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PyPI | GHSA-97wf-p777-86jq | Division by zero in TFLite's implementation of Split | ### Impact
The implementation of the `Split` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/e2752089ef7ce9bcf3db0ec618ebd23ea119d0c7/tensorflow/lite/kernels/split.cc#L63-L65):
```cc
TF_LITE_ENSURE_MSG(context, input_size % num_splits == 0, "Not an even split");
const int slice_size = input_size / num_splits;
```
An attacker can craft a model such that `num_splits` would be 0.
### Patches
We have patched the issue in GitHub commit [b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d](https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d).
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-2021-29599'} | 2022-03-03T05:13:40.775608Z | 2021-05-21T14:28:01Z | LOW | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29599', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-97wf-p777-86jq', 'https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d'} | null | {'https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d'} | {'https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d'} |
PyPI | PYSEC-2019-138 | null | In Waitress through version 1.4.0, if a proxy server is used in front of waitress, an invalid request may be sent by an attacker that bypasses the front-end and is parsed differently by waitress leading to a potential for HTTP request smuggling. Specially crafted requests containing special whitespace characters in the Transfer-Encoding header would get parsed by Waitress as being a chunked request, but a front-end server would use the Content-Length instead as the Transfer-Encoding header is considered invalid due to containing invalid characters. If a front-end server does HTTP pipelining to a backend Waitress server this could lead to HTTP request splitting which may lead to potential cache poisoning or unexpected information disclosure. This issue is fixed in Waitress 1.4.1 through more strict HTTP field validation. | {'GHSA-968f-66r5-5v74', 'CVE-2019-16789'} | 2020-02-25T17:15:00Z | 2019-12-26T17:15:00Z | null | null | null | {'https://access.redhat.com/errata/RHSA-2020:0720', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/GVDHR2DNKCNQ7YQXISJ45NT4IQDX3LJ7/', 'https://github.com/github/advisory-review/pull/14604', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/LYEOTGWJZVKPRXX2HBNVIYWCX73QYPM5/', 'https://github.com/advisories/GHSA-968f-66r5-5v74', '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-2022-58 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `MapStage` is vulnerable a `CHECK`-fail if the key tensor is not a scalar. 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-gcvh-66ff-4mwm', 'CVE-2022-21734'} | 2022-03-09T00:17:31.053811Z | 2022-02-03T13:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gcvh-66ff-4mwm', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/map_stage_op.cc#L519-L550', 'https://github.com/tensorflow/tensorflow/commit/f57315566d7094f322b784947093406c2aea0d7d'} | null | {'https://github.com/tensorflow/tensorflow/commit/f57315566d7094f322b784947093406c2aea0d7d'} | {'https://github.com/tensorflow/tensorflow/commit/f57315566d7094f322b784947093406c2aea0d7d'} |
PyPI | PYSEC-2021-484 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.Reverse`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-fxqh-cfjm-fp93', 'CVE-2021-29556'} | 2021-12-09T06:34:52.071121Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxqh-cfjm-fp93'} | null | {'https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1'} | {'https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1'} |
PyPI | PYSEC-2021-650 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/496c2630e51c1a478f095b084329acedb253db6b/tensorflow/core/kernels/conv_grad_shape_utils.cc#L130) does a modulus operation where the divisor is controlled by the caller. 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-r4pj-74mg-8868', 'CVE-2021-29524'} | 2021-12-09T06:35:18.930076Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r4pj-74mg-8868'} | null | {'https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a'} | {'https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a'} |
PyPI | PYSEC-2021-200 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.RFFT`. Eigen code operating on an empty matrix can trigger on an assertion and will cause program termination. 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-ph87-fvjr-v33w', 'CVE-2021-29563'} | 2021-08-27T03:22:32.655132Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-ph87-fvjr-v33w'} | null | {'https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1'} | {'https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1'} |
PyPI | GHSA-q65m-pv3f-wr5r | XSS in Bleach when noscript and raw tag whitelisted | ### Impact
A [mutation XSS](https://cure53.de/fp170.pdf) affects users calling `bleach.clean` with `noscript` and a raw tag (see below) in the allowed/whitelisted tags option.
### Patches
v3.1.1
### Workarounds
* modify `bleach.clean` calls to not whitelist `noscript` and one or more of the following raw tags:
```
title
textarea
script
style
noembed
noframes
iframe
xmp
```
* A strong [Content-Security-Policy](https://developer.mozilla.org/en-US/docs/Web/HTTP/CSP) without `unsafe-inline` and `unsafe-eval` [`script-src`s](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Security-Policy/script-src)) will also help mitigate the risk.
### References
* https://bugzilla.mozilla.org/show_bug.cgi?id=1615315
* https://cure53.de/fp170.pdf
* https://nvd.nist.gov/vuln/detail/CVE-2020-6802
* https://www.checkmarx.com/blog/vulnerabilities-discovered-in-mozilla-bleach
### Credits
* Reported by [Yaniv Nizry](https://twitter.com/ynizry) from the CxSCA AppSec group at Checkmarx
### For more information
If you have any questions or comments about this advisory:
* Open an issue at [https://github.com/mozilla/bleach/issues](https://github.com/mozilla/bleach/issues)
* Email us at [security@mozilla.org](mailto:security@mozilla.org)
| {'CVE-2020-6802'} | 2022-03-03T05:12:38.876936Z | 2020-02-24T17:33:44Z | MODERATE | null | {'CWE-79'} | {'https://bugzilla.mozilla.org/show_bug.cgi?id=1615315', 'https://nvd.nist.gov/vuln/detail/CVE-2020-6802', 'https://github.com/mozilla/bleach/security/advisories/GHSA-q65m-pv3f-wr5r', 'https://www.checkmarx.com/blog/vulnerabilities-discovered-in-mozilla-bleach', 'https://github.com/mozilla/bleach/commit/f77e0f6392177a06e46a49abd61a4d9f035e57fd', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/YTULPQB7HVPPYWEYVNHJGDTSPVIDHIZX/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/OCNLM2MGQTOLCIVVYS2Z5S7KOQJR5JC4/', 'https://cure53.de/fp170.pdf', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/72R4VFFHDRSQMNT7IZU3X2755ZP4HGNI/', 'https://advisory.checkmarx.net/advisory/CX-2020-4276'} | null | {'https://github.com/mozilla/bleach/commit/f77e0f6392177a06e46a49abd61a4d9f035e57fd'} | {'https://github.com/mozilla/bleach/commit/f77e0f6392177a06e46a49abd61a4d9f035e57fd'} |
PyPI | GHSA-g25h-jr74-qp5j | Incomplete validation in `QuantizeV2` | ### Impact
Due to incomplete validation in `tf.raw_ops.QuantizeV2`, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays:
```python
import tensorflow as tf
tf.raw_ops.QuantizeV2(
input=[1,2,3],
min_range=[1,2],
max_range=[],
T=tf.qint32,
mode='SCALED',
round_mode='HALF_AWAY_FROM_ZERO',
narrow_range=False,
axis=1,
ensure_minimum_range=3)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor.
### Patches
We have patched the issue in GitHub commit [6da6620efad397c85493b8f8667b821403516708](https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### 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-2021-37663'} | 2022-03-03T05:12:48.390795Z | 2021-08-25T14:42:23Z | HIGH | null | {'CWE-20'} | {'https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37663', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g25h-jr74-qp5j', 'https://github.com/tensorflow/tensorflow/'} | null | {'https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708'} | {'https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708'} |
PyPI | PYSEC-2017-68 | null | The Recurly Client Python Library before 2.0.5, 2.1.16, 2.2.22, 2.3.1, 2.4.5, 2.5.1, 2.6.2 is vulnerable to a Server-Side Request Forgery vulnerability in the "Resource.get" method that could result in compromise of API keys or other critical resources. | {'CVE-2017-0906', 'GHSA-38rv-5jqc-m2cv'} | 2021-07-25T23:34:52.943535Z | 2017-11-13T17:29:00Z | null | null | null | {'https://github.com/advisories/GHSA-38rv-5jqc-m2cv', 'https://hackerone.com/reports/288635', 'https://dev.recurly.com/page/python-updates', 'https://github.com/recurly/recurly-client-python/commit/049c74699ce93cf126feff06d632ea63fba36742'} | null | {'https://github.com/recurly/recurly-client-python/commit/049c74699ce93cf126feff06d632ea63fba36742'} | {'https://github.com/recurly/recurly-client-python/commit/049c74699ce93cf126feff06d632ea63fba36742'} |
PyPI | PYSEC-2021-715 | 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'} | 2021-12-09T06:35:30.233507Z | 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-2021-345 | null | The rencode package through 1.0.6 for Python allows an infinite loop in typecode decoding (such as via ;\x2f\x7f), enabling a remote attack that consumes CPU and memory. | {'GHSA-gh8j-2pgf-x458', 'CVE-2021-40839'} | 2021-09-26T23:32:54.963571Z | 2021-09-10T02:15:00Z | null | null | null | {'https://github.com/aresch/rencode/commit/572ff74586d9b1daab904c6f7f7009ce0143bb75', 'https://seclists.org/fulldisclosure/2021/Sep/16', 'https://pypi.org/project/rencode/#history', 'https://github.com/advisories/GHSA-gh8j-2pgf-x458', 'https://github.com/aresch/rencode/pull/29'} | null | {'https://github.com/aresch/rencode/commit/572ff74586d9b1daab904c6f7f7009ce0143bb75'} | {'https://github.com/aresch/rencode/commit/572ff74586d9b1daab904c6f7f7009ce0143bb75'} |
PyPI | PYSEC-2021-596 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of division in TFLite is [vulnerable to a division by 0 error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/div.cc). There is no check that the divisor tensor does not contain zero elements. We have patched the issue in GitHub commit 1e206baedf8bef0334cca3eb92bab134ef525a28. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37683', 'GHSA-rhrq-64mq-hf9h'} | 2021-12-09T06:35:06.073182Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1e206baedf8bef0334cca3eb92bab134ef525a28', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rhrq-64mq-hf9h'} | null | {'https://github.com/tensorflow/tensorflow/commit/1e206baedf8bef0334cca3eb92bab134ef525a28'} | {'https://github.com/tensorflow/tensorflow/commit/1e206baedf8bef0334cca3eb92bab134ef525a28'} |
PyPI | PYSEC-2022-120 | null | Tensorflow is an Open Source Machine Learning Framework. ### Impact An attacker can craft a TFLite model that would trigger a division by zero in the implementation of depthwise convolutions. The parameters of the convolution can be user controlled and are also used within a division operation to determine the size of the padding that needs to be added before applying the convolution. There is no check before this division that the divisor is strictly positive. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-428x-9xc2-m8mj', 'CVE-2022-21741'} | 2022-03-09T00:18:25.119172Z | 2022-02-03T15:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-428x-9xc2-m8mj', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/lite/kernels/depthwise_conv.cc#L96', 'https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc'} | null | {'https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc'} | {'https://github.com/tensorflow/tensorflow/commit/e5b0eec199c2d03de54fd6a7fd9275692218e2bc'} |
PyPI | GHSA-55j9-849x-26h4 | Remote Code Execution in Red Discord Bot | ### Impact
A RCE exploit has been discovered in the Trivia module: this exploit allows Discord users with specifically crafted usernames to inject code into the Trivia module's leaderboard command. By abusing this exploit, it's possible to perform destructive actions and/or access sensitive information.
### Patches
This critical exploit has been fixed on version 3.3.11.
### Workarounds
Unloading the Trivia module with ``unload trivia`` can render this exploit not accessible. We still highly recommend updating to 3.3.11 to completely patch this issue.
### References
https://github.com/Cog-Creators/Red-DiscordBot/pull/4175
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Cog-Creators/Red-DiscordBot](https://github.com/Cog-Creators/Red-DiscordBot)
* Over on our [Discord server](https://discord.gg/red) | {'CVE-2020-15140'} | 2022-03-03T05:10:56.501261Z | 2020-08-21T16:30:52Z | HIGH | null | {'CWE-74'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15140', 'https://github.com/Cog-Creators/Red-DiscordBot/security/advisories/GHSA-55j9-849x-26h4', 'https://github.com/Cog-Creators/Red-DiscordBot', 'https://github.com/Cog-Creators/Red-DiscordBot/pull/4175/commits/9ab536235bafc2b42c3c17d7ce26f1cc64482a81'} | null | {'https://github.com/Cog-Creators/Red-DiscordBot/pull/4175/commits/9ab536235bafc2b42c3c17d7ce26f1cc64482a81'} | {'https://github.com/Cog-Creators/Red-DiscordBot/pull/4175/commits/9ab536235bafc2b42c3c17d7ce26f1cc64482a81'} |
PyPI | PYSEC-2021-579 | 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.RaggedTensorToVariant`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L129) has an incomplete validation of the splits values, missing the case when the argument would be empty. We have patched the issue in GitHub commit be7a4de6adfbd303ce08be4332554dff70362612. 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-2021-37666', 'GHSA-w4xf-2pqw-5mq7'} | 2021-12-09T06:35:04.618744Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w4xf-2pqw-5mq7'} | null | {'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612'} | {'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612'} |
PyPI | PYSEC-2014-78 | null | Cross-site scripting (XSS) vulnerability in util/templatetags/djblets_js.py in Djblets before 0.7.30 and 0.8.x before 0.8.3 for Django, as used in Review Board, allows remote attackers to inject arbitrary web script or HTML via a JSON object, as demonstrated by the name field when changing a user name. | {'CVE-2014-3994'} | 2021-08-27T03:22:03.219875Z | 2014-06-16T18:55:00Z | null | null | null | {'http://www.securityfocus.com/bid/67932', 'http://secunia.com/advisories/58691', 'https://github.com/djblets/djblets/commit/e2c79117efd925636acd871a5f473512602243cf', 'https://code.google.com/p/reviewboard/issues/detail?id=3406', 'http://seclists.org/oss-sec/2014/q2/498', 'https://github.com/djblets/djblets/commit/77a68c03cd619a0996f3f37337b8c39ca6643d6e', 'http://seclists.org/oss-sec/2014/q2/494', 'https://github.com/djblets/djblets/commit/50000d0bbb983fa8c097b588d06b64df8df483bd'} | null | {'https://github.com/djblets/djblets/commit/e2c79117efd925636acd871a5f473512602243cf', 'https://github.com/djblets/djblets/commit/50000d0bbb983fa8c097b588d06b64df8df483bd', 'https://github.com/djblets/djblets/commit/77a68c03cd619a0996f3f37337b8c39ca6643d6e'} | {'https://github.com/djblets/djblets/commit/77a68c03cd619a0996f3f37337b8c39ca6643d6e', 'https://github.com/djblets/djblets/commit/50000d0bbb983fa8c097b588d06b64df8df483bd', 'https://github.com/djblets/djblets/commit/e2c79117efd925636acd871a5f473512602243cf'} |
PyPI | PYSEC-2020-129 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15206', 'GHSA-w5gh-2wr2-pm6g'} | 2020-10-29T16:15:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w5gh-2wr2-pm6g', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6'} | null | {'https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6'} | {'https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6'} |
PyPI | GHSA-q4qf-3fc6-8x34 | Segfault and data corruption in tensorflow-lite | ### Impact
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:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reduce.h#L68-L72
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.
### Patches
We have patched the issue in 2d88f470dea2671b430884260f3626b1fe99830a and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2020-15207'} | 2022-03-03T05:11:52.408012Z | 2020-09-25T18:28:43Z | HIGH | null | {'CWE-787', 'CWE-119'} | {'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://nvd.nist.gov/vuln/detail/CVE-2020-15207', 'https://github.com/tensorflow/tensorflow', '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-2021-216 | 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'} | 2021-08-27T03:22:35.384566Z | 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-2021-646 | null | TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. 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-2021-29520', 'GHSA-wcv5-qrj6-9pfm'} | 2021-12-09T06:35:18.334867Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wcv5-qrj6-9pfm', 'https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197'} | null | {'https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197'} | {'https://github.com/tensorflow/tensorflow/commit/8f37b52e1320d8d72a9529b2468277791a261197'} |
PyPI | PYSEC-2021-703 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-v6r6-84gr-92rm', 'CVE-2021-29577'} | 2021-12-09T06:35:28.044558Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v6r6-84gr-92rm'} | null | {'https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d'} | {'https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d'} |
PyPI | GHSA-9x4c-63pf-525f | Arbitrary Code Generation | ### Impact
Clients generated with a maliciously crafted OpenAPI Document can generate arbitrary Python code. Subsequent execution of this malicious client is arbitrary code execution.
Giving this a CVSS of 8.0 (high) with CVSS:3.0/AV:N/AC:H/PR:L/UI:R/S:C/C:H/I:H/A:H/E:P/RL:U/RC:C .
### Patches
Fix will be included in version 0.5.3
### Workarounds
Inspect OpenAPI documents before generating, or inspect generated code before executing.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [openapi-python-client](https://github.com/triaxtec/openapi-python-client/issues)
* Email us at [danthony@triaxtec.com](mailto:danthony@triaxtec.com) | {'CVE-2020-15142'} | 2022-03-03T05:13:26.062571Z | 2020-08-20T14:38:24Z | HIGH | null | {'CWE-94'} | {'https://github.com/triaxtec/openapi-python-client/blob/main/CHANGELOG.md#053---2020-08-13', 'https://github.com/triaxtec/openapi-python-client/security/advisories/GHSA-9x4c-63pf-525f', 'https://pypi.org/project/openapi-python-client/', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15142', 'https://github.com/triaxtec/openapi-python-client/commit/f7a56aae32cba823a77a84a1f10400799b19c19a'} | null | {'https://github.com/triaxtec/openapi-python-client/commit/f7a56aae32cba823a77a84a1f10400799b19c19a'} | {'https://github.com/triaxtec/openapi-python-client/commit/f7a56aae32cba823a77a84a1f10400799b19c19a'} |
PyPI | GHSA-wvcv-832q-fjg7 | RSA weakness in tslite-ng | ### Impact
The code that performs decryption and padding check in RSA PKCS#1 v1.5 decryption is data dependant.
In particular, code in current (as of 0.8.0-alpha38) master
https://github.com/tlsfuzzer/tlslite-ng/blob/0812ed60860fa61a6573b2c0e18771414958f46d/tlslite/utils/rsakey.py#L407-L441
and code in 0.7.5 branch
https://github.com/tlsfuzzer/tlslite-ng/blob/acdde3161124d6ae37c506b3476aea9996d12e97/tlslite/utils/rsakey.py#L394-L425
has multiple ways in which it leaks information (for one, it aborts as soon as the plaintext doesn't start with 0x00, 0x02) about the decrypted ciphertext (both the bit length of the decrypted message as well as where the first unexpected byte lays).
All TLS servers that enable RSA key exchange as well as applications that use the RSA decryption API directly are vulnerable.
All previous versions of tlslite-ng are vulnerable.
### Patches
The patches to fix it are proposed in
https://github.com/tlsfuzzer/tlslite-ng/pull/438
https://github.com/tlsfuzzer/tlslite-ng/pull/439
Note: the patches depend on Python processing the individual bytes in side-channel free manner, this is known to not be the case: https://securitypitfalls.wordpress.com/2018/08/03/constant-time-compare-in-python/
As such, users that require side-channel resistance are recommended to use different TLS implementations, as stated in the [security policy](https://github.com/tlsfuzzer/tlslite-ng/blob/master/SECURITY.md) of tlslite-ng.
### Workarounds
There is no way to workaround this issue.
### References
https://securitypitfalls.wordpress.com/2018/08/03/constant-time-compare-in-python/
### For more information
If you have any questions or comments about this advisory please open an issue in [tlslite-ng](https://github.com/tlsfuzzer/tlslite-ng/issues). | {'CVE-2020-26263'} | 2022-03-03T05:13:09.290965Z | 2020-12-21T16:56:37Z | LOW | null | {'CWE-326'} | {'https://github.com/tlsfuzzer/tlslite-ng/pull/439', 'https://pypi.org/project/tlslite-ng/', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26263', 'https://github.com/tlsfuzzer/tlslite-ng/security/advisories/GHSA-wvcv-832q-fjg7', 'https://securitypitfalls.wordpress.com/2018/08/03/constant-time-compare-in-python/', 'https://github.com/tlsfuzzer/tlslite-ng/commit/c28d6d387bba59d8bd5cb3ba15edc42edf54b368', 'https://github.com/tlsfuzzer/tlslite-ng/pull/438'} | null | {'https://github.com/tlsfuzzer/tlslite-ng/commit/c28d6d387bba59d8bd5cb3ba15edc42edf54b368'} | {'https://github.com/tlsfuzzer/tlslite-ng/commit/c28d6d387bba59d8bd5cb3ba15edc42edf54b368'} |
PyPI | PYSEC-2022-136 | null | Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a type during shape inference. This case is covered by the `DCHECK` function however, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the `ValueOrDie` line. This results in an assertion failure as `ret` contains an error `Status`, not a value. In the second case we also get a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range. | {'GHSA-rww7-2gpw-fv6j', 'CVE-2022-23572'} | 2022-03-09T00:18:27.267952Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.cc#L168-L174', 'https://github.com/tensorflow/tensorflow/commit/cb164786dc891ea11d3a900e90367c339305dc7b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rww7-2gpw-fv6j'} | null | {'https://github.com/tensorflow/tensorflow/commit/cb164786dc891ea11d3a900e90367c339305dc7b'} | {'https://github.com/tensorflow/tensorflow/commit/cb164786dc891ea11d3a900e90367c339305dc7b'} |
PyPI | PYSEC-2021-580 | 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.UnicodeEncode`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unicode_ops.cc#L533-L539) reads the first dimension of the `input_splits` tensor before validating that this tensor is not empty. We have patched the issue in GitHub commit 2e0ee46f1a47675152d3d865797a18358881d7a6. 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-2021-37667', 'GHSA-w74j-v8xh-3w5h'} | 2021-12-09T06:35:04.699565Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/2e0ee46f1a47675152d3d865797a18358881d7a6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w74j-v8xh-3w5h'} | null | {'https://github.com/tensorflow/tensorflow/commit/2e0ee46f1a47675152d3d865797a18358881d7a6'} | {'https://github.com/tensorflow/tensorflow/commit/2e0ee46f1a47675152d3d865797a18358881d7a6'} |
PyPI | GHSA-9w2p-5mgw-p94c | Integer overflow due to conversion to unsigned | ### Impact
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.
```python
import tensorflow as tf
tf.raw_ops.QuantizeAndDequantizeV4Grad(
gradients=[1.0,2.0],
input=[1.0,1.0],
input_min=[0.0],
input_max=[10.0],
axis=-100)
```
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.
### Patches
We have patched the issue in GitHub commit [96f364a1ca3009f98980021c4b32be5fdcca33a1](https://github.com/tensorflow/tensorflow/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.
### 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-2021-37645'} | 2022-03-03T05:14:08.591493Z | 2021-08-25T14:43:37Z | MODERATE | null | {'CWE-681'} | {'https://github.com/tensorflow/tensorflow/commit/96f364a1ca3009f98980021c4b32be5fdcca33a1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9w2p-5mgw-p94c', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37645'} | null | {'https://github.com/tensorflow/tensorflow/commit/96f364a1ca3009f98980021c4b32be5fdcca33a1'} | {'https://github.com/tensorflow/tensorflow/commit/96f364a1ca3009f98980021c4b32be5fdcca33a1'} |
PyPI | GHSA-cm5x-837x-jf3c | Division by 0 in inplace operations | ### Impact
An attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0:
```python
import tensorflow as tf
tf.raw_ops.InplaceSub(x=[],i=[-99,-1,-1],v=[1,1,1])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if `x` and `v` are empty but the code uses `||` instead of `&&`.
### Patches
We have patched the issue in GitHub commit [e86605c0a336c088b638da02135ea6f9f6753618](https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618).
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-2021-37660'} | 2022-03-03T05:12:57.003702Z | 2021-08-25T14:42:44Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cm5x-837x-jf3c', 'https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37660'} | null | {'https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618'} | {'https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618'} |
PyPI | GHSA-2wx6-wc87-rmjm | GitHub personal access token leaking into temporary EasyBuild (debug) logs | ### Impact
The GitHub Personal Access Token (PAT) used by EasyBuild for the GitHub integration features (like `--new-pr`, `--from-pr`, etc.) is shown in plain text in EasyBuild debug log files.
Scope:
* the log message only appears in the top-level log file, *not* in the individual software installation logs (see https://easybuild.readthedocs.io/en/latest/Logfiles.html);
- as a consequence, tokens are *not* included in the partial log files that are uploaded into a gist when using `--upload-test-report` in combination with `--from-pr`, nor in the installation logs that are copied to the software installation directories;
* the message is only logged when using `--debug`, so it will not appear when using the default EasyBuild configuration (only info messages are logged by default);
* the log message is triggered via `--from-pr`, but also via various other GitHub integration options like `--new-pr`, `--merge-pr`, `--close-pr`, etc., but usually only appears in the temporary log file that is cleaned up automatically as soon as eb completes successfully;
* you may have several debug log files that include your GitHub token in `/tmp` (or a different location if you've set the `--tmpdir` EasyBuild configuration option) on the systems where you use EasyBuild, but they are located in a subdirectory that is only accessible to your account (permissions set to 700);
* the only way that a log file that may include your token could have been made public is *if you shared it yourself*, for example by copying the contents of the log file into a gist manually, or by sending a log file to someone;
* for log files uploaded to GitHub, your token would be revoked automatically when GitHub notices it;
### Patches
The issue is fixed with the changes in https://github.com/easybuilders/easybuild-framework/pull/3248.
This fix is included in EasyBuild v4.1.2 (released on Mon Mar 16th 2020), and in the `master`+ `develop` branches of the `easybuild-framework` repository since Mon Mar 16th 2020 (see https://github.com/easybuilders/easybuild-framework/pull/3248 and https://github.com/easybuilders/easybuild-framework/pull/3249 resp.).
**Make sure you revoke the existing GitHub tokens you're using with EasyBuild** (via https://github.com/settings/tokens), and install new ones using "`eb --install-github-token --force`" (see also https://easybuild.readthedocs.io/en/latest/Integration_with_GitHub.html#installing-a-github-token-install-github-token).
### Workarounds
* avoid using the GitHub integration features (see https://easybuild.readthedocs.io/en/latest/Integration_with_GitHub.html) with EasyBuild versions older than version 4.1.2;
* don't share top-level EasyBuild (debug) log files with others, unless you are sure your GitHub token is not included in them;
* clean up temporary EasyBuild log files in `/tmp`on the system(s) where you`re using EasyBuild
### References
* https://github.com/easybuilders/easybuild-framework/pull/3248 (PR that fixes the issue)
* (release announcement to EasyBuild mailing list)
### For more information
* Open an issue in [the `easybuild-framework` repository](https://github.com/easybuilders/easybuild-framework)
* Email us at [easybuild-admin@lists.ugent.be](mailto:easybuild-admin@lists.ugent.be) | {'CVE-2020-5262'} | 2022-03-03T05:09:52.633203Z | 2020-03-19T17:29:58Z | MODERATE | null | {'CWE-532'} | {'https://github.com/easybuilders/easybuild-framework/security/advisories/GHSA-2wx6-wc87-rmjm', 'https://github.com/easybuilders/easybuild-framework/pull/3249', 'https://github.com/easybuilders/easybuild-framework/pull/3248', 'https://github.com/easybuilders/easybuild-framework/commit/210743d0e3618a8ac0a56eb9c0f4fa4fd8ae53b9', 'https://nvd.nist.gov/vuln/detail/CVE-2020-5262'} | null | {'https://github.com/easybuilders/easybuild-framework/commit/210743d0e3618a8ac0a56eb9c0f4fa4fd8ae53b9'} | {'https://github.com/easybuilders/easybuild-framework/commit/210743d0e3618a8ac0a56eb9c0f4fa4fd8ae53b9'} |
PyPI | PYSEC-2021-611 | null | TensorFlow is an open source platform for machine learning. In affeced versions during execution, `EinsumHelper::ParseEquation()` is supposed to set the flags in `input_has_ellipsis` vector and `*output_has_ellipsis` boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to `true` and never assigns `false`. This results in unitialized variable access if callers assume that `EinsumHelper::ParseEquation()` always sets these flags. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41201', 'GHSA-j86v-p27c-73fm'} | 2021-12-09T06:35:07.767696Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j86v-p27c-73fm'} | null | {'https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6'} | {'https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6'} |
PyPI | PYSEC-2021-241 | null | TensorFlow is an end-to-end open source platform for machine learning. The TFLite implementation of hashtable lookup is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/hashtable_lookup.cc#L114-L115) An attacker can craft a model such that `values`'s first dimension would be 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-8rm6-75mf-7r7r', 'CVE-2021-29604'} | 2021-08-27T03:22:39.893665Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rm6-75mf-7r7r', 'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2'} | null | {'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2'} | {'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2'} |
PyPI | GHSA-xw93-v57j-fcgh | Division by 0 in `SparseMatMul` | ### Impact
An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.SparseMatMul`:
```python
import tensorflow as tf
a = tf.constant([100.0, 100.0, 100.0, 100.0], shape=[2, 2], dtype=tf.float32)
b = tf.constant([], shape=[0, 2], dtype=tf.float32)
tf.raw_ops.SparseMatMul(
a=a, b=b, transpose_a=True, transpose_b=True,
a_is_sparse=True, b_is_sparse=True)
```
The division by 0 occurs deep in Eigen code because the `b` tensor is empty.
### Patches
We have patched the issue in GitHub commit [7f283ff806b2031f407db64c4d3edcda8fb9f9f5](https://github.com/tensorflow/tensorflow/commit/7f283ff806b2031f407db64c4d3edcda8fb9f9f5).
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-2021-29557'} | 2022-03-03T05:14:10.416499Z | 2021-05-21T14:24:48Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/7f283ff806b2031f407db64c4d3edcda8fb9f9f5', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29557', '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-f8q4-jwww-x3wv | Race Condition in Paramiko | In Paramiko before 2.10.1, a race condition (between creation and chmod) in the write_private_key_file function could allow unauthorized information disclosure. | {'CVE-2022-24302'} | 2022-04-18T22:32:22.773225Z | 2022-03-19T00:01:03Z | MODERATE | null | {'CWE-362'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TPMKRUS4HO3P7NR7P4Y6CLHB4MBEE3AI/', 'https://www.paramiko.org/changelog.html', 'https://github.com/paramiko/paramiko', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/LUEUEGILZ7MQXRSUF5VMMO4SWJQVPTQL/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24302', 'https://github.com/advisories/GHSA-f8q4-jwww-x3wv', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/U63MJ2VOLLQ35R7CYNREUHSXYLWNPVSB/', 'https://github.com/paramiko/paramiko/commit/4c491e299c9b800358b16fa4886d8d94f45abe2e', 'https://lists.debian.org/debian-lts-announce/2022/03/msg00032.html', 'https://github.com/pypa/advisory-database/tree/main/vulns/paramiko/PYSEC-2022-166.yaml', 'https://github.com/paramiko/paramiko/blob/363a28d94cada17f012c1604a3c99c71a2bda003/paramiko/pkey.py#L546'} | null | {'https://github.com/paramiko/paramiko/commit/4c491e299c9b800358b16fa4886d8d94f45abe2e'} | {'https://github.com/paramiko/paramiko/commit/4c491e299c9b800358b16fa4886d8d94f45abe2e'} |
PyPI | PYSEC-2014-7 | null | The administrative interface (contrib.admin) in Django before 1.4.14, 1.5.x before 1.5.9, 1.6.x before 1.6.6, and 1.7 before release candidate 3 does not check if a field represents a relationship between models, which allows remote authenticated users to obtain sensitive information via a to_field parameter in a popup action to an admin change form page, as demonstrated by a /admin/auth/user/?pop=1&t=password URI. | {'CVE-2014-0483'} | 2021-07-05T00:01:19.203370Z | 2014-08-26T14:55:00Z | null | null | null | {'http://www.debian.org/security/2014/dsa-3010', 'https://www.djangoproject.com/weblog/2014/aug/20/security/', 'http://secunia.com/advisories/59782', 'https://github.com/django/django/commit/2b31342cdf14fc20e07c43d258f1e7334ad664a6', 'http://secunia.com/advisories/61281', 'http://secunia.com/advisories/61276', 'http://lists.opensuse.org/opensuse-updates/2014-09/msg00023.html'} | null | {'https://github.com/django/django/commit/2b31342cdf14fc20e07c43d258f1e7334ad664a6'} | {'https://github.com/django/django/commit/2b31342cdf14fc20e07c43d258f1e7334ad664a6'} |
PyPI | PYSEC-2020-241 | null | MoinMoin is a wiki engine. In MoinMoin before version 1.9.11, an attacker with write permissions can upload an SVG file that contains malicious javascript. This javascript will be executed in a user's browser when the user is viewing that SVG file on the wiki. Users are strongly advised to upgrade to a patched version. MoinMoin Wiki 1.9.11 has the necessary fixes and also contains other important fixes. | {'GHSA-4q96-6xhq-ff43', 'CVE-2020-15275'} | 2021-08-27T03:22:07.834309Z | 2020-11-11T16:15:00Z | null | null | null | {'https://advisory.checkmarx.net/advisory/CX-2020-4285', 'https://github.com/moinwiki/moin-1.9/commit/31de9139d0aabc171e94032168399b4a0b2a88a2', 'https://github.com/moinwiki/moin-1.9/security/advisories/GHSA-4q96-6xhq-ff43', 'https://github.com/moinwiki/moin-1.9/releases/tag/1.9.11'} | null | {'https://github.com/moinwiki/moin-1.9/commit/31de9139d0aabc171e94032168399b4a0b2a88a2'} | {'https://github.com/moinwiki/moin-1.9/commit/31de9139d0aabc171e94032168399b4a0b2a88a2'} |
PyPI | PYSEC-2017-29 | null | modules/serverdensity_device.py in SaltStack before 2014.7.4 does not properly handle files in /tmp. | {'CVE-2015-1838'} | 2021-07-05T00:01:26.175371Z | 2017-04-13T14:59:00Z | null | null | null | {'https://github.com/saltstack/salt/commit/e11298d7155e9982749483ca5538e46090caef9c', 'https://docs.saltstack.com/en/latest/topics/releases/2014.7.4.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-January/175568.html', 'https://bugzilla.redhat.com/show_bug.cgi?id=1212784'} | null | {'https://github.com/saltstack/salt/commit/e11298d7155e9982749483ca5538e46090caef9c'} | {'https://github.com/saltstack/salt/commit/e11298d7155e9982749483ca5538e46090caef9c'} |
PyPI | PYSEC-2020-304 | null | TensorFlow before 1.7.0 has an integer overflow that causes an out-of-bounds read, possibly causing disclosure of the contents of process memory. This occurs in the DecodeBmp feature of the BMP decoder in core/kernels/decode_bmp_op.cc. | {'CVE-2018-21233'} | 2021-12-09T06:35:11.657729Z | 2020-05-04T15:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2018-001.md', 'https://github.com/tensorflow/tensorflow/commit/49f73c55d56edffebde4bca4a407ad69c1cae433'} | null | {'https://github.com/tensorflow/tensorflow/commit/49f73c55d56edffebde4bca4a407ad69c1cae433'} | {'https://github.com/tensorflow/tensorflow/commit/49f73c55d56edffebde4bca4a407ad69c1cae433'} |
PyPI | GHSA-cqxx-66wh-8pjw | Improper Removal of Sensitive Information Before Storage or Transfer in irrd | IRRd did not always filter password hashes in query responses relating to `mntner` objects and database exports. This may have allowed adversaries to retrieve some of these hashes, perform a brute-force search for the clear-text passphrase, and use these to make unauthorised changes to affected IRR objects. This issue only affected instances that process password hashes, which means it is limited to IRRd instances that serve authoritative databases. IRRd instances operating solely as mirrors of other IRR databases are not affected.
The issue occurred:
* For `mntner` objects where all password hash names (`MD5-PW` and `CRYPT-PW`) were in lower or mixed case in the `auth` attribute. For these objects, hashes remained in the output of all queries of any method and all database exports made with the `export_destination` setting. Fortunately, objects in the common public IRR database virtually all use uppercase hash names which means very few of those objects were affected.
* For any GraphQL queries that queried the `auth` field on `mntner` objects.
* For any GraphQL queries that queried the `objectText` field on the `journal` field on `mntner` objects, if the `nrtm_access_list` setting permitted journal access.
The two GraphQL cases are visible in logs, allowing users to determine whether any existing objects had their hashes exposed.
This has been fixed in IRRd 4.2.3 and the main branch. Versions in the 4.1.x series never were affected. Users of the 4.2.x series are strongly recommended to upgrade. All users running a more recent version from the main branch should update to the latest version. Alternatively, but not recommended, apply the patch manually [for 4.2.x] | {'CVE-2022-24798'} | 2022-04-13T01:30:11.714249Z | 2022-04-01T13:59:17Z | HIGH | null | {'CWE-212'} | {'https://github.com/irrdnet/irrd', 'https://github.com/irrdnet/irrd/commit/fdffaf8dd71713f06e99dff417e6aa1e6fa84b70', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24798', 'https://github.com/irrdnet/irrd/security/advisories/GHSA-cqxx-66wh-8pjw', 'https://irrd.readthedocs.io/en/stable/releases/4.2.3/', 'https://github.com/irrdnet/irrd/commit/0e41bae8d3d27316381a2fc7b466597230e35ec6'} | null | {'https://github.com/irrdnet/irrd/commit/fdffaf8dd71713f06e99dff417e6aa1e6fa84b70', 'https://github.com/irrdnet/irrd/commit/0e41bae8d3d27316381a2fc7b466597230e35ec6'} | {'https://github.com/irrdnet/irrd/commit/0e41bae8d3d27316381a2fc7b466597230e35ec6', 'https://github.com/irrdnet/irrd/commit/fdffaf8dd71713f06e99dff417e6aa1e6fa84b70'} |
PyPI | PYSEC-2021-754 | null | TensorFlow is an end-to-end open source platform for machine learning. 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. 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. We have patched the issue in GitHub 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. | {'CVE-2021-37643', 'GHSA-fcwc-p4fc-c5cc'} | 2021-12-09T06:35:36.031970Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/482da92095c4d48f8784b1f00dda4f81c28d2988', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fcwc-p4fc-c5cc'} | null | {'https://github.com/tensorflow/tensorflow/commit/482da92095c4d48f8784b1f00dda4f81c28d2988'} | {'https://github.com/tensorflow/tensorflow/commit/482da92095c4d48f8784b1f00dda4f81c28d2988'} |
PyPI | PYSEC-2021-304 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. [For example](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/depthwise_conv.cc#L198-L200). The issue stems from the fact that `quantization.params` is only valid if `quantization.type` is different that `kTfLiteNoQuantization`. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. 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-2021-37682', 'GHSA-4c4g-crqm-xrxw'} | 2021-08-27T03:22:46.967506Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5', 'https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887', 'https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4c4g-crqm-xrxw'} | null | {'https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538', 'https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5', 'https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887'} | {'https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5', 'https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887', 'https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538'} |
PyPI | PYSEC-2022-161 | null | Open Redirect in GitHub repository archivy/archivy prior to 1.7.0. | {'GHSA-28mg-98xm-q493', 'CVE-2022-0697'} | 2022-03-11T17:31:40.885093Z | 2022-03-06T23:15:00Z | null | null | null | {'https://github.com/archivy/archivy/commit/2d8cb29853190d42572b36deb61127e68d6be574', 'https://huntr.dev/bounties/2d0301a2-10ff-48f4-a346-5a0e8707835b', 'https://github.com/advisories/GHSA-28mg-98xm-q493'} | null | {'https://github.com/archivy/archivy/commit/2d8cb29853190d42572b36deb61127e68d6be574'} | {'https://github.com/archivy/archivy/commit/2d8cb29853190d42572b36deb61127e68d6be574'} |
PyPI | PYSEC-2021-187 | 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'} | 2021-08-27T03:22:30.332227Z | 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 | PYSEC-2021-168 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when calling `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the first line of `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29531', 'GHSA-3qxp-qjq7-w4hf'} | 2021-08-27T03:22:26.851089Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3qxp-qjq7-w4hf'} | null | {'https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1'} | {'https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1'} |
PyPI | PYSEC-2021-492 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference in the implementation of `tf.raw_ops.EditDistance`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/79865b542f9ffdc9caeb255631f7c56f1d4b6517/tensorflow/core/kernels/edit_distance_op.cc#L103-L159) has incomplete validation of the input parameters. 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-2021-29564', 'GHSA-75f6-78jr-4656'} | 2021-12-09T06:34:53.290029Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-75f6-78jr-4656', 'https://github.com/tensorflow/tensorflow/commit/f4c364a5d6880557f6f5b6eb5cee2c407f0186b3'} | null | {'https://github.com/tensorflow/tensorflow/commit/f4c364a5d6880557f6f5b6eb5cee2c407f0186b3'} | {'https://github.com/tensorflow/tensorflow/commit/f4c364a5d6880557f6f5b6eb5cee2c407f0186b3'} |
PyPI | PYSEC-2021-538 | 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'} | 2021-12-09T06:35:00.479412Z | 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 | GHSA-m87f-9fvv-2mgg | Deserialization of Untrusted Data in parlai | ### Impact
Due to use of unsafe YAML deserialization logic, an attacker with the ability to modify local YAML configuration files could provide malicious input, resulting in remote code execution or similar risks.
### Patches
The issue can be patched by upgrading to v1.1.0 or later. It can also be patched by replacing YAML deserialization with equivalent safe_load calls.
### References
- https://github.com/facebookresearch/ParlAI/commit/507d066ef432ea27d3e201da08009872a2f37725
- https://github.com/facebookresearch/ParlAI/commit/4374fa2aba383db6526ab36e939eb1cf8ef99879
- https://anon-artist.github.io/blogs/blog3.html | {'CVE-2021-39207'} | 2022-03-03T05:14:11.580279Z | 2021-09-13T20:05:39Z | HIGH | null | {'CWE-502'} | {'https://github.com/facebookresearch/ParlAI/commit/507d066ef432ea27d3e201da08009872a2f37725', 'https://nvd.nist.gov/vuln/detail/CVE-2021-39207', 'https://github.com/facebookresearch/ParlAI', '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-2017-52 | null | Plone 3.3.0 through 3.3.6, 4.0.0 through 4.0.10, 4.1.0 through 4.1.6, 4.2.0 through 4.2.7, 4.3.0 through 4.3.6, and 5.0rc1 allows remote attackers to add a new member to a Plone site with registration enabled, without acknowledgment of site administrator. | {'CVE-2015-7315'} | 2021-07-25T23:34:48.187458Z | 2017-09-25T17:29:00Z | null | null | null | {'https://github.com/zopefoundation/Products.CMFCore/commit/e1d981bfa14b664317285f0f36498f4be4a23406', 'https://bugzilla.redhat.com/show_bug.cgi?id=1264791', 'https://plone.org/security/hotfix/20150910/anonymous-is-able-to-create-plone-members', 'http://www.openwall.com/lists/oss-security/2015/09/22/13'} | null | {'https://github.com/zopefoundation/Products.CMFCore/commit/e1d981bfa14b664317285f0f36498f4be4a23406'} | {'https://github.com/zopefoundation/Products.CMFCore/commit/e1d981bfa14b664317285f0f36498f4be4a23406'} |
PyPI | GHSA-rjjg-hgv6-h69v | Memory corruption in Tensorflow | ### Impact
The implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/python/tfe_wrapper.cc#L1361
However, there is nothing stopping users from passing in a Python object instead of a tensor.
```python
In [2]: tf.experimental.dlpack.to_dlpack([2])
==1720623==WARNING: MemorySanitizer: use-of-uninitialized-value
#0 0x55b0ba5c410a in tensorflow::(anonymous namespace)::GetTensorFromHandle(TFE_TensorHandle*, TF_Status*) third_party/tensorflow/c/eager/dlpack.cc:46:7
#1 0x55b0ba5c38f4 in tensorflow::TFE_HandleToDLPack(TFE_TensorHandle*, TF_Status*) third_party/tensorflow/c/eager/dlpack.cc:252:26
...
```
The uninitialized memory address is due to a `reinterpret_cast`
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/python/eager/pywrap_tensor.cc#L848-L850
Since the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fails.
### Patches
We have patched the issue in 22e07fb204386768e5bcbea563641ea11f96ceb8 and will release a patch release for all affected versions.
We recommend users to upgrade to TensorFlow 2.2.1 or 2.3.1.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2020-15193'} | 2021-08-26T15:11:03Z | 2020-09-25T18:28:27Z | HIGH | null | {'CWE-908'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15193', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} |
PyPI | PYSEC-2021-685 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can access data outside of bounds of heap allocated array in `tf.raw_ops.UnicodeEncode`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/472c1f12ad9063405737679d4f6bd43094e1d36d/tensorflow/core/kernels/unicode_ops.cc) assumes that the `input_value`/`input_splits` pair specify a valid sparse tensor. 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-2021-29559', 'GHSA-59q2-x2qc-4c97'} | 2021-12-09T06:35:24.947926Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-59q2-x2qc-4c97', 'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298'} | null | {'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298'} | {'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298'} |
PyPI | GHSA-8xjv-v9xq-m5h9 | Moderate severity vulnerability that affects Pillow | Buffer overflow in the ImagingFliDecode function in libImaging/FliDecode.c in Pillow before 3.1.1 allows remote attackers to cause a denial of service (crash) via a crafted FLI file. | {'CVE-2016-0775'} | 2022-03-03T05:13:11.215565Z | 2018-07-24T20:15:36Z | MODERATE | null | {'CWE-119'} | {'https://github.com/python-pillow/Pillow', 'https://github.com/python-pillow/Pillow/commit/893a40850c2d5da41537958e40569c029a6e127b', 'https://security.gentoo.org/glsa/201612-52', 'https://github.com/python-pillow/Pillow/blob/c3cb690fed5d4bf0c45576759de55d054916c165/CHANGES.rst', 'https://github.com/advisories/GHSA-8xjv-v9xq-m5h9', 'http://www.debian.org/security/2016/dsa-3499', 'https://nvd.nist.gov/vuln/detail/CVE-2016-0775'} | null | {'https://github.com/python-pillow/Pillow/commit/893a40850c2d5da41537958e40569c029a6e127b'} | {'https://github.com/python-pillow/Pillow/commit/893a40850c2d5da41537958e40569c029a6e127b'} |
PyPI | PYSEC-2021-390 | null | TensorFlow is an open source platform for machine learning. In affected versions TensorFlow allows tensor to have a large number of dimensions and each dimension can be as large as desired. However, the total number of elements in a tensor must fit within an `int64_t`. If an overflow occurs, `MultiplyWithoutOverflow` would return a negative result. In the majority of TensorFlow codebase this then results in a `CHECK`-failure. Newer constructs exist which return a `Status` instead of crashing the binary. This is similar to CVE-2021-29584. 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-prcg-wp5q-rv7p', 'CVE-2021-41197'} | 2021-11-13T06:52:41.833730Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a871989d7b6c18cdebf2fb4f0e5c5b62fbc19edf', 'https://github.com/tensorflow/tensorflow/issues/46890', 'https://github.com/tensorflow/tensorflow/commit/d81b1351da3e8c884ff836b64458d94e4a157c15', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-prcg-wp5q-rv7p', 'https://github.com/tensorflow/tensorflow/commit/7c1692bd417eb4f9b33ead749a41166d6080af85', 'https://github.com/tensorflow/tensorflow/issues/51908'} | null | {'https://github.com/tensorflow/tensorflow/commit/d81b1351da3e8c884ff836b64458d94e4a157c15', 'https://github.com/tensorflow/tensorflow/commit/7c1692bd417eb4f9b33ead749a41166d6080af85', 'https://github.com/tensorflow/tensorflow/commit/a871989d7b6c18cdebf2fb4f0e5c5b62fbc19edf'} | {'https://github.com/tensorflow/tensorflow/commit/7c1692bd417eb4f9b33ead749a41166d6080af85', 'https://github.com/tensorflow/tensorflow/commit/a871989d7b6c18cdebf2fb4f0e5c5b62fbc19edf', 'https://github.com/tensorflow/tensorflow/commit/d81b1351da3e8c884ff836b64458d94e4a157c15'} |
PyPI | GHSA-pg36-wpm5-g57p | HTTP Request Smuggling: LF vs CRLF handling in Waitress | ### Impact
Waitress implemented a &quot;MAY&quot; part of the RFC7230 (https://tools.ietf.org/html/rfc7230#section-3.5) which states:
Although the line terminator for the start-line and header fields is
the sequence CRLF, a recipient MAY recognize a single LF as a line
terminator and ignore any preceding CR.
Unfortunately if a front-end server does not parse header fields with an LF the same way as it does those with a CRLF it can lead to the front-end and the back-end server parsing the same HTTP message in two different ways. This can lead to a potential for HTTP request smuggling/splitting whereby Waitress may see two requests while the front-end server only sees a single HTTP message.
Example:
```
Content-Length: 100[CRLF]
X-Header: x[LF]Content-Length: 0[CRLF]
```
Would get treated by Waitress as if it were:
```
Content-Length: 100
X-Header: x
Content-Length: 0
```
This could potentially get used by attackers to split the HTTP request and smuggle a second request in the body of the first.
### Patches
This issue is fixed in Waitress 1.4.0. This brings a range of changes to harden Waitress against potential HTTP request confusions, and may change the behaviour of Waitress behind non-conformist proxies.
Waitress no longer implements the MAY part of the specification and instead requires that all lines are terminated correctly with CRLF. If any lines are found with a bare CR or LF a 400 Bad Request is sent back to the requesting entity.
The Pylons Project recommends upgrading as soon as possible, while validating that the changes in Waitress don&#39;t cause any changes in behavior.
### Workarounds
Various reverse proxies may have protections against sending potentially bad HTTP requests to the backend, and or hardening against potential issues like this. If the reverse proxy doesn&#39;t use HTTP/1.1 for connecting to the backend issues are also somewhat mitigated, as HTTP pipelining does not exist in HTTP/1.0 and Waitress will close the connection after every single request (unless the Keep Alive header is explicitly sent... so this is not a fool proof security method)
### Issues/more security issues:
* 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-2019-16785'} | 2022-04-25T23:17:05.279169Z | 2019-12-20T23:03:57Z | HIGH | null | {'CWE-444'} | {'https://github.com/Pylons/waitress/security/advisories/GHSA-pg36-wpm5-g57p', 'https://github.com/Pylons/waitress', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/LYEOTGWJZVKPRXX2HBNVIYWCX73QYPM5/', 'https://nvd.nist.gov/vuln/detail/CVE-2019-16785', 'https://access.redhat.com/errata/RHSA-2020:0720', 'https://github.com/Pylons/waitress/commit/8eba394ad75deaf9e5cd15b78a3d16b12e6b0eba', 'https://www.oracle.com/security-alerts/cpuapr2022.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/GVDHR2DNKCNQ7YQXISJ45NT4IQDX3LJ7/', 'https://docs.pylonsproject.org/projects/waitress/en/latest/#security-fixes'} | null | {'https://github.com/Pylons/waitress/commit/8eba394ad75deaf9e5cd15b78a3d16b12e6b0eba'} | {'https://github.com/Pylons/waitress/commit/8eba394ad75deaf9e5cd15b78a3d16b12e6b0eba'} |
PyPI | GHSA-hp4c-x6r7-6555 | Floating point exception in `SparseDenseCwiseDiv` | ### Impact
The implementation of `tf.raw_ops.SparseDenseCwiseDiv` is vulnerable to a division by 0 error:
```python
import tensorflow as tf
import numpy as np
tf.raw_ops.SparseDenseCwiseDiv(
sp_indices=np.array([[4]]),
sp_values=np.array([-400]),
sp_shape=np.array([647.]),
dense=np.array([0]))
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L56) uses a common class for all binary operations but fails to treat the division by 0 case separately.
### Patches
We have patched the issue in GitHub commit [d9204be9f49520cdaaeb2541d1dc5187b23f31d9](https://github.com/tensorflow/tensorflow/commit/d9204be9f49520cdaaeb2541d1dc5187b23f31d9).
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-2021-37636'} | 2022-03-03T05:12:25.706071Z | 2021-08-25T14:44:14Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/d9204be9f49520cdaaeb2541d1dc5187b23f31d9', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hp4c-x6r7-6555', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37636'} | null | {'https://github.com/tensorflow/tensorflow/commit/d9204be9f49520cdaaeb2541d1dc5187b23f31d9'} | {'https://github.com/tensorflow/tensorflow/commit/d9204be9f49520cdaaeb2541d1dc5187b23f31d9'} |
PyPI | OSV-2021-1809 | Heap-buffer-overflow in ujson.cpython-38-x86_64-linux-gnu.so | OSS-Fuzz report: https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=44973
```
Crash type: Heap-buffer-overflow WRITE 1
Crash state:
ujson.cpython-38-x86_64-linux-gnu.so
ujson.cpython-38-x86_64-linux-gnu.so
ujson.cpython-38-x86_64-linux-gnu.so
```
| null | 2022-04-13T03:04:31.104493Z | 2022-02-25T00:00:25.106722Z | HIGH | null | null | {'https://github.com/ultrajson/ultrajson/commit/550ba4d77294e61597a5259d00769c61281e0042', 'https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=44973'} | {'https://github.com/ultrajson/ultrajson/commit/b9275f7b001da11495040f1332f6c3adf3daa57b'} | {'https://github.com/ultrajson/ultrajson/commit/550ba4d77294e61597a5259d00769c61281e0042'} | {'https://github.com/ultrajson/ultrajson/commit/550ba4d77294e61597a5259d00769c61281e0042'} |
PyPI | PYSEC-2021-543 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `ParseAttrValue`(https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/framework/attr_value_util.cc#L397-L453) can be tricked into stack overflow due to recursion by giving in a specially crafted input. 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-qw5h-7f53-xrp6', 'CVE-2021-29615'} | 2021-12-09T06:35:01.249240Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qw5h-7f53-xrp6', 'https://github.com/tensorflow/tensorflow/commit/e07e1c3d26492c06f078c7e5bf2d138043e199c1'} | null | {'https://github.com/tensorflow/tensorflow/commit/e07e1c3d26492c06f078c7e5bf2d138043e199c1'} | {'https://github.com/tensorflow/tensorflow/commit/e07e1c3d26492c06f078c7e5bf2d138043e199c1'} |
PyPI | GHSA-rgvq-pcvf-hx75 | Heap OOB and null pointer dereference in `RaggedTensorToTensor` | ### Impact
Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty:
```python
import tensorflow as tf
shape = tf.constant([-1, -1], shape=[2], dtype=tf.int64)
values = tf.constant([], shape=[0], dtype=tf.int64)
default_value = tf.constant(404, dtype=tf.int64)
row = tf.constant([269, 404, 0, 0, 0, 0, 0], shape=[7], dtype=tf.int64)
rows = [row]
types = ['ROW_SPLITS']
tf.raw_ops.RaggedTensorToTensor(
shape=shape, values=values, default_value=default_value,
row_partition_tensors=rows, row_partition_types=types)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones.
There are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything.
### Patches
We have patched the issue in GitHub commit [b761c9b652af2107cfbc33efd19be0ce41daa33e](https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e) followed by GitHub commit [f94ef358bb3e91d517446454edff6535bcfe8e4a](https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a) and GitHub commit [c4d7afb6a5986b04505aca4466ae1951686c80f6](https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6).
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-2021-29608'} | 2022-03-03T05:13:06.990646Z | 2021-05-21T14:28:27Z | MODERATE | null | {'CWE-131'} | {'https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e', 'https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rgvq-pcvf-hx75', 'https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29608'} | null | {'https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e', 'https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6', 'https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a'} | {'https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6', 'https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e', 'https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a'} |
PyPI | GHSA-77gp-3h4r-6428 | Out of bounds read and write in Tensorflow | ### Impact
There is a typo in TensorFlow's [`SpecializeType`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/full_type_util.cc#L81-L102) which results in heap OOB read/write:
```cc
for (int i = 0; i < op_def.output_arg_size(); i++) {
// ...
for (int j = 0; j < t->args_size(); j++) {
auto* arg = t->mutable_args(i);
// ...
}
}
```
Due to a typo, `arg` is initialized to the `i`th mutable argument in a loop where the loop index is `j`. Hence it is possible to assign to `arg` from outside the vector of arguments. Since this is a mutable proto value, it allows both read and write to outside of bounds data.
### Patches
We have patched the issue in GitHub commit [0657c83d08845cc434175934c642299de2c0f042](https://github.com/tensorflow/tensorflow/commit/0657c83d08845cc434175934c642299de2c0f042).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, 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-2022-23574'} | 2022-03-03T05:13:11.658649Z | 2022-02-09T23:25:40Z | HIGH | null | {'CWE-787', 'CWE-125'} | {'https://github.com/tensorflow/tensorflow/commit/0657c83d08845cc434175934c642299de2c0f042', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/full_type_util.cc#L81-L102', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-77gp-3h4r-6428', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23574'} | null | {'https://github.com/tensorflow/tensorflow/commit/0657c83d08845cc434175934c642299de2c0f042'} | {'https://github.com/tensorflow/tensorflow/commit/0657c83d08845cc434175934c642299de2c0f042'} |
PyPI | PYSEC-2021-810 | null | TensorFlow is an open source platform for machine learning. In affected versions while calculating the size of the output within the `tf.range` kernel, there is a conditional statement of type `int64 = condition ? int64 : double`. Due to C++ implicit conversion rules, both branches of the condition will be cast to `double` and the result would be truncated before the assignment. This result in overflows. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41202', 'GHSA-xrqm-fpgr-6hhx'} | 2021-12-09T06:35:41.560413Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/46889', 'https://github.com/tensorflow/tensorflow/issues/46912', 'https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xrqm-fpgr-6hhx', 'https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94'} | null | {'https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94', 'https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899'} | {'https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899', 'https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94'} |
PyPI | PYSEC-2020-113 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `tf.raw_ops.Switch` operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is `nullptr`, hence we are binding a reference to `nullptr`. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. In this case, this results in a segmentation fault The issue is patched in commit da8558533d925694483d2c136a9220d6d49d843c, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15190', 'GHSA-4g9f-63rx-5cw4'} | 2020-10-29T16:15:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/da8558533d925694483d2c136a9220d6d49d843c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4g9f-63rx-5cw4', '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/da8558533d925694483d2c136a9220d6d49d843c'} | {'https://github.com/tensorflow/tensorflow/commit/da8558533d925694483d2c136a9220d6d49d843c'} |
PyPI | PYSEC-2022-35 | null | Weblate is a copyleft software web-based continuous localization system. Versions prior to 4.11 do not properly neutralize user input used in user name and language fields. Due to this improper neutralization it is possible to perform cross-site scripting via these fields. The issues were fixed in the 4.11 release. Users unable to upgrade are advised to add their own neutralize logic. | {'GHSA-6jp6-9rf9-gc66', 'CVE-2022-24710'} | 2022-03-08T17:35:37.351702Z | 2022-02-25T21:15:00Z | null | null | null | {'https://github.com/WeblateOrg/weblate/commit/9e19a8414337692cc90da2a91c9af5420f2952f1', 'https://github.com/WeblateOrg/weblate/commit/22d577b1f1e88665a88b4569380148030e0f8389', 'https://github.com/WeblateOrg/weblate/security/advisories/GHSA-6jp6-9rf9-gc66', 'https://github.com/WeblateOrg/weblate/commit/f6753a1a1c63fade6ad418fbda827c6750ab0bda'} | null | {'https://github.com/WeblateOrg/weblate/commit/22d577b1f1e88665a88b4569380148030e0f8389', 'https://github.com/WeblateOrg/weblate/commit/9e19a8414337692cc90da2a91c9af5420f2952f1', 'https://github.com/WeblateOrg/weblate/commit/f6753a1a1c63fade6ad418fbda827c6750ab0bda'} | {'https://github.com/WeblateOrg/weblate/commit/22d577b1f1e88665a88b4569380148030e0f8389', 'https://github.com/WeblateOrg/weblate/commit/9e19a8414337692cc90da2a91c9af5420f2952f1', 'https://github.com/WeblateOrg/weblate/commit/f6753a1a1c63fade6ad418fbda827c6750ab0bda'} |
PyPI | PYSEC-2019-155 | null | python-dbusmock before version 0.15.1 AddTemplate() D-Bus method call or DBusTestCase.spawn_server_template() method could be tricked into executing malicious code if an attacker supplies a .pyc file. | {'GHSA-74xw-82v7-hmrm', 'CVE-2015-1326'} | 2021-07-05T00:01:25.330872Z | 2019-04-22T16:29:00Z | null | null | null | {'https://github.com/martinpitt/python-dbusmock/commit/4e7d0df9093', 'https://github.com/advisories/GHSA-74xw-82v7-hmrm'} | null | {'https://github.com/martinpitt/python-dbusmock/commit/4e7d0df9093'} | {'https://github.com/martinpitt/python-dbusmock/commit/4e7d0df9093'} |
PyPI | GHSA-vfr4-x8j2-3rf9 | Division by zero in TFLite's implementation of `TransposeConv` | ### Impact
The optimized implementation of the `TransposeConv` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L5221-L5222):
```cc
int height_col = (height + pad_t + pad_b - filter_h) / stride_h + 1;
int width_col = (width + pad_l + pad_r - filter_w) / stride_w + 1;
```
An attacker can craft a model such that `stride_{h,w}` values are 0. Code calling this function must validate these arguments.
### Patches
We have patched the issue in GitHub commit [801c1c6be5324219689c98e1bd3e0ca365ee834d](https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d).
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-2021-29588'} | 2022-03-03T05:13:23.243455Z | 2021-05-21T14:26:48Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vfr4-x8j2-3rf9', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29588'} | null | {'https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d'} | {'https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d'} |
PyPI | PYSEC-2021-406 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` has an undefined behavior due to binding a reference to `nullptr`. 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-vwhq-49r4-gj9v', 'CVE-2021-41214'} | 2021-11-13T06:52:44.328170Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vwhq-49r4-gj9v'} | null | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} |
PyPI | PYSEC-2021-778 | 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.UnicodeEncode`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unicode_ops.cc#L533-L539) reads the first dimension of the `input_splits` tensor before validating that this tensor is not empty. We have patched the issue in GitHub commit 2e0ee46f1a47675152d3d865797a18358881d7a6. 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-2021-37667', 'GHSA-w74j-v8xh-3w5h'} | 2021-12-09T06:35:38.165715Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/2e0ee46f1a47675152d3d865797a18358881d7a6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w74j-v8xh-3w5h'} | null | {'https://github.com/tensorflow/tensorflow/commit/2e0ee46f1a47675152d3d865797a18358881d7a6'} | {'https://github.com/tensorflow/tensorflow/commit/2e0ee46f1a47675152d3d865797a18358881d7a6'} |
PyPI | PYSEC-2021-282 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if `x` and `v` are empty but the code uses `||` instead of `&&`. We have patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618. 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-cm5x-837x-jf3c', 'CVE-2021-37660'} | 2021-08-27T03:22:44.908068Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cm5x-837x-jf3c', 'https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618'} | null | {'https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618'} | {'https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618'} |
PyPI | PYSEC-2020-328 | null | In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. | {'CVE-2020-15213', 'GHSA-hjmq-236j-8m87'} | 2021-12-09T06:35:15.598802Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87', 'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} |
PyPI | PYSEC-2020-282 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments. However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used. In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. The issue is patched in commits 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15202', 'GHSA-h6fg-mjxg-hqq4'} | 2021-12-09T06:34:41.876848Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/27b417360cbd671ef55915e4bb6bb06af8b8a832', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/ca8c013b5e97b1373b3bb1c97ea655e69f31a575', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6fg-mjxg-hqq4', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/ca8c013b5e97b1373b3bb1c97ea655e69f31a575', 'https://github.com/tensorflow/tensorflow/commit/27b417360cbd671ef55915e4bb6bb06af8b8a832'} | {'https://github.com/tensorflow/tensorflow/commit/ca8c013b5e97b1373b3bb1c97ea655e69f31a575', 'https://github.com/tensorflow/tensorflow/commit/27b417360cbd671ef55915e4bb6bb06af8b8a832'} |
PyPI | GHSA-gpfh-jvf9-7wg5 | Use after free / memory leak in `CollectiveReduceV2` | ### Impact
The [async implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/collective_ops.cc#L604-L615) of `CollectiveReduceV2` suffers from a memory leak and a use after free:
```python
import tensorflow as tf
tf.raw_ops.CollectiveReduceV2(
input=[],
group_size=[-10, -10, -10],
group_key=[-10, -10],
instance_key=[-10],
ordering_token=[],
merge_op='Mul',
final_op='Div')
```
This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed:
```cc
auto done_with_cleanup = [col_params, done = std::move(done)]() {
done();
col_params->Unref();
};
OP_REQUIRES_OK_ASYNC(c,
FillCollectiveParams(col_params, REDUCTION_COLLECTIVE,
/*group_size*/ c->input(1),
/*group_key*/ c->input(2),
/*instance_key*/ c->input(3)),
done);
```
Here, `done` is already moved from by the time `OP_REQUIRES_OK_ASYNC` macro needs to invoke it in case of errors. In this case, we get an undefined behavior, which can manifest via crashes, `std::bad_alloc` throws or just memory leaks.
### Patches
We have patched the issue in GitHub commit [ca38dab9d3ee66c5de06f11af9a4b1200da5ef75](https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.
### 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-2021-41220'} | 2021-11-08T22:07:11Z | 2021-11-10T18:51:21Z | HIGH | null | {'CWE-416'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5', 'https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41220'} | null | {'https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75'} | {'https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75'} |
PyPI | PYSEC-2021-797 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for [ellipsis in axis definition](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/strided_slice.cc#L103-L122). An attacker can craft a model such that `ellipsis_end_idx` is smaller than `i` (e.g., always negative). In this case, the inner loop does not increase `i` and the `continue` statement causes execution to skip over the preincrement at the end of the outer loop. We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695. TensorFlow 2.6.0 is the only affected version. | {'CVE-2021-37686', 'GHSA-mhhc-q96p-mfm9'} | 2021-12-09T06:35:39.861916Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mhhc-q96p-mfm9', 'https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695'} | null | {'https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695'} | {'https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695'} |
PyPI | GHSA-rf3h-xgv5-2q39 | Division by zero in TFLite's implementation of `DepthwiseConv` | ### Impact
The implementation of the `DepthwiseConv` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/depthwise_conv.cc#L287-L288):
```cc
int num_input_channels = SizeOfDimension(input, 3);
TF_LITE_ENSURE_EQ(context, num_filter_channels % num_input_channels, 0);
```
An attacker can craft a model such that `input`'s fourth dimension would be 0.
### Patches
We have patched the issue in GitHub commit [cbda3c6b2dbbd3fbdc482ff8c0170a78ec2e97d0](https://github.com/tensorflow/tensorflow/commit/cbda3c6b2dbbd3fbdc482ff8c0170a78ec2e97d0).
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-2021-29602'} | 2022-03-03T05:13:41.084874Z | 2021-05-21T14:28:12Z | LOW | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29602', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rf3h-xgv5-2q39', 'https://github.com/tensorflow/tensorflow/commit/cbda3c6b2dbbd3fbdc482ff8c0170a78ec2e97d0'} | null | {'https://github.com/tensorflow/tensorflow/commit/cbda3c6b2dbbd3fbdc482ff8c0170a78ec2e97d0'} | {'https://github.com/tensorflow/tensorflow/commit/cbda3c6b2dbbd3fbdc482ff8c0170a78ec2e97d0'} |
PyPI | PYSEC-2020-144 | null | In Tortoise ORM before versions 0.15.23 and 0.16.6, various forms of SQL injection have been found for MySQL and when filtering or doing mass-updates on char/text fields. SQLite & PostgreSQL are only affected when filtering with contains, starts_with, or ends_with filters (and their case-insensitive counterparts). | {'CVE-2020-11010', 'GHSA-9j2c-x8qm-qmjq'} | 2020-04-28T17:16:00Z | 2020-04-20T22:15:00Z | null | null | null | {'https://github.com/tortoise/tortoise-orm/commit/91c364053e0ddf77edc5442914c6f049512678b3', 'https://github.com/tortoise/tortoise-orm/security/advisories/GHSA-9j2c-x8qm-qmjq'} | null | {'https://github.com/tortoise/tortoise-orm/commit/91c364053e0ddf77edc5442914c6f049512678b3'} | {'https://github.com/tortoise/tortoise-orm/commit/91c364053e0ddf77edc5442914c6f049512678b3'} |
PyPI | PYSEC-2021-847 | null | TensorFlow is an open source platform for machine learning. In affected versions several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues. These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41206', 'GHSA-pgcq-h79j-2f69'} | 2021-12-13T06:20:52.886778Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/e7f497570abb6b4ae5af4970620cd880e4c0c904', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pgcq-h79j-2f69', 'https://github.com/tensorflow/tensorflow/commit/4d74d8a00b07441cba090a02e0dd9ed385145bf4', 'https://github.com/tensorflow/tensorflow/commit/68422b215e618df5ad375bcdc6d2052e9fd3080a', 'https://github.com/tensorflow/tensorflow/commit/da4aad5946be30e5f049920fa076e1f7ef021261', 'https://github.com/tensorflow/tensorflow/commit/579261dcd446385831fe4f7457d802a59685121d', 'https://github.com/tensorflow/tensorflow/commit/4dddb2fd0b01cdd196101afbba6518658a2c9e07'} | null | {'https://github.com/tensorflow/tensorflow/commit/68422b215e618df5ad375bcdc6d2052e9fd3080a', 'https://github.com/tensorflow/tensorflow/commit/e7f497570abb6b4ae5af4970620cd880e4c0c904', 'https://github.com/tensorflow/tensorflow/commit/4dddb2fd0b01cdd196101afbba6518658a2c9e07', 'https://github.com/tensorflow/tensorflow/commit/da4aad5946be30e5f049920fa076e1f7ef021261', 'https://github.com/tensorflow/tensorflow/commit/4d74d8a00b07441cba090a02e0dd9ed385145bf4', 'https://github.com/tensorflow/tensorflow/commit/579261dcd446385831fe4f7457d802a59685121d'} | {'https://github.com/tensorflow/tensorflow/commit/4d74d8a00b07441cba090a02e0dd9ed385145bf4', 'https://github.com/tensorflow/tensorflow/commit/4dddb2fd0b01cdd196101afbba6518658a2c9e07', 'https://github.com/tensorflow/tensorflow/commit/68422b215e618df5ad375bcdc6d2052e9fd3080a', 'https://github.com/tensorflow/tensorflow/commit/579261dcd446385831fe4f7457d802a59685121d', 'https://github.com/tensorflow/tensorflow/commit/e7f497570abb6b4ae5af4970620cd880e4c0c904', 'https://github.com/tensorflow/tensorflow/commit/da4aad5946be30e5f049920fa076e1f7ef021261'} |
PyPI | PYSEC-2017-2 | null | The user module in ansible before 1.6.6 allows remote authenticated users to execute arbitrary commands. | {'CVE-2014-3498'} | 2021-07-02T02:41:33.064199Z | 2017-06-08T18:29:00Z | null | null | null | {'https://github.com/ansible/ansible/commit/8ed6350e65c82292a631f08845dfaacffe7f07f5', 'https://bugzilla.redhat.com/show_bug.cgi?id=1335551'} | null | {'https://github.com/ansible/ansible/commit/8ed6350e65c82292a631f08845dfaacffe7f07f5'} | {'https://github.com/ansible/ansible/commit/8ed6350e65c82292a631f08845dfaacffe7f07f5'} |
PyPI | PYSEC-2021-514 | null | TensorFlow is an end-to-end open source platform for machine learning. Optimized pooling implementations in TFLite fail to check that the stride arguments are not 0 before calling `ComputePaddingHeightWidth`(https://github.com/tensorflow/tensorflow/blob/3f24ccd932546416ec906a02ddd183b48a1d2c83/tensorflow/lite/kernels/pooling.cc#L90). Since users can craft special models which will have `params->stride_{height,width}` be zero, this will result in a division by zero. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29586', 'GHSA-26j7-6w8w-7922'} | 2021-12-09T06:34:56.717383Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-26j7-6w8w-7922', 'https://github.com/tensorflow/tensorflow/commit/5f7975d09eac0f10ed8a17dbb6f5964977725adc'} | null | {'https://github.com/tensorflow/tensorflow/commit/5f7975d09eac0f10ed8a17dbb6f5964977725adc'} | {'https://github.com/tensorflow/tensorflow/commit/5f7975d09eac0f10ed8a17dbb6f5964977725adc'} |
PyPI | GHSA-4952-p58q-6crx | JupyterLab: XSS due to lack of sanitization of the action attribute of an html <form> | ### Impact
Untrusted notebook can execute code on load. This is a remote code execution, but requires user action to open a notebook.
### Patches
Patched in the following versions: 3.1.4, 3.0.17, 2.3.2, 2.2.10, 1.2.21.
### References
[OWASP Page on Restricting Form Submissions](https://cheatsheetseries.owasp.org/cheatsheets/Content_Security_Policy_Cheat_Sheet.html)
### For more information
If you have any questions or comments about this advisory, or vulnerabilities to report, please email our security list security@ipython.org.
Credit: Guillaume Jeanne from Google
| {'CVE-2021-32797'} | 2022-04-08T22:00:13.419205Z | 2021-08-23T19:40:22Z | HIGH | null | {'CWE-87', 'CWE-79', 'CWE-75'} | {'https://github.com/jupyterlab/jupyterlab/security/advisories/GHSA-4952-p58q-6crx', 'https://nvd.nist.gov/vuln/detail/CVE-2021-32797', 'https://github.com/jupyterlab/jupyterlab/commit/504825938c0abfa2fb8ff8d529308830a5ae42ed', 'https://github.com/google/security-research/security/advisories/GHSA-c469-p3jp-2vhx'} | null | {'https://github.com/jupyterlab/jupyterlab/commit/504825938c0abfa2fb8ff8d529308830a5ae42ed'} | {'https://github.com/jupyterlab/jupyterlab/commit/504825938c0abfa2fb8ff8d529308830a5ae42ed'} |
PyPI | PYSEC-2021-451 | 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.AddManySparseToTensorsMap`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/kernels/sparse_tensors_map_ops.cc#L257) takes the values specified in `sparse_shape` as dimensions for the output shape. The `TensorShape` constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when `InitDims`(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. 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-2cpx-427x-q2c6', 'CVE-2021-29523'} | 2021-12-09T06:34:46.920888Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2cpx-427x-q2c6'} | null | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c'} | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c'} |
PyPI | GHSA-xqxm-2rpm-3889 | Comment reply notifications sent to incorrect users | ### Impact
When notifications for new replies in comment threads are sent, they are sent to all users who have replied or commented anywhere on the site, rather than only in the relevant threads. This means that a user could listen in to new comment replies on pages they have not had editing access to, as long as they have left a comment or reply somewhere on the site.
### Patches
A patched version has been released as Wagtail 2.15.2 (for the current LTS), which restores the intended behaviour - to send notifications for new replies to the participants in the active thread only (editing permissions are not considered).
### Workarounds
New comments can be disabled by setting `WAGTAILADMIN_COMMENTS_ENABLED = False` in the Django settings file.
### Acknowledgements
Many thanks to Ihor Marhitych for identifying this issue.
### For more information
If you have any questions or comments about this advisory:
* Visit Wagtail's [support channels](https://docs.wagtail.io/en/stable/support.html)
* Email us at security@wagtail.io (if you wish to send encrypted email, the public key ID is `0x6ba1e1a86e0f8ce8`)
| {'CVE-2022-21683'} | 2022-03-03T05:14:03.080144Z | 2022-01-21T23:43:50Z | LOW | null | {'CWE-200'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-21683', 'https://github.com/wagtail/wagtail/releases/tag/v2.15.2', 'https://github.com/wagtail/wagtail/security/advisories/GHSA-xqxm-2rpm-3889', 'https://github.com/wagtail/wagtail/commit/5fe901e5d86ed02dbbb63039a897582951266afd', 'https://github.com/wagtail/wagtail'} | null | {'https://github.com/wagtail/wagtail/commit/5fe901e5d86ed02dbbb63039a897582951266afd'} | {'https://github.com/wagtail/wagtail/commit/5fe901e5d86ed02dbbb63039a897582951266afd'} |
PyPI | PYSEC-2019-102 | null | Eval injection in the Math plugin of Limnoria (before 2019.11.09) and Supybot (through 2018-05-09) allows remote unprivileged attackers to disclose information or possibly have unspecified other impact via the calc and icalc IRC commands. | {'CVE-2019-19010', 'GHSA-6g88-vr3v-76mf'} | 2020-08-24T17:37:00Z | 2019-11-16T01:15:00Z | null | null | null | {'https://github.com/ProgVal/Limnoria/commit/3848ae78de45b35c029cc333963d436b9d2f0a35', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/5P2AGND54UIJV3WHOYO2YINIXSDGAAPO/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/54CQM2TEXRADLE77VOMCPHL5PBHR3ZWJ/', 'https://github.com/ProgVal/Limnoria/wiki/math-eval-vulnerability', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DRNOUHFEN75QAIKT4Y3HDN3TT5LSIWN2/', 'https://github.com/advisories/GHSA-6g88-vr3v-76mf'} | null | {'https://github.com/ProgVal/Limnoria/commit/3848ae78de45b35c029cc333963d436b9d2f0a35'} | {'https://github.com/ProgVal/Limnoria/commit/3848ae78de45b35c029cc333963d436b9d2f0a35'} |
PyPI | PYSEC-2022-62 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseCountSparseOutput` can be made to crash a TensorFlow process by an integer overflow whose result is then used in a memory allocation. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-x4qx-4fjv-hmw6', 'CVE-2022-21738'} | 2022-03-09T00:17:31.553710Z | 2022-02-03T14:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/6f4d3e8139ec724dbbcb40505891c81dd1052c4a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4qx-4fjv-hmw6', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/count_ops.cc#L168-L273'} | null | {'https://github.com/tensorflow/tensorflow/commit/6f4d3e8139ec724dbbcb40505891c81dd1052c4a'} | {'https://github.com/tensorflow/tensorflow/commit/6f4d3e8139ec724dbbcb40505891c81dd1052c4a'} |
PyPI | PYSEC-2020-294 | null | In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a write out bounds / segmentation fault if the segment ids are not sorted. Code assumes that the segment ids are in increasing order, using the last element of the tensor holding them to determine the dimensionality of output tensor. This results in allocating insufficient memory for the output tensor and in a write outside the bounds of the output array. This usually results in a segmentation fault, but depending on runtime conditions it can provide for a write gadget to be used in future memory corruption-based exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are sorted, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. | {'CVE-2020-15214', 'GHSA-p2cq-cprg-frvm'} | 2021-12-09T06:34:43.930382Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2cq-cprg-frvm', 'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} |
PyPI | PYSEC-2021-294 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.SdcaOptimizerV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of `example_labels` is the same as the number of examples. We have patched the issue in GitHub commit a4e138660270e7599793fa438cd7b2fc2ce215a6. 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-5hj3-vjjf-f5m7', 'CVE-2021-37672'} | 2021-08-27T03:22:46.024313Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hj3-vjjf-f5m7', 'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} | null | {'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} | {'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} |
PyPI | GHSA-7v94-64hj-m82h | FPE in `ParallelConcat` | ### Impact
The [implementation of `ParallelConcat`](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/inplace_ops.cc#L72-L97) misses some input validation and can produce a division by 0:
```python
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.ParallelConcat(values=[['tf']],shape=0)
return y
test()
```
### 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-2021-41207'} | 2022-03-03T05:14:00.378314Z | 2021-11-10T19:02:57Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7v94-64hj-m82h', 'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41207'} | null | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} |
PyPI | GHSA-v2wc-pfq2-5cm6 | Possible XSS attack in Wagtail | ### Impact
A cross-site scripting (XSS) vulnerability exists on the page revision comparison view within the Wagtail admin interface. A user with a limited-permission editor account for the Wagtail admin could potentially craft a page revision history that, when viewed by a user with higher privileges, could perform actions with that user's credentials. The vulnerability is not exploitable by an ordinary site visitor without access to the Wagtail admin.
### Patches
Patched versions have been released as Wagtail 2.7.2 (for the LTS 2.7 branch) and Wagtail 2.8.1 (for the current 2.8 branch).
### Workarounds
Site owners who are unable to upgrade to the new versions can disable the revision comparison view by adding the following URL route to the top of their project's `urls.py` configuration:
from django.views.generic.base import RedirectView
urlpatterns = [
url(r'^admin/pages/(\d+)/revisions/compare/', RedirectView.as_view(url='/admin/')),
# ...
]
### Acknowledgements
Many thanks to Vlad Gerasimenko for reporting this issue.
### For more information
If you have any questions or comments about this advisory:
* Visit Wagtail's [support channels](https://docs.wagtail.io/en/stable/support.html)
* Email us at [security@wagtail.io](mailto:security@wagtail.io) (if you wish to send encrypted email, the public key ID is `0x6ba1e1a86e0f8ce8`) | {'CVE-2020-11001'} | 2022-03-03T05:14:12.582949Z | 2020-04-14T23:09:29Z | MODERATE | null | {'CWE-80'} | {'https://github.com/wagtail/wagtail/releases/tag/v2.8.1', 'https://nvd.nist.gov/vuln/detail/CVE-2020-11001', 'https://github.com/wagtail/wagtail/security/advisories/GHSA-v2wc-pfq2-5cm6', 'https://github.com/wagtail/wagtail/commit/61045ceefea114c40ac4b680af58990dbe732389'} | null | {'https://github.com/wagtail/wagtail/commit/61045ceefea114c40ac4b680af58990dbe732389'} | {'https://github.com/wagtail/wagtail/commit/61045ceefea114c40ac4b680af58990dbe732389'} |
PyPI | GHSA-828x-qc2p-wprq | Undefined behavior in `MaxPool3DGradGrad` | ### Impact
The implementation of `tf.raw_ops.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors:
```python
import tensorflow as tf
orig_input = tf.constant([0.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
orig_output = tf.constant([0.0], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
grad = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)
ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.MaxPool3DGradGrad(
orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,
strides=strides, padding=padding)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer.
### Patches
We have patched the issue in GitHub commit [a3d9f9be9ac2296615644061b40cefcee341dcc4](https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4).
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-2021-29574'} | 2022-03-03T05:11:38.042452Z | 2021-05-21T14:26:10Z | LOW | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29574', 'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-828x-qc2p-wprq'} | null | {'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4'} | {'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4'} |
PyPI | PYSEC-2021-64 | null | django-filter is a generic system for filtering Django QuerySets based on user selections. In django-filter before version 2.4.0, automatically generated `NumberFilter` instances, whose value was later converted to an integer, were subject to potential DoS from maliciously input using exponential format with sufficiently large exponents. Version 2.4.0+ applies a `MaxValueValidator` with a a default `limit_value` of 1e50 to the form field used by `NumberFilter` instances. In addition, `NumberFilter` implements the new `get_max_validator()` which should return a configured validator instance to customise the limit, or else `None` to disable the additional validation. Users may manually apply an equivalent validator if they are not able to upgrade. | {'GHSA-x7gm-rfgv-w973', 'CVE-2020-15225'} | 2021-05-10T17:55:00Z | 2021-04-29T21:15:00Z | null | null | null | {'https://github.com/carltongibson/django-filter/security/advisories/GHSA-x7gm-rfgv-w973', 'https://github.com/carltongibson/django-filter/commit/340cf7a23a2b3dcd7183f6a0d6c383e85b130d2b', 'https://github.com/carltongibson/django-filter/releases/tag/2.4.0', 'https://pypi.org/project/django-filter/'} | null | {'https://github.com/carltongibson/django-filter/commit/340cf7a23a2b3dcd7183f6a0d6c383e85b130d2b'} | {'https://github.com/carltongibson/django-filter/commit/340cf7a23a2b3dcd7183f6a0d6c383e85b130d2b'} |
PyPI | PYSEC-2021-781 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.UpperBound`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/searchsorted_op.cc#L85-L104) does not validate the rank of `sorted_input` argument. A similar issue occurs in `tf.raw_ops.LowerBound`. We have patched the issue in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38. 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-9697-98pf-4rw7', 'CVE-2021-37670'} | 2021-12-09T06:35:38.435010Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9697-98pf-4rw7', 'https://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38'} | null | {'https://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38'} | {'https://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38'} |
PyPI | GHSA-5gm3-px64-rw72 | Uncontrolled Resource Consumption in Pillow | There is a DoS vulnerability in Pillow before 6.2.2 caused by FpxImagePlugin.py calling the range function on an unvalidated 32-bit integer if the number of bands is large. On Windows running 32-bit Python, this results in an OverflowError or MemoryError due to the 2 GB limit. However, on Linux running 64-bit Python this results in the process being terminated by the OOM killer. | {'CVE-2019-19911'} | 2022-03-03T05:13:26.301897Z | 2020-04-01T16:36:44Z | HIGH | null | {'CWE-190'} | {'https://github.com/python-pillow/Pillow/commit/774e53bb132461d8d5ebefec1162e29ec0ebc63d', 'https://github.com/python-pillow/Pillow/blob/master/CHANGES.rst#622-2020-01-02', 'https://nvd.nist.gov/vuln/detail/CVE-2019-19911', 'https://www.debian.org/security/2020/dsa-4631', 'https://usn.ubuntu.com/4272-1/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/3DUMIBUYGJRAVJCTFUWBRLVQKOUTVX5P/', 'https://pillow.readthedocs.io/en/stable/releasenotes/6.2.2.html'} | null | {'https://github.com/python-pillow/Pillow/commit/774e53bb132461d8d5ebefec1162e29ec0ebc63d'} | {'https://github.com/python-pillow/Pillow/commit/774e53bb132461d8d5ebefec1162e29ec0ebc63d'} |
PyPI | GHSA-9236-8w7q-rmrv | archivy is vulnerable to Cross-Site Request Forgery (CSRF) | archivy is vulnerable to Cross-Site Request Forgery (CSRF). There is [a fix](https://github.com/archivy/archivy/commit/796c3ae318eea183fc88c87ec5a27355b0f6a99d) available in the master branch. | {'CVE-2021-4162'} | 2022-03-03T05:13:53.359632Z | 2022-01-06T21:59:50Z | MODERATE | null | {'CWE-352'} | {'https://github.com/archivy/archivy/commit/796c3ae318eea183fc88c87ec5a27355b0f6a99d', 'https://huntr.dev/bounties/e204a768-2129-4b6f-abad-e436309c7c32', 'https://nvd.nist.gov/vuln/detail/CVE-2021-4162', 'https://github.com/archivy/archivy/'} | null | {'https://github.com/archivy/archivy/commit/796c3ae318eea183fc88c87ec5a27355b0f6a99d'} | {'https://github.com/archivy/archivy/commit/796c3ae318eea183fc88c87ec5a27355b0f6a99d'} |
PyPI | GHSA-6g88-vr3v-76mf | Eval injection in Supybot/Limnoria | Eval injection in the Math plugin of Limnoria (before 2019.11.09) and Supybot (through 2018-05-09) allows remote unprivileged attackers to disclose information or possibly have unspecified other impact via the calc and icalc IRC commands. | {'CVE-2019-19010'} | 2022-03-03T05:13:38.000662Z | 2019-11-20T01:31:31Z | CRITICAL | null | {'CWE-94'} | {'https://github.com/ProgVal/Limnoria/commit/3848ae78de45b35c029cc333963d436b9d2f0a35', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/5P2AGND54UIJV3WHOYO2YINIXSDGAAPO/', 'https://nvd.nist.gov/vuln/detail/CVE-2019-19010', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/54CQM2TEXRADLE77VOMCPHL5PBHR3ZWJ/', 'https://github.com/ProgVal/Limnoria/wiki/math-eval-vulnerability', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DRNOUHFEN75QAIKT4Y3HDN3TT5LSIWN2/'} | null | {'https://github.com/ProgVal/Limnoria/commit/3848ae78de45b35c029cc333963d436b9d2f0a35'} | {'https://github.com/ProgVal/Limnoria/commit/3848ae78de45b35c029cc333963d436b9d2f0a35'} |
PyPI | PYSEC-2021-152 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixDiag*` operations(https://github.com/tensorflow/tensorflow/blob/4c4f420e68f1cfaf8f4b6e8e3eb857e9e4c3ff33/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L195-L197) does not validate that the tensor arguments are non-empty. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29515', 'GHSA-hc6c-75p4-hmq4'} | 2021-08-27T03:22:24.038004Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hc6c-75p4-hmq4'} | null | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6'} | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6'} |
PyPI | PYSEC-2021-502 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors. The implementation(https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null pointer. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-828x-qc2p-wprq', 'CVE-2021-29574'} | 2021-12-09T06:34:54.849798Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-828x-qc2p-wprq'} | null | {'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4'} | {'https://github.com/tensorflow/tensorflow/commit/a3d9f9be9ac2296615644061b40cefcee341dcc4'} |
PyPI | PYSEC-2021-851 | null | Flask-AppBuilder is a development framework built on top of Flask. Verions prior to 3.3.4 contain an improper authentication vulnerability in the REST API. The issue allows for a malicious actor with a carefully crafted request to successfully authenticate and gain access to existing protected REST API endpoints. This only affects non database authentication types and new REST API endpoints. Users should upgrade to Flask-AppBuilder 3.3.4 to receive a patch. | {'CVE-2021-41265', 'GHSA-m3rf-7m4w-r66q'} | 2021-12-15T19:23:46.849981Z | 2021-12-09T17:15:00Z | null | null | null | {'https://github.com/dpgaspar/Flask-AppBuilder/releases/tag/v3.3.4', 'https://github.com/dpgaspar/Flask-AppBuilder/security/advisories/GHSA-m3rf-7m4w-r66q', 'https://github.com/dpgaspar/Flask-AppBuilder/commit/eba517aab121afa3f3f2edb011ec6bc4efd61fbc'} | null | {'https://github.com/dpgaspar/Flask-AppBuilder/commit/eba517aab121afa3f3f2edb011ec6bc4efd61fbc'} | {'https://github.com/dpgaspar/Flask-AppBuilder/commit/eba517aab121afa3f3f2edb011ec6bc4efd61fbc'} |
PyPI | PYSEC-2020-152 | null | In Wagtail before versions 2.8.1 and 2.7.2, a cross-site scripting (XSS) vulnerability exists on the page revision comparison view within the Wagtail admin interface. A user with a limited-permission editor account for the Wagtail admin could potentially craft a page revision history that, when viewed by a user with higher privileges, could perform actions with that user's credentials. The vulnerability is not exploitable by an ordinary site visitor without access to the Wagtail admin. Patched versions have been released as Wagtail 2.7.2 (for the LTS 2.7 branch) and Wagtail 2.8.1 (for the current 2.8 branch). | {'GHSA-v2wc-pfq2-5cm6', 'CVE-2020-11001'} | 2020-04-15T19:15:00Z | 2020-04-14T23:15:00Z | null | null | null | {'https://github.com/wagtail/wagtail/releases/tag/v2.8.1', 'https://github.com/wagtail/wagtail/security/advisories/GHSA-v2wc-pfq2-5cm6', 'https://github.com/wagtail/wagtail/commit/61045ceefea114c40ac4b680af58990dbe732389'} | null | {'https://github.com/wagtail/wagtail/commit/61045ceefea114c40ac4b680af58990dbe732389'} | {'https://github.com/wagtail/wagtail/commit/61045ceefea114c40ac4b680af58990dbe732389'} |
PyPI | PYSEC-2018-20 | null | privacyIDEA version 2.23.1 and earlier contains a Improper Input Validation vulnerability in token validation api that can result in Denial-of-Service. This attack appear to be exploitable via http request with user=<space>&pass= to /validate/check url. This vulnerability appears to have been fixed in 2.23.2. | {'GHSA-7qqv-r2q4-jxhm', 'CVE-2018-1000809'} | 2021-06-10T06:51:13.416740Z | 2018-10-08T15:29:00Z | null | null | null | {'https://github.com/privacyidea/privacyidea/issues/1227', 'https://github.com/privacyidea/privacyidea/commit/a3edc09beffa2104f357fe24971ea3211ce40751', 'https://github.com/advisories/GHSA-7qqv-r2q4-jxhm'} | null | {'https://github.com/privacyidea/privacyidea/commit/a3edc09beffa2104f357fe24971ea3211ce40751'} | {'https://github.com/privacyidea/privacyidea/commit/a3edc09beffa2104f357fe24971ea3211ce40751'} |
PyPI | GHSA-g67g-hvc3-xmvf | Inconsistent input sanitisation leads to XSS vectors | ### Background
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 XSS possibilities with specially crafted input to a variety of fields.
### Impact
OMERO.web before 5.11.0 and OMERO.figure before 4.4.1.
### Patches
Users should upgrade OMERO.web to 5.11.0 or higher and OMERO.figure to 4.4.1 or higher. | {'CVE-2021-41132'} | 2022-03-22T21:32:00.913617Z | 2021-10-14T21:19:23Z | CRITICAL | null | {'CWE-79', 'CWE-116'} | {'https://github.com/ome/omero-web/security/advisories/GHSA-g67g-hvc3-xmvf', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41132', 'https://github.com/ome/omero-web', 'https://www.openmicroscopy.org/security/advisories/2021-SV3/', 'https://github.com/ome/omero-web/commit/0168067accde5e635341b3c714b1d53ae92ba424'} | null | {'https://github.com/ome/omero-web/commit/0168067accde5e635341b3c714b1d53ae92ba424'} | {'https://github.com/ome/omero-web/commit/0168067accde5e635341b3c714b1d53ae92ba424'} |
PyPI | PYSEC-2022-74 | null | Tensorflow is an Open Source Machine Learning Framework. An attacker can trigger denial of service via assertion failure by altering a `SavedModel` on disk such that `AttrDef`s of some operation are duplicated. 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-2022-23565', 'GHSA-4v5p-v5h9-6xjx'} | 2022-03-09T00:17:33.047887Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4v5p-v5h9-6xjx'} | null | {'https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0'} | {'https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0'} |
PyPI | PYSEC-2012-1 | null | Beaker before 1.6.4, when using PyCrypto to encrypt sessions, uses AES in ECB cipher mode, which might allow remote attackers to obtain portions of sensitive session data via unspecified vectors. | {'CVE-2012-3458'} | 2021-07-05T00:01:17.212849Z | 2012-09-15T17:55:00Z | null | null | null | {'http://secunia.com/advisories/50520', 'http://www.debian.org/security/2012/dsa-2541', 'http://www.openwall.com/lists/oss-security/2012/08/13/10', 'http://secunia.com/advisories/50226', 'https://github.com/bbangert/beaker/commit/91becae76101cf87ce8cbfabe3af2622fc328fe5', 'https://bugzilla.redhat.com/show_bug.cgi?id=809267'} | null | {'https://github.com/bbangert/beaker/commit/91becae76101cf87ce8cbfabe3af2622fc328fe5'} | {'https://github.com/bbangert/beaker/commit/91becae76101cf87ce8cbfabe3af2622fc328fe5'} |
PyPI | PYSEC-2021-447 | null | TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. 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-772j-h9xw-ffp5', 'CVE-2021-29519'} | 2021-12-09T06:34:46.373650Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772j-h9xw-ffp5'} | null | {'https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025'} | {'https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025'} |
PyPI | GHSA-xr38-w74q-r8jv | Permissions not properly checked in Invenio-Drafts-Resources | ### Impact
Invenio-Drafts-Resources does not properly check permissions when a record is published. The vulnerability is exploitable in a default installation of InvenioRDM. An authenticated user is able via REST API calls to publish draft records of other users if they know the record identifier and the draft validates (e.g. all require fields filled out). An attacker is not able to modify the data in the record, and thus e.g. *cannot* change a record from restricted to public.
### Details
The service's ``publish()`` method contains the following permission check:
```python
def publish(..):
self.require_permission(identity, "publish")
```
However, the record should have been passed into the permission check so that the need generators have access to e.g. the record owner.
```python
def publish(..):
self.require_permission(identity, "publish", record=record)
```
The bug is activated in Invenio-RDM-Records which has a need generator called ``RecordOwners()``, which when no record is passed in defaults to allow any authenticated user:
```python
class RecordOwners(Generator):
def needs(self, record=None, **kwargs):
if record is None:
return [authenticated_user]
# ...
```
### Patches
The problem is patched in Invenio-Drafts-Resources v0.13.7 and 0.14.6+, which is part of InvenioRDM v6.0.1 and InvenioRDM v7.0 respectively.
You can verify the version installed of Invenio-Drafts-Resources via PIP:
```console
cd ~/src/my-site
pipenv run pip freeze | grep invenio-drafts-resources
```
### References
- [Security policy](https://invenio.readthedocs.io/en/latest/community/security-policy.html)
### For more information
If you have any questions or comments about this advisory:
* Chat with us on Discord: https://discord.gg/8qatqBC
| {'CVE-2021-43781'} | 2022-03-03T05:14:00.978142Z | 2021-12-06T23:57:59Z | MODERATE | null | {'CWE-862'} | {'https://github.com/inveniosoftware/invenio-drafts-resources/security/advisories/GHSA-xr38-w74q-r8jv', 'https://github.com/inveniosoftware/invenio-drafts-resources/commit/039b0cff1ad4b952000f4d8c3a93f347108b6626', 'https://nvd.nist.gov/vuln/detail/CVE-2021-43781'} | null | {'https://github.com/inveniosoftware/invenio-drafts-resources/commit/039b0cff1ad4b952000f4d8c3a93f347108b6626'} | {'https://github.com/inveniosoftware/invenio-drafts-resources/commit/039b0cff1ad4b952000f4d8c3a93f347108b6626'} |
PyPI | PYSEC-2021-369 | null | The Unicorn framework before 0.36.1 for Django allows XSS via a component. NOTE: this issue exists because of an incomplete fix for CVE-2021-42053. | {'CVE-2021-42134', 'GHSA-ggmv-6q9p-9gm6'} | 2021-10-11T05:26:07.255634Z | 2021-10-11T01:15:00Z | null | null | null | {'https://github.com/adamghill/django-unicorn/commit/3a832a9e3f6455ddd3b87f646247269918ad10c6', 'https://github.com/adamghill/django-unicorn/compare/0.36.0...0.36.1', 'https://github.com/advisories/GHSA-ggmv-6q9p-9gm6'} | null | {'https://github.com/adamghill/django-unicorn/commit/3a832a9e3f6455ddd3b87f646247269918ad10c6'} | {'https://github.com/adamghill/django-unicorn/commit/3a832a9e3f6455ddd3b87f646247269918ad10c6'} |
PyPI | PYSEC-2021-693 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.SparseDenseCwiseMul`, an attacker can trigger denial of service via `CHECK`-fails or accesses to outside the bounds of heap allocated data. Since the implementation(https://github.com/tensorflow/tensorflow/blob/38178a2f7a681a7835bb0912702a134bfe3b4d84/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L68-L80) only validates the rank of the input arguments but no constraints between dimensions(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseDenseCwiseMul), an attacker can abuse them to trigger internal `CHECK` assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29567', 'GHSA-wp3c-xw9g-gpcg'} | 2021-12-09T06:35:26.340630Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp3c-xw9g-gpcg', 'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc'} | null | {'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc'} | {'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc'} |
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