit/9f42426727e16609ad3d1337f6637588b8ed28e4'} | {'https://github.com/SiCKRAGE/SiCKRAGE/commit/9f42426727e16609ad3d1337f6637588b8ed28e4'} |
PyPI | PYSEC-2021-713 | null | TensorFlow is an end-to-end open source platform for machine learning. The `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division(https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be zero. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29587', 'GHSA-j7rm-8ww4-xx2g'} | 2021-12-09T06:35:29.871064Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j7rm-8ww4-xx2g', 'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7'} | null | {'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7'} | {'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7'} |
PyPI | GHSA-6pc9-xqrg-wfqw | Exposure of Sensitive information in httpie | httpie is a modern, user-friendly command-line HTTP client for the API era. Prior to version 3.1.0, all cookies saved to session storage are supercookies. At this time, there is no known workaround. Users are recommended to update to version 3.1.0. | {'CVE-2022-0430'} | 2022-04-05T19:00:40.328192Z | 2022-03-16T00:00:47Z | LOW | null | {'CWE-200'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-0430', 'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b', 'https://huntr.dev/bounties/dafb2e4f-c6b6-4768-8ef5-b396cd6a801f', 'https://github.com/httpie/httpie', 'https://github.com/pypa/advisory-database/tree/main/vulns/httpie/PYSEC-2022-167.yaml'} | null | {'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b'} | {'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b'} |
PyPI | PYSEC-2021-590 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. 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-2021-37677', 'GHSA-qfpc-5pjr-mh26'} | 2021-12-09T06:35:05.572307Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qfpc-5pjr-mh26', 'https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764'} | null | {'https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764'} | {'https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764'} |
PyPI | PYSEC-2022-126 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `Range` suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations. 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-qx3f-p745-w4hr', 'CVE-2022-23562'} | 2022-03-09T00:18:25.916972Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/52676', 'https://github.com/tensorflow/tensorflow/pull/51733', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qx3f-p745-w4hr', 'https://github.com/tensorflow/tensorflow/commit/f0147751fd5d2ff23251149ebad9af9f03010732'} | null | {'https://github.com/tensorflow/tensorflow/commit/f0147751fd5d2ff23251149ebad9af9f03010732'} | {'https://github.com/tensorflow/tensorflow/commit/f0147751fd5d2ff23251149ebad9af9f03010732'} |
PyPI | GHSA-3qxp-qjq7-w4hf | CHECK-fail in tf.raw_ops.EncodePng | ### Impact
An attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data:
```python
import tensorflow as tf
image = tf.zeros([0, 0, 3])
image = tf.cast(image, dtype=tf.uint8)
tf.raw_ops.EncodePng(image=image)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when [calling `png::WriteImageToBuffer`](https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the [first line of `png::WriteImageToBuffer`](https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349).
```cc
template <typename T>
bool WriteImageToBuffer(
const void* image, int width, int height, int row_bytes, int num_channels,
int channel_bits, int compression, T* png_string,
const std::vector<std::pair<std::string, std::string> >* metadata) {
CHECK_NOTNULL(image);
...
}
```
Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack.
### Patches
We have patched the issue in GitHub commit [26eb323554ffccd173e8a79a8c05c15b685ae4d1](https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1).
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-2021-29531'} | 2022-03-03T05:14:18.316115Z | 2021-05-21T14:22:13Z | LOW | null | {'CWE-754'} | {'https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29531', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3qxp-qjq7-w4hf'} | null | {'https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1'} | {'https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1'} |
PyPI | PYSEC-2021-210 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` is vulnerable to a division by 0. The implementation(https://github.com/tensorflow/tensorflow/blob/279bab6efa22752a2827621b7edb56a730233bd8/tensorflow/core/kernels/maxpooling_op.cc#L1033-L1034) fails to validate that the batch dimension of the tensor is non-zero, before dividing by this quantity. 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-9vpm-rcf4-9wqw', 'CVE-2021-29573'} | 2021-08-27T03:22:34.367051Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vpm-rcf4-9wqw', 'https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d'} | null | {'https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d'} | {'https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d'} |
PyPI | PYSEC-2021-640 | null | TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L446). Before the `for` loop, `batch_idx` is set to 0. The attacker sets `splits(0)` to be 7, hence the `while` loop does not execute and `batch_idx` remains 0. This then results in writing to `out(-1, bin)`, which is before the heap allocated buffer for the output tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected. | {'CVE-2021-29514', 'GHSA-8h46-5m9h-7553'} | 2021-12-09T06:35:17.368785Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8h46-5m9h-7553'} | null | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} |
PyPI | GHSA-r38r-qp28-2m63 | Critical severity vulnerability that affects rope | base/oi/doa.py in the Rope library in CPython (aka Python) allows remote attackers to execute arbitrary code by leveraging an unsafe call to pickle.load. | {'CVE-2014-3539'} | 2022-03-23T21:00:09.517202Z | 2018-07-26T16:08:49Z | CRITICAL | null | null | {'https://github.com/python-rope/rope', 'http://www.openwall.com/lists/oss-security/2015/02/07/1', 'https://nvd.nist.gov/vuln/detail/CVE-2014-3539', 'https://github.com/python-rope/rope/commit/b01da7aab5cd02129941d2a900e6e5e3b5f7d4fb', 'https://bugzilla.redhat.com/show_bug.cgi?id=1116485'} | null | {'https://github.com/python-rope/rope/commit/b01da7aab5cd02129941d2a900e6e5e3b5f7d4fb'} | {'https://github.com/python-rope/rope/commit/b01da7aab5cd02129941d2a900e6e5e3b5f7d4fb'} |
PyPI | GHSA-fcwc-p4fc-c5cc | Null pointer dereference in `MatrixDiagPartOp` | ### Impact
If a user does not provide a valid padding value to `tf.raw_ops.MatrixDiagPartOp`, then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first:
```python
import tensorflow as tf
tf.raw_ops.MatrixDiagPartV2(
input=tf.ones(2,dtype=tf.int32),
k=tf.ones(2,dtype=tf.int32),
padding_value=[])
```
Although this example is given for `MatrixDiagPartV2`, all versions of the operation are affected.
The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from.
### Patches
We have patched the issue in GitHub commit [482da92095c4d48f8784b1f00dda4f81c28d2988](https://github.com/tensorflow/tensorflow/commit/482da92095c4d48f8784b1f00dda4f81c28d2988).
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-2021-37643'} | 2022-03-03T05:13:40.355999Z | 2021-08-25T14:43:42Z | HIGH | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37643', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fcwc-p4fc-c5cc', 'https://github.com/tensorflow/tensorflow/commit/482da92095c4d48f8784b1f00dda4f81c28d2988'} | null | {'https://github.com/tensorflow/tensorflow/commit/482da92095c4d48f8784b1f00dda4f81c28d2988'} | {'https://github.com/tensorflow/tensorflow/commit/482da92095c4d48f8784b1f00dda4f81c28d2988'} |
PyPI | PYSEC-2017-78 | null | An exploitable vulnerability exists in the YAML parsing functionality in config.py in Confire 0.2.0. Due to the user-specific configuration being loaded from "~/.confire.yaml" using the yaml.load function, a YAML parser can execute arbitrary Python commands resulting in command execution. An attacker can insert Python into loaded YAML to trigger this vulnerability. | {'GHSA-m85c-9mf8-m2m6', 'CVE-2017-16763'} | 2021-08-25T04:29:57.582065Z | 2017-11-10T09:29:00Z | null | null | null | {'https://github.com/bbengfort/confire/commit/8cc86a5ec2327e070f1d576d61bbaadf861597ea', 'https://github.com/advisories/GHSA-m85c-9mf8-m2m6', 'https://joel-malwarebenchmark.github.io/blog/2017/11/12/cve-2017-16763-configure-loaded-through-confire/', 'https://github.com/bbengfort/confire/issues/24'} | null | {'https://github.com/bbengfort/confire/commit/8cc86a5ec2327e070f1d576d61bbaadf861597ea'} | {'https://github.com/bbengfort/confire/commit/8cc86a5ec2327e070f1d576d61bbaadf861597ea'} |
PyPI | GHSA-962m-m8jw-8wrr | Path Traversal in Zope | Zope is an open-source web application server. In Zope versions prior to 4.6 and 5.2, users can access untrusted modules indirectly through Python modules that are available for direct use. By default, only users with the Manager role can add or edit Zope Page Templates through the web, but sites that allow untrusted users to add/edit Zope Page Templates through the web are at risk from this vulnerability. The problem has been fixed in Zope 5.2 and 4.6. As a workaround, 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. | {'CVE-2021-32633'} | 2022-03-03T05:13:58.513548Z | 2021-06-15T16:10:49Z | HIGH | null | {'CWE-22'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-32633', 'http://www.openwall.com/lists/oss-security/2021/05/22/1', 'https://github.com/zopefoundation/Zope', 'https://cyllective.com/blog/post/plone-authenticated-rce-cve-2021-32633/', 'http://www.openwall.com/lists/oss-security/2021/05/21/1', 'https://github.com/zopefoundation/Zope/security/advisories/GHSA-5pr9-v234-jw36', 'https://github.com/zopefoundation/Zope/commit/1f8456bf1f908ea46012537d52bd7e752a532c91'} | null | {'https://github.com/zopefoundation/Zope/commit/1f8456bf1f908ea46012537d52bd7e752a532c91'} | {'https://github.com/zopefoundation/Zope/commit/1f8456bf1f908ea46012537d52bd7e752a532c91'} |
PyPI | PYSEC-2021-355 | null | “Shuup” application in versions 0.4.2 to 2.10.8 is affected by the “Formula Injection” vulnerability. A customer can inject payloads in the name input field in the billing address while buying a product. When a store administrator accesses the reports page to export the data as an Excel file and opens it, the payload gets executed. | {'GHSA-663j-rjcr-789f', 'CVE-2021-25962'} | 2021-09-30T23:26:29.598032Z | 2021-09-29T14:15:00Z | null | null | null | {'https://github.com/shuup/shuup/commit/0a2db392e8518410c282412561461cd8797eea51', 'https://github.com/advisories/GHSA-663j-rjcr-789f', 'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25962'} | null | {'https://github.com/shuup/shuup/commit/0a2db392e8518410c282412561461cd8797eea51'} | {'https://github.com/shuup/shuup/commit/0a2db392e8518410c282412561461cd8797eea51'} |
PyPI | GHSA-vq36-27g6-p492 | Out of bounds read in Tensorflow | ### Impact
TensorFlow's [type inference](https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/core/graph/graph.cc#L223-L229) can cause a heap OOB read as the bounds checking is done in a `DCHECK` (which is a no-op during production):
```cc
if (node_t.type_id() != TFT_UNSET) {
int ix = input_idx[i];
DCHECK(ix < node_t.args_size())
<< "input " << i << " should have an output " << ix
<< " but instead only has " << node_t.args_size()
<< " outputs: " << node_t.DebugString();
input_types.emplace_back(node_t.args(ix));
// ...
}
```
An attacker can control `input_idx` such that `ix` would be larger than the number of values in `node_t.args`.
### Patches
We have patched the issue in GitHub commit [c99d98cd189839dcf51aee94e7437b54b31f8abd](https://github.com/tensorflow/tensorflow/commit/c99d98cd189839dcf51aee94e7437b54b31f8abd).
The fix will be included in TensorFlow 2.8.0. This is the only affected version.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2022-23592'} | 2022-03-28T19:45:06.569091Z | 2022-02-09T23:31:48Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/commit/c99d98cd189839dcf51aee94e7437b54b31f8abd', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/core/graph/graph.cc#L223-L229', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23592', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vq36-27g6-p492'} | null | {'https://github.com/tensorflow/tensorflow/commit/c99d98cd189839dcf51aee94e7437b54b31f8abd'} | {'https://github.com/tensorflow/tensorflow/commit/c99d98cd189839dcf51aee94e7437b54b31f8abd'} |
PyPI | GHSA-q2q7-5pp4-w6pg | Catastrophic backtracking in URL authority parser when passed URL containing many @ characters | ### Impact
When provided with a URL containing many `@` characters in the authority component the authority regular expression exhibits catastrophic backtracking causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
### Patches
The issue has been fixed in urllib3 v1.26.5.
### References
- [CVE-2021-33503](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-33503)
- [JVNVU#92413403 (English)](https://jvn.jp/en/vu/JVNVU92413403/)
- [JVNVU#92413403 (Japanese)](https://jvn.jp/vu/JVNVU92413403/)
- [urllib3 v1.26.5](https://github.com/urllib3/urllib3/releases/tag/1.26.5)
### For more information
If you have any questions or comments about this advisory:
* Ask in our [community Discord](https://discord.gg/urllib3)
* Email [sethmichaellarson@gmail.com](mailto:sethmichaellarson@gmail.com)
| {'CVE-2021-33503'} | 2022-03-03T05:13:06.285390Z | 2021-06-01T21:19:32Z | HIGH | null | {'CWE-400'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/FMUGWEAUYGGHTPPXT6YBD53WYXQGVV73/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-33503', 'https://github.com/advisories/GHSA-q2q7-5pp4-w6pg', 'https://github.com/urllib3/urllib3', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/6SCV7ZNAHS3E6PBFLJGENCDRDRWRZZ6W/', 'https://github.com/urllib3/urllib3/commit/2d4a3fee6de2fa45eb82169361918f759269b4ec', 'https://www.oracle.com/security-alerts/cpuoct2021.html', 'https://security.gentoo.org/glsa/202107-36', 'https://github.com/urllib3/urllib3/security/advisories/GHSA-q2q7-5pp4-w6pg'} | null | {'https://github.com/urllib3/urllib3/commit/2d4a3fee6de2fa45eb82169361918f759269b4ec'} | {'https://github.com/urllib3/urllib3/commit/2d4a3fee6de2fa45eb82169361918f759269b4ec'} |
PyPI | PYSEC-2021-705 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid. Whereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access. 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-79fv-9865-4qcv', 'CVE-2021-29579'} | 2021-12-09T06:35:28.365362Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-79fv-9865-4qcv', 'https://github.com/tensorflow/tensorflow/commit/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7'} | null | {'https://github.com/tensorflow/tensorflow/commit/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7'} | {'https://github.com/tensorflow/tensorflow/commit/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7'} |
PyPI | PYSEC-2022-130 | 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'} | 2022-03-09T00:18:26.438942Z | 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-2021-586 | 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'} | 2021-12-09T06:35:05.237190Z | 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 | PYSEC-2021-569 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToSparse`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/ragged_tensor_to_sparse_kernel.cc#L30) has an incomplete validation of the splits values: it does not check that they are in increasing order. We have patched the issue in GitHub commit 1071f554dbd09f7e101324d366eec5f4fe5a3ece. 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-4xfp-4pfp-89wg', 'CVE-2021-37656'} | 2021-12-09T06:35:03.759832Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4xfp-4pfp-89wg'} | null | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece'} | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece'} |
PyPI | GHSA-hwv5-w8gm-fq9f | Directory Traversal vulnerability in GET/PUT allows attackers to Disclose Information or Write Files via a crafted GET/PUT request | ### Impact
#### Information Disclosure
When the GET method is attacked, attackers can read files which have a `.data` suffix and which are accompanied by a JSON file with the `.meta` suffix. This can lead to Information Disclosure and in some shared-hosting scenarios also to circumvention of authentication or other limitations on the outbound (GET) traffic.
For example, in a scenario where a single server has multiple instances of the application running (with separate DATA_ROOT settings), an attacker who has knowledge about the directory structure is able to read files from any other instance to which the process has read access.
If instances have individual authentication (for example, HTTP authentication via a reverse proxy, source IP based filtering) or other restrictions (such as quotas), attackers may circumvent those limits in such a scenario by using the Directory Traversal to retrieve data from the other instances.
#### File Write
If the associated XMPP server (or anyone knowing the SECRET_KEY) is malicious, they can write files outside the DATA_ROOT. The files which are written are constrained to have the `.meta` and the `.data` suffixes; the `.meta` file will contain the JSON with the Content-Type of the original request and the `.data` file will contain the payload.
### Patches
PR #12 fixes the issue. The PR has been merged into version 0.4.0 and 0.4.0 has been released and pushed to PyPI. Users are advised to upgrade immediately.
### Workarounds
- Apache can apparently be configured to filter such malicious paths when reverse-proxying.
- There are no other workarounds known.
### References
- [Pull Request #12](https://github.com/horazont/xmpp-http-upload/pull/12) | {'CVE-2020-15239'} | 2022-03-03T05:13:07.970107Z | 2020-10-06T18:21:02Z | LOW | null | {'CWE-22'} | {'https://github.com/horazont/xmpp-http-upload/security/advisories/GHSA-hwv5-w8gm-fq9f', 'https://github.com/horazont/xmpp-http-upload/pull/12', 'https://github.com/horazont/xmpp-http-upload/commit/82056540191e89f0cd697c81f57714c00962ed75', 'https://pypi.org/project/xmpp-http-upload/#history', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15239'} | null | {'https://github.com/horazont/xmpp-http-upload/commit/82056540191e89f0cd697c81f57714c00962ed75'} | {'https://github.com/horazont/xmpp-http-upload/commit/82056540191e89f0cd697c81f57714c00962ed75'} |
PyPI | PYSEC-2012-16 | null | PyCrypto before 2.6 does not produce appropriate prime numbers when using an ElGamal scheme to generate a key, which reduces the signature space or public key space and makes it easier for attackers to conduct brute force attacks to obtain the private key. | {'CVE-2012-2417'} | 2021-08-27T03:22:16.601238Z | 2012-06-17T03:41:00Z | null | null | null | {'http://lists.fedoraproject.org/pipermail/package-announce/2012-June/081789.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2012-June/081759.html', 'https://github.com/dlitz/pycrypto/blob/373ea760f21701b162e8c4912a66928ee30d401a/ChangeLog', 'http://secunia.com/advisories/49263', 'http://www.debian.org/security/2012/dsa-2502', 'https://bugs.launchpad.net/pycrypto/+bug/985164', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/75871', 'https://hermes.opensuse.org/messages/15083589', 'http://www.osvdb.org/82279', 'http://www.securityfocus.com/bid/53687', 'http://www.mandriva.com/security/advisories?name=MDVSA-2012:117', 'https://github.com/Legrandin/pycrypto/commit/9f912f13df99ad3421eff360d6a62d7dbec755c2', 'http://lists.fedoraproject.org/pipermail/package-announce/2012-June/081713.html', 'http://www.openwall.com/lists/oss-security/2012/05/25/1'} | null | {'https://github.com/Legrandin/pycrypto/commit/9f912f13df99ad3421eff360d6a62d7dbec755c2'} | {'https://github.com/Legrandin/pycrypto/commit/9f912f13df99ad3421eff360d6a62d7dbec755c2'} |
PyPI | PYSEC-2020-139 | null | In Tensorflow before version 2.4.0, when the `boxes` argument of `tf.image.crop_and_resize` has a very large value, the CPU kernel implementation receives it as a C++ `nan` floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault. 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-xwhf-g6j5-j5gc', 'CVE-2020-15266'} | 2021-09-01T08:19:35.637564Z | 2020-10-21T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/42129', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc', 'https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845'} | null | {'https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845'} | {'https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845'} |
PyPI | GHSA-24x4-6qmh-88qg | Use after free in `DecodePng` kernel | ### Impact
A malicious user can cause a use after free behavior when [decoding PNG images](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/image/decode_image_op.cc#L339-L346):
```cc
if (/* ... error conditions ... */) {
png::CommonFreeDecode(&decode);
OP_REQUIRES(context, false,
errors::InvalidArgument("PNG size too large for int: ",
decode.width, " by ", decode.height));
}
```
After `png::CommonFreeDecode(&decode)` gets called, the values of `decode.width` and `decode.height` are in an unspecified state.
### Patches
We have patched the issue in GitHub commit [e746adbfcfee15e9cfdb391ff746c765b99bdf9b](https://github.com/tensorflow/tensorflow/commit/e746adbfcfee15e9cfdb391ff746c765b99bdf9b).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2022-23584'} | 2022-03-03T05:13:53.559196Z | 2022-02-09T23:57:42Z | HIGH | null | {'CWE-416'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-23584', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/image/decode_image_op.cc#L339-L346', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/commit/e746adbfcfee15e9cfdb391ff746c765b99bdf9b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-24x4-6qmh-88qg'} | null | {'https://github.com/tensorflow/tensorflow/commit/e746adbfcfee15e9cfdb391ff746c765b99bdf9b'} | {'https://github.com/tensorflow/tensorflow/commit/e746adbfcfee15e9cfdb391ff746c765b99bdf9b'} |
PyPI | GHSA-vvg4-vgrv-xfr7 | Incomplete validation in `tf.raw_ops.CTCLoss` | ### Impact
Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap:
```python
import tensorflow as tf
inputs = tf.constant([], shape=[10, 16, 0], dtype=tf.float32)
labels_indices = tf.constant([], shape=[8, 0], dtype=tf.int64)
labels_values = tf.constant([-100] * 8, shape=[8], dtype=tf.int32)
sequence_length = tf.constant([-100] * 16, shape=[16], dtype=tf.int32)
tf.raw_ops.CTCLoss(inputs=inputs, labels_indices=labels_indices,
labels_values=labels_values, sequence_length=sequence_length,
preprocess_collapse_repeated=True, ctc_merge_repeated=False,
ignore_longer_outputs_than_inputs=True)
```
An attacker can also trigger a heap buffer overflow:
```python
import tensorflow as tf
inputs = tf.constant([], shape=[7, 2, 0], dtype=tf.float32)
labels_indices = tf.constant([-100, -100], shape=[2, 1], dtype=tf.int64)
labels_values = tf.constant([-100, -100], shape=[2], dtype=tf.int32)
sequence_length = tf.constant([-100, -100], shape=[2], dtype=tf.int32)
tf.raw_ops.CTCLoss(inputs=inputs, labels_indices=labels_indices,
labels_values=labels_values, sequence_length=sequence_length,
preprocess_collapse_repeated=False, ctc_merge_repeated=False,
ignore_longer_outputs_than_inputs=False)
```
Finally, an attacker can trigger a null pointer dereference:
```python
import tensorflow as tf
inputs = tf.constant([], shape=[0, 2, 11], dtype=tf.float32)
labels_indices = tf.constant([], shape=[0, 2], dtype=tf.int64)
labels_values = tf.constant([], shape=[0], dtype=tf.int32)
sequence_length = tf.constant([-100, -100], shape=[2], dtype=tf.int32)
tf.raw_ops.CTCLoss(inputs=inputs, labels_indices=labels_indices,
labels_values=labels_values, sequence_length=sequence_length,
preprocess_collapse_repeated=False, ctc_merge_repeated=False,
ignore_longer_outputs_than_inputs=False)
```
### Patches
We have patched the issue in GitHub commit[14607c0707040d775e06b6817325640cb4b5864c](https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c) followed by GitHub commit [4504a081af71514bb1828048363e6540f797005b](https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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-2021-29613'} | 2022-03-03T05:14:18.725259Z | 2021-05-21T14:28:39Z | MODERATE | null | {'CWE-665'} | {'https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b', 'https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vvg4-vgrv-xfr7', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29613'} | null | {'https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c', 'https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b'} | {'https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b', 'https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c'} |
PyPI | PYSEC-2021-58 | null | sopel-channelmgnt is a channelmgnt plugin for sopel. In versions prior to 2.0.1, on some IRC servers, restrictions around the removal of the bot using the kick/kickban command could be bypassed when kicking multiple users at once. We also believe it may have been possible to remove users from other channels but due to the wonder that is IRC and following RfCs, We have no POC for that. Freenode is not affected. This is fixed in version 2.0.1. As a workaround, do not use this plugin on networks where TARGMAX > 1. | {'CVE-2021-21431', 'GHSA-23c7-6444-399m'} | 2021-05-04T13:59:00Z | 2021-04-09T16:15:00Z | null | null | null | {'https://pypi.org/project/sopel-plugins.channelmgnt/', 'https://github.com/MirahezeBots/sopel-channelmgnt/commit/7c96d400358221e59135f0a0be0744f3fad73856', 'https://github.com/MirahezeBots/sopel-channelmgnt/security/advisories/GHSA-23c7-6444-399m'} | null | {'https://github.com/MirahezeBots/sopel-channelmgnt/commit/7c96d400358221e59135f0a0be0744f3fad73856'} | {'https://github.com/MirahezeBots/sopel-channelmgnt/commit/7c96d400358221e59135f0a0be0744f3fad73856'} |
PyPI | PYSEC-2021-617 | null | TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing `nullptr`s or via `CHECK`-failures) as well as abuse undefined behavior (binding references to `nullptr`s). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. We will deprecate TensorFlow's boosted trees APIs in subsequent releases. 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-57wx-m983-2f88', 'CVE-2021-41208'} | 2021-12-09T06:35:08.671870Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-57wx-m983-2f88', 'https://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c'} | null | {'https://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c'} | {'https://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c'} |
PyPI | GHSA-fphq-gw9m-ghrv | CHECK-fail in `CTCGreedyDecoder` | ### Impact
An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.CTCGreedyDecoder`:
```python
import tensorflow as tf
inputs = tf.constant([], shape=[18, 2, 0], dtype=tf.float32)
sequence_length = tf.constant([-100, 17], shape=[2], dtype=tf.int32)
merge_repeated = False
tf.raw_ops.CTCGreedyDecoder(inputs=inputs, sequence_length=sequence_length, merge_repeated=merge_repeated)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1615440b17b364b875eb06f43d087381f1460a65/tensorflow/core/kernels/ctc_decoder_ops.cc#L37-L50) has a `CHECK_LT` inserted to validate some invariants. When this condition is false, the program aborts, instead of returning a valid error to the user. This abnormal termination can be weaponized in denial of service attacks.
### Patches
We have patched the issue in GitHub commit [ea3b43e98c32c97b35d52b4c66f9107452ca8fb2](https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2).
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-2021-29543'} | 2022-03-03T05:13:59.592746Z | 2021-05-21T14:23:18Z | LOW | null | {'CWE-617'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fphq-gw9m-ghrv', 'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29543'} | null | {'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2'} | {'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2'} |
PyPI | PYSEC-2021-247 | null | TensorFlow is an end-to-end open source platform for machine learning. The validation in `tf.raw_ops.QuantizeAndDequantizeV2` allows invalid values for `axis` argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses `||` to mix two different conditions. If `axis_ < -1` the condition in `OP_REQUIRES` will still be true, but this value of `axis_` results in heap underflow. This allows attackers to read/write to other data on the heap. 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-mq5c-prh3-3f3h', 'CVE-2021-29610'} | 2021-08-27T03:22:41.001819Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mq5c-prh3-3f3h'} | null | {'https://github.com/tensorflow/tensorflow/commit/c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9'} | {'https://github.com/tensorflow/tensorflow/commit/c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9'} |
PyPI | PYSEC-2021-752 | 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'} | 2021-12-09T06:35:35.841569Z | 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 | PYSEC-2021-302 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of fully connected layers in TFLite is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226). We have patched the issue in GitHub commit 718721986aa137691ee23f03638867151f74935f. 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-2021-37680', 'GHSA-cfpj-3q4c-jhvr'} | 2021-08-27T03:22:46.794136Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cfpj-3q4c-jhvr', 'https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f'} | null | {'https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f'} | {'https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f'} |
PyPI | PYSEC-2020-302 | null | In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | {'CVE-2020-26271', 'GHSA-q263-fvxm-m5mw'} | 2021-12-09T06:34:45.035634Z | 2020-12-10T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q263-fvxm-m5mw', 'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b'} | null | {'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b'} | {'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b'} |
PyPI | PYSEC-2022-167 | null | Exposure of Sensitive Information to an Unauthorized Actor in GitHub repository httpie/httpie prior to 3.1.0. | {'GHSA-6pc9-xqrg-wfqw', 'CVE-2022-0430'} | 2022-03-23T14:28:20.245559Z | 2022-03-15T15:15:00Z | null | null | null | {'https://huntr.dev/bounties/dafb2e4f-c6b6-4768-8ef5-b396cd6a801f', 'https://github.com/advisories/GHSA-6pc9-xqrg-wfqw', 'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b'} | null | {'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b'} | {'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b'} |
PyPI | PYSEC-2021-181 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.QuantizeAndDequantizeV4Grad`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to `QuantizeAndDequantizePerChannelGradientImpl`(https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306). However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK` failure otherwise. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version. | {'CVE-2021-29544', 'GHSA-6g85-3hm8-83f9'} | 2021-08-27T03:22:29.285990Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6g85-3hm8-83f9', 'https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b'} | null | {'https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b'} | {'https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b'} |
PyPI | GHSA-4ph2-8337-hm62 | Key Caching behavior in the DynamoDB Encryption Client. | ### Impact
This advisory concerns users of MostRecentProvider in the DynamoDB Encryption Client with a key provider like AWS Key Management Service that allows for permissions on keys to be modified.
When key usage permissions were changed at the key provider, time-based key reauthorization logic in MostRecentProvider did not reauthorize the use of the key. This created the potential for keys to be used in the DynamoDB Encryption Client after permissions to do so were revoked at the key provider.
### Patches
Fixed as of 1.3.0. We recommend users to modify their code and adopt `CachingMostRecentProvider`.
### Workarounds
Users who cannot upgrade to use the `CachingMostRecentProvider` can call `clear()` on the cache to manually flush all of its contents. Next use of the key will force a re-validation to occur with the key provider. | null | 2022-03-03T05:13:41.146598Z | 2021-02-08T17:43:49Z | LOW | null | {'CWE-862'} | {'https://github.com/aws/aws-dynamodb-encryption-python/blob/master/CHANGELOG.rst#130----2021-02-04', 'https://github.com/aws/aws-dynamodb-encryption-python/security/advisories/GHSA-4ph2-8337-hm62', 'https://github.com/aws/aws-dynamodb-encryption-python/commit/90606ec9af7c2b5cb338d64639a62ee867d38d6b', 'https://pypi.org/project/dynamodb-encryption-sdk'} | null | {'https://github.com/aws/aws-dynamodb-encryption-python/commit/90606ec9af7c2b5cb338d64639a62ee867d38d6b'} | {'https://github.com/aws/aws-dynamodb-encryption-python/commit/90606ec9af7c2b5cb338d64639a62ee867d38d6b'} |
PyPI | PYSEC-2019-128 | null | In Twisted before 19.2.1, twisted.web did not validate or sanitize URIs or HTTP methods, allowing an attacker to inject invalid characters such as CRLF. | {'GHSA-6cc5-2vg4-cc7m', 'CVE-2019-12387'} | 2020-08-24T17:37:00Z | 2019-06-10T12:29:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2019-07/msg00030.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2G5RPDQ4BNB336HL6WW5ZJ344MAWNN7N/', 'https://github.com/advisories/GHSA-6cc5-2vg4-cc7m', 'https://usn.ubuntu.com/4308-1/', 'https://www.oracle.com/security-alerts/cpuapr2020.html', 'http://lists.opensuse.org/opensuse-security-announce/2019-07/msg00042.html', 'https://github.com/twisted/twisted/commit/6c61fc4503ae39ab8ecee52d10f10ee2c371d7e2', 'https://twistedmatrix.com/pipermail/twisted-python/2019-June/032352.html', 'https://labs.twistedmatrix.com/2019/06/twisted-1921-released.html', 'https://usn.ubuntu.com/4308-2/'} | null | {'https://github.com/twisted/twisted/commit/6c61fc4503ae39ab8ecee52d10f10ee2c371d7e2'} | {'https://github.com/twisted/twisted/commit/6c61fc4503ae39ab8ecee52d10f10ee2c371d7e2'} |
PyPI | PYSEC-2020-75 | null | petl before 1.68, in some configurations, allows resolution of entities in an XML document. | {'CVE-2020-29128', 'GHSA-69q2-p9xp-739v', 'GHSA-f5gc-p5m3-v347'} | 2020-12-03T16:48:00Z | 2020-11-26T05:15:00Z | null | null | null | {'https://github.com/petl-developers/petl/pull/527/commits/1b0a09f08c3cdfe2e69647bd02f97c1367a5b5f8', 'https://petl.readthedocs.io/en/stable/changes.html', 'https://github.com/petl-developers/petl/pull/527', 'https://github.com/petl-developers/petl/issues/526', 'https://github.com/petl-developers/petl/security/advisories/GHSA-f5gc-p5m3-v347', 'https://github.com/nvn1729/advisories/blob/master/cve-2020-29128.md', 'https://github.com/petl-developers/petl/compare/v1.6.7...v1.6.8', 'https://github.com/advisories/GHSA-69q2-p9xp-739v'} | null | {'https://github.com/petl-developers/petl/pull/527/commits/1b0a09f08c3cdfe2e69647bd02f97c1367a5b5f8'} | {'https://github.com/petl-developers/petl/pull/527/commits/1b0a09f08c3cdfe2e69647bd02f97c1367a5b5f8'} |
PyPI | GHSA-9mqp-7v2h-2382 | Denial of Service in Tensorflow | ### Impact
The `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L235-L241
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.
### Patches
We have patched the issue in 390611e0d45c5793c7066110af37c8514e6a6c54 and will release a patch release for all affected versions.
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 is a variant of [GHSA-63xm-rx5p-xvqr](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr) | {'CVE-2020-15194'} | 2022-03-03T05:14:22.542012Z | 2020-09-25T18:28:19Z | MODERATE | null | {'CWE-20', 'CWE-617'} | {'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9mqp-7v2h-2382', 'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15194', 'https://github.com/tensorflow/tensorflow', '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-55x5-fj6c-h6m8 | HTML Cleaner allows crafted and SVG embedded scripts to pass through | ### Impact
The HTML Cleaner in lxml.html lets certain crafted script content pass through, as well as script content in SVG files embedded using data URIs.
Users that employ the HTML cleaner in a security relevant context should upgrade to lxml 4.6.5.
### Patches
The issue has been resolved in lxml 4.6.5.
### Workarounds
None.
### References
The issues are tracked under the report IDs GHSL-2021-1037 and GHSL-2021-1038.
| {'CVE-2021-43818'} | 2022-04-22T15:47:20.348262Z | 2021-12-13T18:14:36Z | HIGH | null | {'CWE-74', 'CWE-79'} | {'https://github.com/lxml/lxml/commit/a3eacbc0dcf1de1c822ec29fb7d090a4b1712a9c#diff-59130575b4fb2932c957db2922977d7d89afb0b2085357db1a14615a2fcad776', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/V2XMOM5PFT6U5AAXY6EFNT5JZCKKHK2V/', 'https://github.com/lxml/lxml', 'https://lists.debian.org/debian-lts-announce/2021/12/msg00037.html', 'https://nvd.nist.gov/vuln/detail/CVE-2021-43818', 'https://github.com/lxml/lxml/commit/f2330237440df7e8f39c3ad1b1aa8852be3b27c0', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/WZGNET2A4WGLSUXLBFYKNC5PXHQMI3I7/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/ZQ4SPKJX3RRJK4UWA6FXCRHD2TVRQI44/', 'https://www.oracle.com/security-alerts/cpuapr2022.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TUIS2KE3HZ2AAQKXFLTJFZPP2IFHJTC7/', 'https://github.com/lxml/lxml/security/advisories/GHSA-55x5-fj6c-h6m8', 'https://security.netapp.com/advisory/ntap-20220107-0005/', 'https://github.com/lxml/lxml/commit/12fa9669007180a7bb87d990c375cf91ca5b664a', 'https://www.debian.org/security/2022/dsa-5043'} | null | {'https://github.com/lxml/lxml/commit/a3eacbc0dcf1de1c822ec29fb7d090a4b1712a9c#diff-59130575b4fb2932c957db2922977d7d89afb0b2085357db1a14615a2fcad776', 'https://github.com/lxml/lxml/commit/12fa9669007180a7bb87d990c375cf91ca5b664a', 'https://github.com/lxml/lxml/commit/f2330237440df7e8f39c3ad1b1aa8852be3b27c0'} | {'https://github.com/lxml/lxml/commit/12fa9669007180a7bb87d990c375cf91ca5b664a', 'https://github.com/lxml/lxml/commit/a3eacbc0dcf1de1c822ec29fb7d090a4b1712a9c#diff-59130575b4fb2932c957db2922977d7d89afb0b2085357db1a14615a2fcad776', 'https://github.com/lxml/lxml/commit/f2330237440df7e8f39c3ad1b1aa8852be3b27c0'} |
PyPI | PYSEC-2021-494 | 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'} | 2021-12-09T06:34:53.596467Z | 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 | GHSA-6hpv-v2rx-c5g6 | FPE in convolutions with zero size filters | ### Impact
The [implementations for convolution operators](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/conv_ops.cc) trigger a division by 0 if passed empty filter tensor arguments.
### Patches
We have patched the issue in GitHub commit [f2c3931113eaafe9ef558faaddd48e00a6606235](https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235).
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 members of the Aivul Team from Qihoo 360. | {'CVE-2021-41209'} | 2022-03-03T05:13:34.997508Z | 2021-11-10T19:02:17Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hpv-v2rx-c5g6', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41209', 'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} | null | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} |
PyPI | PYSEC-2021-729 | null | TensorFlow is an end-to-end open source platform for machine learning. 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). 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`. 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-crch-j389-5f84', 'CVE-2021-29603'} | 2021-12-09T06:35:32.536343Z | 2021-05-14T20:15:00Z | null | null | null | {'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-j7rm-8ww4-xx2g | Division by zero in TFLite's implementation of `SpaceToDepth` | ### Impact
The `Prepare` step of the `SpaceToDepth` TFLite operator [does not check for 0 before division](https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67).
```cc
const int block_size = params->block_size;
const int input_height = input->dims->data[1];
const int input_width = input->dims->data[2];
int output_height = input_height / block_size;
int output_width = input_width / block_size;
```
An attacker can craft a model such that `params->block_size` would be zero.
### Patches
We have patched the issue in GitHub commit [0d45ea1ca641b21b73bcf9c00e0179cda284e7e7](https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7).
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-2021-29587'} | 2022-03-03T05:13:23.496562Z | 2021-05-21T14:26:45Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j7rm-8ww4-xx2g', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29587', 'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7'} | null | {'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7'} | {'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7'} |
PyPI | PYSEC-2021-379 | 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'} | 2021-10-24T23:24:38.547709Z | 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 | PYSEC-2021-683 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.SparseMatMul`. The division by 0 occurs deep in Eigen code because the `b` tensor is empty. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-xw93-v57j-fcgh', 'CVE-2021-29557'} | 2021-12-09T06:35:24.626277Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/7f283ff806b2031f407db64c4d3edcda8fb9f9f5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xw93-v57j-fcgh'} | null | {'https://github.com/tensorflow/tensorflow/commit/7f283ff806b2031f407db64c4d3edcda8fb9f9f5'} | {'https://github.com/tensorflow/tensorflow/commit/7f283ff806b2031f407db64c4d3edcda8fb9f9f5'} |
PyPI | GHSA-hm3x-jwwf-jpr9 | Exposure of Sensitive Information to an Unauthorized Actor in OpenStack tripleo-heat-templates | An information exposure flaw in openstack-tripleo-heat-templates allows an external user to discover the internal IP or hostname. An attacker could exploit this by checking the `www_authenticate_uri parameter` (which is visible to all end users) in configuration files. This would give sensitive information which may aid in additional system exploitation. A patch is available on the `master` branch and anticipated to be part of version 11.6.1. | {'CVE-2021-4180'} | 2022-03-31T20:48:39.881307Z | 2022-03-24T00:00:17Z | MODERATE | null | {'CWE-200'} | {'https://bugs.launchpad.net/tripleo/+bug/1955397', 'https://github.com/openstack/tripleo-heat-templates/commit/160936df134a471cfd245bd60964046027a571ea', 'https://nvd.nist.gov/vuln/detail/CVE-2021-4180', 'https://github.com/openstack/tripleo-heat-templates', 'https://github.com/openstack/tripleo-heat-templates/commit/2b9461e97fc5c4ceb0848d1cc4484f656bb85515', 'https://bugzilla.redhat.com/show_bug.cgi?id=2035793'} | null | {'https://github.com/openstack/tripleo-heat-templates/commit/2b9461e97fc5c4ceb0848d1cc4484f656bb85515', 'https://github.com/openstack/tripleo-heat-templates/commit/160936df134a471cfd245bd60964046027a571ea'} | {'https://github.com/openstack/tripleo-heat-templates/commit/2b9461e97fc5c4ceb0848d1cc4484f656bb85515', 'https://github.com/openstack/tripleo-heat-templates/commit/160936df134a471cfd245bd60964046027a571ea'} |
PyPI | PYSEC-2021-23 | null | Sydent is a reference matrix identity server. A malicious user could abuse Sydent to send out arbitrary emails from the Sydent email address. This could be used to construct plausible phishing emails, for example. This issue has been fixed in 4469d1d. | {'CVE-2021-29432', 'GHSA-mh74-4m5g-fcjx'} | 2021-04-22T15:25:00Z | 2021-04-15T21:15:00Z | null | null | null | {'https://pypi.org/project/matrix-sydent/', 'https://github.com/matrix-org/sydent/security/advisories/GHSA-mh74-4m5g-fcjx', 'https://github.com/matrix-org/sydent/commit/4469d1d42b2b1612b70638224c07e19623039c42', 'https://github.com/matrix-org/sydent/releases/tag/v2.3.0'} | null | {'https://github.com/matrix-org/sydent/commit/4469d1d42b2b1612b70638224c07e19623039c42'} | {'https://github.com/matrix-org/sydent/commit/4469d1d42b2b1612b70638224c07e19623039c42'} |
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