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PyPI | GHSA-pg59-2f92-5cph | Heap buffer overflow in Tensorflow | ### Impact
The `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L110-L117
In the sparse and ragged count weights are still accessed in parallel with the data:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L199-L201
But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights.
### Patches
We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.
We recommend users to upgrade to TensorFlow 2.3.1.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability is a variant of [GHSA-p5f8-gfw5-33w4](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4) | {'CVE-2020-15196'} | 2021-08-26T15:11:34Z | 2020-09-25T18:28:21Z | HIGH | null | {'CWE-125', 'CWE-119', 'CWE-122'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15196', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pg59-2f92-5cph', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | GHSA-5p9j-w2wx-qx4c | Open Redirect in django-spirit | django-spirit prior to version 0.12.3 is vulnerable to open redirect. In the /user/login endpoint, it doesn't check the value of the next parameter when the user is logged in and passes it directly to redirect which result to open redirect. This also affects /user/logout, /user/register, /user/login, /user/resend-activation. | {'CVE-2022-0869'} | 2022-03-14T21:31:59.838702Z | 2022-03-07T00:00:40Z | MODERATE | null | {'CWE-601'} | {'https://github.com/nitely/spirit', 'https://huntr.dev/bounties/ed335a88-f68c-4e4d-ac85-f29a51b03342', 'https://github.com/nitely/spirit/commit/8f32f89654d6c30d56e0dd167059d32146fb32ef', 'https://nvd.nist.gov/vuln/detail/CVE-2022-0869'} | null | {'https://github.com/nitely/spirit/commit/8f32f89654d6c30d56e0dd167059d32146fb32ef'} | {'https://github.com/nitely/spirit/commit/8f32f89654d6c30d56e0dd167059d32146fb32ef'} |
PyPI | PYSEC-2020-145 | null | Python TUF (The Update Framework) reference implementation before version 0.12 it will incorrectly trust a previously downloaded root metadata file which failed verification at download time. This allows an attacker who is able to serve multiple new versions of root metadata (i.e. by a person-in-the-middle attack) culminating in a version which has not been correctly signed to control the trust chain for future updates. This is fixed in version 0.12 and newer. | {'GHSA-f8mr-jv2c-v8mg', 'CVE-2020-15163'} | 2020-09-15T17:35:00Z | 2020-09-09T18:15:00Z | null | null | null | {'https://github.com/theupdateframework/tuf/security/advisories/GHSA-f8mr-jv2c-v8mg', 'https://pypi.org/project/tuf', 'https://github.com/theupdateframework/tuf/releases/tag/v0.12.0', 'https://github.com/theupdateframework/tuf/commit/3d342e648fbacdf43a13d7ba8886aaaf07334af7', 'https://github.com/theupdateframework/tuf/pull/885'} | null | {'https://github.com/theupdateframework/tuf/commit/3d342e648fbacdf43a13d7ba8886aaaf07334af7'} | {'https://github.com/theupdateframework/tuf/commit/3d342e648fbacdf43a13d7ba8886aaaf07334af7'} |
PyPI | PYSEC-2021-846 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `tf.math.segment_*` operations results in a `CHECK`-fail related abort (and denial of service) if a segment id in `segment_ids` is large. This is similar to CVE-2021-29584 (and similar other reported vulnerabilities in TensorFlow, localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using `AddDim`. However, if the number of elements in the tensor overflows an `int64_t` value, `AddDim` results in a `CHECK` failure which provokes a `std::abort`. Instead, code should use `AddDimWithStatus`. 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-cq76-mxrc-vchh', 'CVE-2021-41195'} | 2021-12-13T06:20:52.754328Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/46888', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh', 'https://github.com/tensorflow/tensorflow/pull/51733', 'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429'} | null | {'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429'} | {'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429'} |
PyPI | PYSEC-2017-3 | null | The chroot, jail, and zone connection plugins in ansible before 1.9.2 allow local users to escape a restricted environment via a symlink attack. | {'CVE-2015-6240'} | 2021-07-02T02:41:33.471109Z | 2017-06-07T20:29:00Z | null | null | null | {'https://github.com/ansible/ansible/commit/952166f48eb0f5797b75b160fd156bbe1e8fc647', 'https://github.com/ansible/ansible/commit/ca2f2c4ebd7b5e097eab0a710f79c1f63badf95b', 'https://lists.debian.org/debian-lts-announce/2019/09/msg00016.html', 'https://bugzilla.redhat.com/show_bug.cgi?id=1243468', 'http://www.openwall.com/lists/oss-security/2015/08/17/10'} | null | {'https://github.com/ansible/ansible/commit/952166f48eb0f5797b75b160fd156bbe1e8fc647', 'https://github.com/ansible/ansible/commit/ca2f2c4ebd7b5e097eab0a710f79c1f63badf95b'} | {'https://github.com/ansible/ansible/commit/ca2f2c4ebd7b5e097eab0a710f79c1f63badf95b', 'https://github.com/ansible/ansible/commit/952166f48eb0f5797b75b160fd156bbe1e8fc647'} |
PyPI | PYSEC-2021-515 | null | TensorFlow is an end-to-end open source platform for machine learning. The `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division(https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be zero. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29587', 'GHSA-j7rm-8ww4-xx2g'} | 2021-12-09T06:34:56.873016Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j7rm-8ww4-xx2g', 'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7'} | null | {'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7'} | {'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7'} |
PyPI | GHSA-4fx9-vc88-q2xc | Infinite loop in Pillow | JpegImagePlugin may append an EOF marker to the end of a truncated file, so that the last segment of the data will still be processed by the decoder.
If the EOF marker is not detected as such however, this could lead to an infinite loop where JpegImagePlugin keeps trying to end the file. | null | 2022-03-11T23:47:55.619705Z | 2022-03-11T23:39:27Z | LOW | null | {'CWE-400'} | {'https://github.com/python-pillow/Pillow/commit/baae9ec4b67c68e3adaf1208cf54e8de5e38a6fd', 'https://pillow.readthedocs.io/en/stable/releasenotes/9.0.0.html#ensure-jpegimageplugin-stops-at-the-end-of-a-truncated-file', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/baae9ec4b67c68e3adaf1208cf54e8de5e38a6fd'} | {'https://github.com/python-pillow/Pillow/commit/baae9ec4b67c68e3adaf1208cf54e8de5e38a6fd'} |
PyPI | PYSEC-2021-450 | null | TensorFlow is an end-to-end open source platform for machine learning. The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. 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-2021-29522', 'GHSA-c968-pq7h-7fxv'} | 2021-12-09T06:34:46.764419Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c968-pq7h-7fxv'} | null | {'https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa'} | {'https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa'} |
PyPI | PYSEC-2022-63 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `QuantizedMaxPool` has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer. 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-3mw4-6rj6-74g5', 'CVE-2022-21739'} | 2022-03-09T00:17:31.682282Z | 2022-02-03T14:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/53b0dd6dc5957652f35964af16b892ec9af4a559', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/quantized_pooling_ops.cc#L114-L130', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3mw4-6rj6-74g5'} | null | {'https://github.com/tensorflow/tensorflow/commit/53b0dd6dc5957652f35964af16b892ec9af4a559'} | {'https://github.com/tensorflow/tensorflow/commit/53b0dd6dc5957652f35964af16b892ec9af4a559'} |
PyPI | PYSEC-2021-779 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.UnravelIndex` by triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unravel_index_op.cc#L36) does not check that the tensor subsumed by `dims` is not empty. Hence, if one element of `dims` is 0, the implementation does a division by 0. We have patched the issue in GitHub commit a776040a5e7ebf76eeb7eb923bf1ae417dd4d233. 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-2021-37668', 'GHSA-2wmv-37vq-52g5'} | 2021-12-09T06:35:38.256317Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2wmv-37vq-52g5', 'https://github.com/tensorflow/tensorflow/commit/a776040a5e7ebf76eeb7eb923bf1ae417dd4d233'} | null | {'https://github.com/tensorflow/tensorflow/commit/a776040a5e7ebf76eeb7eb923bf1ae417dd4d233'} | {'https://github.com/tensorflow/tensorflow/commit/a776040a5e7ebf76eeb7eb923bf1ae417dd4d233'} |
PyPI | PYSEC-2021-283 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a denial of service in `boosted_trees_create_quantile_stream_resource` by using negative arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that `num_streams` only contains non-negative numbers. In turn, [this results in using this value to allocate memory](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). However, `reserve` receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. We have patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37661', 'GHSA-gf88-j2mg-cc82'} | 2021-08-27T03:22:45.010979Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf88-j2mg-cc82'} | null | {'https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992'} | {'https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992'} |
PyPI | PYSEC-2020-329 | 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'} | 2021-12-09T06:35:15.686226Z | 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-2020-283 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-xmq7-7fxm-rr79', 'CVE-2020-15203'} | 2021-12-09T06:34:42.062393Z | 2020-09-25T19:15:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79', 'https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83'} | {'https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83'} |
PyPI | PYSEC-2021-796 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`expand_dims.cc`](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/expand_dims.cc#L36-L50) contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If `axis` is a large negative value (e.g., `-100000`), then after the first `if` it would still be negative. The check following the `if` statement will pass and the `for` loop would read one element before the start of `input_dims.data` (when `i = 0`). We have patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257. 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-2021-37685', 'GHSA-c545-c4f9-rf6v'} | 2021-12-09T06:35:39.778016Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c545-c4f9-rf6v', 'https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257'} | null | {'https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257'} | {'https://github.com/tensorflow/tensorflow/commit/d94ffe08a65400f898241c0374e9edc6fa8ed257'} |
PyPI | PYSEC-2022-6 | null | pipenv is a Python development workflow tool. Starting with version 2018.10.9 and prior to version 2022.1.8, a flaw in pipenv's parsing of requirements files allows an attacker to insert a specially crafted string inside a comment anywhere within a requirements.txt file, which will cause victims who use pipenv to install the requirements file to download dependencies from a package index server controlled by the attacker. By embedding malicious code in packages served from their malicious index server, the attacker can trigger arbitrary remote code execution (RCE) on the victims' systems. If an attacker is able to hide a malicious `--index-url` option in a requirements file that a victim installs with pipenv, the attacker can embed arbitrary malicious code in packages served from their malicious index server that will be executed on the victim's host during installation (remote code execution/RCE). When pip installs from a source distribution, any code in the setup.py is executed by the install process. This issue is patched in version 2022.1.8. The GitHub Security Advisory contains more information about this vulnerability. | {'GHSA-qc9x-gjcv-465w', 'CVE-2022-21668'} | 2022-01-19T19:22:23.694218Z | 2022-01-10T21:15:00Z | null | null | null | {'https://github.com/pypa/pipenv/releases/tag/v2022.1.8', 'https://github.com/pypa/pipenv/security/advisories/GHSA-qc9x-gjcv-465w', 'https://github.com/pypa/pipenv/commit/439782a8ae36c4762c88e43d5f0d8e563371b46f'} | null | {'https://github.com/pypa/pipenv/commit/439782a8ae36c4762c88e43d5f0d8e563371b46f'} | {'https://github.com/pypa/pipenv/commit/439782a8ae36c4762c88e43d5f0d8e563371b46f'} |
PyPI | GHSA-mv2w-4jqc-6fg4 | Command injection in libvcs and vcspull | The package libvcs before 0.11.1 are vulnerable to Command Injection via argument injection. When calling the update_repo function (when using hg), the url parameter is passed to the hg clone command. By injecting some hg options it was possible to get arbitrary command execution. | {'CVE-2022-21187'} | 2022-04-04T21:47:00.808662Z | 2022-03-15T00:00:53Z | HIGH | null | {'CWE-77', 'CWE-74'} | {'https://github.com/vcs-python/libvcs/blob/v0.11.1/CHANGES%23libvcs-0111-2022-03-12', 'https://github.com/vcs-python/vcspull/commit/e1b77128a1fa0754625b5f43d8bc47956f21f33e', 'https://github.com/vcs-python/libvcs/blob/master/CHANGES#libvcs-0111-2022-03-12', 'https://github.com/vcs-python/libvcs/pull/306', 'https://github.com/vcs-python/vcspull/blob/master/CHANGES#vcspull-1111-2022-03-12', 'https://snyk.io/vuln/SNYK-PYTHON-LIBVCS-2421204', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21187'} | null | {'https://github.com/vcs-python/vcspull/commit/e1b77128a1fa0754625b5f43d8bc47956f21f33e'} | {'https://github.com/vcs-python/vcspull/commit/e1b77128a1fa0754625b5f43d8bc47956f21f33e'} |
PyPI | GHSA-2gwj-7jmv-h26r | SQL Injection in Django | An issue was discovered in Django 2.2 before 2.2.28, 3.2 before 3.2.13, and 4.0 before 4.0.4. QuerySet.annotate(), aggregate(), and extra() methods are subject to SQL injection in column aliases via a crafted dictionary (with dictionary expansion) as the passed **kwargs. | {'CVE-2022-28346'} | 2022-04-22T23:30:09.772227Z | 2022-04-13T00:00:33Z | CRITICAL | null | {'CWE-89'} | {'https://github.com/django/django/commit/93cae5cb2f9a4ef1514cf1a41f714fef08005200', 'https://lists.debian.org/debian-lts-announce/2022/04/msg00013.html', 'https://docs.djangoproject.com/en/4.0/releases/security/', 'https://www.djangoproject.com/weblog/2022/apr/11/security-releases/', 'http://www.openwall.com/lists/oss-security/2022/04/11/1', 'https://github.com/django/django', 'https://nvd.nist.gov/vuln/detail/CVE-2022-28346', 'https://groups.google.com/forum/#!forum/django-announce'} | null | {'https://github.com/django/django/commit/93cae5cb2f9a4ef1514cf1a41f714fef08005200'} | {'https://github.com/django/django/commit/93cae5cb2f9a4ef1514cf1a41f714fef08005200'} |
PyPI | GHSA-8rcj-c8pj-v3m3 | Reachable Assertion in Tensorflow | ### Impact
When decoding a resource handle tensor from protobuf, a TensorFlow process can encounter cases where a `CHECK` assertion is invalidated based on user controlled arguments. This allows attackers to cause denial of services in TensorFlow processes.
### Patches
We have patched the issue in GitHub commit [14fea662350e7c26eb5fe1be2ac31704e5682ee6](https://github.com/tensorflow/tensorflow/commit/14fea662350e7c26eb5fe1be2ac31704e5682ee6).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2022-23564'} | 2022-03-03T05:13:53.581634Z | 2022-02-09T23:55:28Z | MODERATE | null | {'CWE-617'} | {'https://github.com/tensorflow/tensorflow/commit/14fea662350e7c26eb5fe1be2ac31704e5682ee6', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23564', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rcj-c8pj-v3m3'} | null | {'https://github.com/tensorflow/tensorflow/commit/14fea662350e7c26eb5fe1be2ac31704e5682ee6'} | {'https://github.com/tensorflow/tensorflow/commit/14fea662350e7c26eb5fe1be2ac31704e5682ee6'} |
PyPI | PYSEC-2021-542 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the `fixed_length` value to the size of the type argument. The `fixed_length` argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the `out_data` pointer by `fixed_length * sizeof(T)` bytes whereas it only copied at most `fixed_length` bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-8pmx-p244-g88h', 'CVE-2021-29614'} | 2021-12-09T06:35:01.093835Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8pmx-p244-g88h'} | null | {'https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d'} | {'https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d'} |
PyPI | PYSEC-2021-811 | null | TensorFlow is an open source platform for machine learning. In affected versions an attacker can trigger undefined behavior, integer overflows, segfaults and `CHECK`-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats. The 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-2021-41203', 'GHSA-7pxj-m4jf-r6h2'} | 2021-12-09T06:35:41.718393Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/e8dc63704c88007ee4713076605c90188d66f3d2', 'https://github.com/tensorflow/tensorflow/commit/b619c6f865715ca3b15ef1842b5b95edbaa710ad', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7pxj-m4jf-r6h2', 'https://github.com/tensorflow/tensorflow/commit/368af875869a204b4ac552b9ddda59f6a46a56ec', 'https://github.com/tensorflow/tensorflow/commit/abcced051cb1bd8fb05046ac3b6023a7ebcc4578'} | null | {'https://github.com/tensorflow/tensorflow/commit/e8dc63704c88007ee4713076605c90188d66f3d2', 'https://github.com/tensorflow/tensorflow/commit/abcced051cb1bd8fb05046ac3b6023a7ebcc4578', 'https://github.com/tensorflow/tensorflow/commit/b619c6f865715ca3b15ef1842b5b95edbaa710ad', 'https://github.com/tensorflow/tensorflow/commit/368af875869a204b4ac552b9ddda59f6a46a56ec'} | {'https://github.com/tensorflow/tensorflow/commit/e8dc63704c88007ee4713076605c90188d66f3d2', 'https://github.com/tensorflow/tensorflow/commit/abcced051cb1bd8fb05046ac3b6023a7ebcc4578', 'https://github.com/tensorflow/tensorflow/commit/b619c6f865715ca3b15ef1842b5b95edbaa710ad', 'https://github.com/tensorflow/tensorflow/commit/368af875869a204b4ac552b9ddda59f6a46a56ec'} |
PyPI | PYSEC-2022-34 | null | HTTPie is a command-line HTTP client. HTTPie has the practical concept of sessions, which help users to persistently store some of the state that belongs to the outgoing requests and incoming responses on the disk for further usage. Before 3.1.0, HTTPie didn‘t distinguish between cookies and hosts they belonged. This behavior resulted in the exposure of some cookies when there are redirects originating from the actual host to a third party website. Users are advised to upgrade. There are no known workarounds. | {'CVE-2022-24737', 'GHSA-9w4w-cpc8-h2fq'} | 2022-03-08T02:44:28.415437Z | 2022-03-07T23:15:00Z | null | null | null | {'https://github.com/httpie/httpie/security/advisories/GHSA-9w4w-cpc8-h2fq', 'https://github.com/httpie/httpie/releases/tag/3.1.0', 'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b'} | null | {'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b'} | {'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b'} |
PyPI | PYSEC-2019-154 | null | The CreateID function in packet.py in pyrad before 2.1 uses sequential packet IDs, which makes it easier for remote attackers to spoof packets by predicting the next ID, a different vulnerability than CVE-2013-0294. | {'CVE-2013-0342'} | 2021-07-05T00:01:25.106986Z | 2019-12-09T21:15:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=911685', 'http://www.openwall.com/lists/oss-security/2013/02/21/27', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/82134', 'https://github.com/pyradius/pyrad/commit/38f74b36814ca5b1a27d9898141126af4953bee5', 'http://www.securityfocus.com/bid/57984', 'http://www.openwall.com/lists/oss-security/2013/02/15/9', 'http://www.openwall.com/lists/oss-security/2013/02/22/2'} | null | {'https://github.com/pyradius/pyrad/commit/38f74b36814ca5b1a27d9898141126af4953bee5'} | {'https://github.com/pyradius/pyrad/commit/38f74b36814ca5b1a27d9898141126af4953bee5'} |
PyPI | PYSEC-2021-407 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `DeserializeSparse` can trigger a null pointer dereference. This is because the shape inference function assumes that the `serialize_sparse` tensor is a tensor with positive rank (and having `3` as the last dimension). 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-x3v8-c8qx-3j3r', 'CVE-2021-41215'} | 2021-11-13T06:52:44.476075Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x3v8-c8qx-3j3r', 'https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850'} | null | {'https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850'} | {'https://github.com/tensorflow/tensorflow/commit/d3738dd70f1c9ceb547258cbb82d853da8771850'} |
PyPI | GHSA-wmg4-8cp2-hpg9 | Denial of service attack via memory exhaustion | ### Impact
Sydent does not limit the size of requests it receives from HTTP clients. A malicious user could send an HTTP request with a very large body, leading to disk space exhaustion and denial of service.
Sydent also does not limit response size for requests it makes to remote Matrix homeservers. A malicious homeserver could return a very large response, again leading to memory exhaustion and denial of service.
This affects any server which accepts registration requests from untrusted clients.
### Patches
Patched by 89071a1, 0523511, f56eee3.
### Workarounds
Request sizes can be limited in an HTTP reverse-proxy.
There are no known workarounds for the problem with overlarge responses.
### For more information
If you have any questions or comments about this advisory, email us at security@matrix.org. | {'CVE-2021-29430'} | 2022-03-03T05:12:47.055938Z | 2021-04-19T14:54:04Z | LOW | null | {'CWE-20', 'CWE-400'} | {'https://github.com/matrix-org/sydent/security/advisories/GHSA-wmg4-8cp2-hpg9', 'https://github.com/matrix-org/sydent/commit/89071a1a754c69a50deac89e6bb74002d4cda19d', 'https://github.com/matrix-org/sydent/releases/tag/v2.3.0', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29430', 'https://github.com/matrix-org/sydent/commit/f56eee315b6c44fdd9f6aa785cc2ec744a594428', 'https://pypi.org/project/matrix-sydent/', 'https://github.com/matrix-org/sydent/commit/0523511d2fb40f2738f8a8549868f44b96e5dab7'} | null | {'https://github.com/matrix-org/sydent/commit/f56eee315b6c44fdd9f6aa785cc2ec744a594428', 'https://github.com/matrix-org/sydent/commit/0523511d2fb40f2738f8a8549868f44b96e5dab7', 'https://github.com/matrix-org/sydent/commit/89071a1a754c69a50deac89e6bb74002d4cda19d'} | {'https://github.com/matrix-org/sydent/commit/0523511d2fb40f2738f8a8549868f44b96e5dab7', 'https://github.com/matrix-org/sydent/commit/f56eee315b6c44fdd9f6aa785cc2ec744a594428', 'https://github.com/matrix-org/sydent/commit/89071a1a754c69a50deac89e6bb74002d4cda19d'} |
PyPI | PYSEC-2017-53 | null | Cross-site scripting (XSS) vulnerability in 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.x before 4.3.7, and 5.0rc1. | {'CVE-2015-7316'} | 2021-07-25T23:34:48.448357Z | 2017-09-25T17:29:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=1264788', 'http://www.openwall.com/lists/oss-security/2015/09/22/14', 'https://plone.org/security/hotfix/20150910/non-persistent-xss-in-plone', 'https://github.com/plone/Products.CMFPlone/commit/3da710a2cd68587f0bf34f2e7ea1167d6eeee087'} | null | {'https://github.com/plone/Products.CMFPlone/commit/3da710a2cd68587f0bf34f2e7ea1167d6eeee087'} | {'https://github.com/plone/Products.CMFPlone/commit/3da710a2cd68587f0bf34f2e7ea1167d6eeee087'} |
PyPI | GHSA-p5w8-wqhj-9hhf | StripComments filter contains a regular expression that is vulnerable to ReDOS (Regular Expression Denial of Service) | ### Impact
The formatter function that strips comments from a SQL contains a regular expression that is vulnerable to [ReDoS](https://owasp.org/www-community/attacks/Regular_expression_Denial_of_Service_-_ReDoS) (Regular Expression Denial of Service). The regular expression may cause exponential backtracking on strings containing many repetitions of '\r\n' in SQL comments.
### Patches
The issues has been fixed in sqlparse 0.4.2.
### Workarounds
Only the formatting feature that removes comments from SQL statements is affected by this regular expression. As a workaround don't use the `sqlformat.format` function with keyword `strip_comments=True` or the `--strip-comments` command line flag when using the `sqlformat` command line tool.
### References
This issue was discovered by GitHub team members @erik-krogh and @yoff. It was found using a [CodeQL](https://codeql.github.com/) query which identifies inefficient regular expressions. You can see the results of the query on python-sqlparse by following [this link](https://lgtm.com/query/2223658096471222354/).
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [sqlparse issue tracker](https://github.com/andialbrecht/sqlparse/issues)
* Email us at [albrecht.andi@gmail.com](mailto:albrecht.andi@gmail.com)
| {'CVE-2021-32839'} | 2022-03-03T05:13:53.522940Z | 2021-09-10T17:56:06Z | HIGH | null | {'CWE-400'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-32839', 'https://github.com/andialbrecht/sqlparse/security/advisories/GHSA-p5w8-wqhj-9hhf', 'https://github.com/andialbrecht/sqlparse', 'https://github.com/andialbrecht/sqlparse/commit/8238a9e450ed1524e40cb3a8b0b3c00606903aeb', 'https://securitylab.github.com/advisories/GHSL-2021-107-andialbrecht-sqlparse/'} | null | {'https://github.com/andialbrecht/sqlparse/commit/8238a9e450ed1524e40cb3a8b0b3c00606903aeb'} | {'https://github.com/andialbrecht/sqlparse/commit/8238a9e450ed1524e40cb3a8b0b3c00606903aeb'} |
PyPI | PYSEC-2013-10 | null | pyshop before 0.7.1 uses HTTP to retrieve packages from the PyPI repository, and does not perform integrity checks on package contents, which allows man-in-the-middle attackers to execute arbitrary code via a crafted response to a download operation. | {'CVE-2013-1630'} | 2021-07-05T00:01:25.257414Z | 2013-08-06T02:52:00Z | null | null | null | {'https://github.com/mardiros/pyshop/commit/ffadb0bcdef1e385884571670210cfd6ba351784', 'https://github.com/mardiros/pyshop/blob/master/CHANGES.txt', 'http://www.reddit.com/r/Python/comments/17rfh7/warning_dont_use_pip_in_an_untrusted_network_a/'} | null | {'https://github.com/mardiros/pyshop/commit/ffadb0bcdef1e385884571670210cfd6ba351784'} | {'https://github.com/mardiros/pyshop/commit/ffadb0bcdef1e385884571670210cfd6ba351784'} |
PyPI | PYSEC-2021-684 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `tf.raw_ops.SparseSplit`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/699bff5d961f0abfde8fa3f876e6d241681fbef8/tensorflow/core/util/sparse/sparse_tensor.h#L528-L530) accesses an array element based on a user controlled offset. 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-mqh2-9wrp-vx84', 'CVE-2021-29558'} | 2021-12-09T06:35:24.786927Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mqh2-9wrp-vx84'} | null | {'https://github.com/tensorflow/tensorflow/commit/8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31'} | {'https://github.com/tensorflow/tensorflow/commit/8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31'} |
PyPI | PYSEC-2021-391 | null | TensorFlow is an open source platform for machine learning. In affected versions if `tf.tile` is called with a large input argument then the TensorFlow process will crash due to a `CHECK`-failure caused by an overflow. The number of elements in the output tensor is too much for the `int64_t` type and the overflow is detected via a `CHECK` statement. This aborts the process. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-2p25-55c9-h58q', 'CVE-2021-41198'} | 2021-11-13T06:52:42.007550Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/9294094df6fea79271778eb7e7ae1bad8b5ef98f', 'https://github.com/tensorflow/tensorflow/issues/46911', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2p25-55c9-h58q'} | null | {'https://github.com/tensorflow/tensorflow/commit/9294094df6fea79271778eb7e7ae1bad8b5ef98f'} | {'https://github.com/tensorflow/tensorflow/commit/9294094df6fea79271778eb7e7ae1bad8b5ef98f'} |
PyPI | GHSA-cgfm-62j4-v4rf | Heap out of bounds access in sparse reduction operations | ### Impact
The implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data:
```python
import tensorflow as tf
x = tf.SparseTensor(
indices=[[773, 773, 773], [773, 773, 773]],
values=[1, 1],
dense_shape=[337, 337, 337])
tf.sparse.reduce_sum(x, 1)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor.
### Patches
We have patched the issue in GitHub commit [87158f43f05f2720a374f3e6d22a7aaa3a33f750](https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### 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-2021-37635'} | 2022-03-03T05:12:59.630077Z | 2021-08-25T14:44:17Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cgfm-62j4-v4rf', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37635'} | null | {'https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750'} | {'https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750'} |
PyPI | GHSA-4g9f-63rx-5cw4 | Segfault in Tensorflow | ### Impact
The [`tf.raw_ops.Switch`](https://www.tensorflow.org/api_docs/python/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:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/common_runtime/eager/kernel_and_device.cc#L308-L313
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
### Patches
We have patched the issue in da8558533d925694483d2c136a9220d6d49d843c and will release a patch release for all affected versions.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2020-15190'} | 2022-03-03T05:14:12.080578Z | 2020-09-25T18:28:14Z | MODERATE | null | {'CWE-20', 'CWE-476'} | {'https://github.com/tensorflow/tensorflow/commit/da8558533d925694483d2c136a9220d6d49d843c', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4g9f-63rx-5cw4', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15190', 'https://github.com/tensorflow/tensorflow', '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-2020-72 | null | OpenSlides is a free, Web-based presentation and assembly system for managing and projecting agenda, motions, and elections of assemblies. OpenSlides version 3.2, due to unsufficient user input validation and escaping, it is vulnerable to persistant cross-site scripting (XSS). In the web applications users can enter rich text in various places, e.g. for personal notes or in motions. These fields can be used to store arbitrary JavaScript Code that will be executed when other users read the respective text. An attacker could utilize this vulnerability be used to manipulate votes of other users, hijack the moderators session or simply disturb the meeting. The vulnerability was introduced with 6eae497abeab234418dfbd9d299e831eff86ed45 on 16.04.2020, which is first included in the 3.2 release. It has been patched in version 3.3 ( in commit f3809fc8a97ee305d721662a75f788f9e9d21938, merged in master on 20.11.2020). | {'GHSA-w5wr-98qm-jx92', 'CVE-2020-26280'} | 2020-12-22T20:11:00Z | 2020-12-18T19:15:00Z | null | null | null | {'https://github.com/OpenSlides/OpenSlides/commit/f3809fc8a97ee305d721662a75f788f9e9d21938', 'https://github.com/OpenSlides/OpenSlides/blob/master/CHANGELOG.rst#version-33-2020-12-18', 'https://github.com/OpenSlides/OpenSlides/security/advisories/GHSA-w5wr-98qm-jx92', 'https://www.syss.de/fileadmin/dokumente/Publikationen/Advisories/SYSS-2020-043.txt', 'https://github.com/OpenSlides/OpenSlides/pull/5714'} | null | {'https://github.com/OpenSlides/OpenSlides/commit/f3809fc8a97ee305d721662a75f788f9e9d21938'} | {'https://github.com/OpenSlides/OpenSlides/commit/f3809fc8a97ee305d721662a75f788f9e9d21938'} |
PyPI | PYSEC-2022-160 | null | Twisted is an event-based framework for internet applications, supporting Python 3.6+. Prior to 22.2.0, Twisted SSH client and server implement is able to accept an infinite amount of data for the peer's SSH version identifier. This ends up with a buffer using all the available memory. The attach is a simple as `nc -rv localhost 22 < /dev/zero`. A patch is available in version 22.2.0. There are currently no known workarounds. | {'CVE-2022-21716', 'GHSA-rv6r-3f5q-9rgx'} | 2022-03-10T17:35:00.079970Z | 2022-03-03T21:15:00Z | null | null | null | {'https://github.com/twisted/twisted/commit/89c395ee794e85a9657b112c4351417850330ef9', 'https://twistedmatrix.com/trac/ticket/10284', 'https://github.com/twisted/twisted/security/advisories/GHSA-rv6r-3f5q-9rgx', 'https://github.com/twisted/twisted/releases/tag/twisted-22.2.0'} | null | {'https://github.com/twisted/twisted/commit/89c395ee794e85a9657b112c4351417850330ef9'} | {'https://github.com/twisted/twisted/commit/89c395ee794e85a9657b112c4351417850330ef9'} |
PyPI | GHSA-w24h-v9qh-8gxj | SQL Injection in Django | A SQL injection issue was discovered in QuerySet.explain() in Django 2.2 before 2.2.28, 3.2 before 3.2.13, and 4.0 before 4.0.4. This occurs by passing a crafted dictionary (with dictionary expansion) as the **options argument, and placing the injection payload in an option name. | {'CVE-2022-28347'} | 2022-04-22T23:30:12.010823Z | 2022-04-13T00:00:33Z | CRITICAL | null | {'CWE-89'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-28347', 'https://github.com/django/django/commit/6723a26e59b0b5429a0c5873941e01a2e1bdbb81', 'https://groups.google.com/forum/#!forum/django-announce', 'https://docs.djangoproject.com/en/4.0/releases/security/', 'http://www.openwall.com/lists/oss-security/2022/04/11/1', 'https://github.com/django/django', 'https://www.djangoproject.com/weblog/2022/apr/11/security-releases/'} | null | {'https://github.com/django/django/commit/6723a26e59b0b5429a0c5873941e01a2e1bdbb81'} | {'https://github.com/django/django/commit/6723a26e59b0b5429a0c5873941e01a2e1bdbb81'} |
PyPI | PYSEC-2021-186 | 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.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-x83m-p7pv-ch8v', 'CVE-2021-29549'} | 2021-08-27T03:22:30.167299Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x83m-p7pv-ch8v', 'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16'} | null | {'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16'} | {'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16'} |
PyPI | PYSEC-2021-169 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads. 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-2021-29532', 'GHSA-j47f-4232-hvv8'} | 2021-08-27T03:22:27.051975Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j47f-4232-hvv8', 'https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a'} | null | {'https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a'} | {'https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a'} |
PyPI | PYSEC-2021-493 | 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.SparseFillEmptyRows`. This is because of missing validation(https://github.com/tensorflow/tensorflow/blob/fdc82089d206e281c628a93771336bf87863d5e8/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L230-L231) that was covered under a `TODO`. If the `dense_shape` tensor is empty, then `dense_shape_t.vec<>()` would cause a null pointer dereference in the implementation of the op. 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-r6pg-pjwc-j585', 'CVE-2021-29565'} | 2021-12-09T06:34:53.440506Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/faa76f39014ed3b5e2c158593b1335522e573c7f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r6pg-pjwc-j585'} | null | {'https://github.com/tensorflow/tensorflow/commit/faa76f39014ed3b5e2c158593b1335522e573c7f'} | {'https://github.com/tensorflow/tensorflow/commit/faa76f39014ed3b5e2c158593b1335522e573c7f'} |
PyPI | PYSEC-2021-539 | null | TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseReshape` results in a denial of service based on a `CHECK`-failure. The implementation(https://github.com/tensorflow/tensorflow/blob/e87b51ce05c3eb172065a6ea5f48415854223285/tensorflow/core/kernels/sparse_reshape_op.cc#L40) has no validation that the input arguments 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 and TensorFlow 2.3.3, as these are the only affected versions. | {'GHSA-9rpc-5v9q-5r7f', 'CVE-2021-29611'} | 2021-12-09T06:35:00.643127Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9rpc-5v9q-5r7f', 'https://github.com/tensorflow/tensorflow/commit/1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6'} | null | {'https://github.com/tensorflow/tensorflow/commit/1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6'} | {'https://github.com/tensorflow/tensorflow/commit/1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6'} |
PyPI | PYSEC-2021-610 | null | TensorFlow is an open source platform for machine learning. In affected versions if `tf.summary.create_file_writer` is called with non-scalar arguments code crashes due to a `CHECK`-fail. 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-gh8h-7j2j-qv4f', 'CVE-2021-41200'} | 2021-12-09T06:35:07.608577Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/46909', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh8h-7j2j-qv4f', 'https://github.com/tensorflow/tensorflow/commit/874bda09e6702cd50bac90b453b50bcc65b2769e'} | null | {'https://github.com/tensorflow/tensorflow/commit/874bda09e6702cd50bac90b453b50bcc65b2769e'} | {'https://github.com/tensorflow/tensorflow/commit/874bda09e6702cd50bac90b453b50bcc65b2769e'} |
PyPI | PYSEC-2021-240 | null | TensorFlow is an end-to-end open source platform for machine learning. A specially crafted TFLite model could trigger an OOB write on heap in the TFLite implementation of `ArgMin`/`ArgMax`(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/arg_min_max.cc#L52-L59). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the condition in the `if` is never true, so code writes past the last valid element of `output_dims->data`. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-crch-j389-5f84', 'CVE-2021-29603'} | 2021-08-27T03:22:39.733041Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c59c37e7b2d563967da813fa50fe20b21f4da683', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-crch-j389-5f84'} | null | {'https://github.com/tensorflow/tensorflow/commit/c59c37e7b2d563967da813fa50fe20b21f4da683'} | {'https://github.com/tensorflow/tensorflow/commit/c59c37e7b2d563967da813fa50fe20b21f4da683'} |
PyPI | GHSA-823f-cwm9-4g74 | Splash authentication credentials potentially leaked to target websites | ### Impact
If you use [`HttpAuthMiddleware`](http://doc.scrapy.org/en/latest/topics/downloader-middleware.html#module-scrapy.downloadermiddlewares.httpauth) (i.e. the `http_user` and `http_pass` spider attributes) for Splash authentication, any non-Splash request will expose your credentials to the request target. This includes `robots.txt` requests sent by Scrapy when the `ROBOTSTXT_OBEY` setting is set to `True`.
### Patches
Upgrade to scrapy-splash 0.8.0 and use the new `SPLASH_USER` and `SPLASH_PASS` settings instead to set your Splash authentication credentials safely.
### Workarounds
If you cannot upgrade, set your Splash request credentials on a per-request basis, [using the `splash_headers` request parameter](https://github.com/scrapy-plugins/scrapy-splash/tree/0.8.x#http-basic-auth), instead of defining them globally using the [`HttpAuthMiddleware`](http://doc.scrapy.org/en/latest/topics/downloader-middleware.html#module-scrapy.downloadermiddlewares.httpauth).
Alternatively, make sure all your requests go through Splash. That includes disabling the [robots.txt middleware](https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#topics-dlmw-robots).
### For more information
If you have any questions or comments about this advisory:
* [Open an issue](https://github.com/scrapy-plugins/scrapy-splash/issues)
* [Email us](mailto:opensource@zyte.com)
| {'CVE-2021-41124'} | 2022-03-03T05:13:59.948687Z | 2021-10-06T17:49:23Z | HIGH | null | {'CWE-200'} | {'https://github.com/scrapy-plugins/scrapy-splash', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41124', 'https://github.com/scrapy-plugins/scrapy-splash/releases/tag/0.8.0', 'https://github.com/scrapy-plugins/scrapy-splash/security/advisories/GHSA-823f-cwm9-4g74', 'https://github.com/scrapy-plugins/scrapy-splash/commit/2b253e57fe64ec575079c8cdc99fe2013502ea31'} | null | {'https://github.com/scrapy-plugins/scrapy-splash/commit/2b253e57fe64ec575079c8cdc99fe2013502ea31'} | {'https://github.com/scrapy-plugins/scrapy-splash/commit/2b253e57fe64ec575079c8cdc99fe2013502ea31'} |
PyPI | GHSA-fq6p-6334-8gr4 | Memory leak in decoding PNG images | ### Impact
When [decoding PNG images](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/image/decode_image_op.cc#L322-L416) TensorFlow can produce a memory leak if the image is invalid.
After calling `png::CommonInitDecode(..., &decode)`, the `decode` value contains allocated buffers which can only be freed by calling `png::CommonFreeDecode(&decode)`. However, several error case in the function implementation invoke the `OP_REQUIRES` macro which immediately terminates the execution of the function, without allowing for the memory free to occur.
### Patches
We have patched the issue in GitHub commit [ab51e5b813573dc9f51efa335aebcf2994125ee9](https://github.com/tensorflow/tensorflow/commit/ab51e5b813573dc9f51efa335aebcf2994125ee9).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2022-23585'} | 2022-03-03T05:12:54.849263Z | 2022-02-09T23:26:08Z | MODERATE | null | {'CWE-401'} | {'https://github.com/tensorflow/tensorflow/commit/ab51e5b813573dc9f51efa335aebcf2994125ee9', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq6p-6334-8gr4', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23585', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/image/decode_image_op.cc#L322-L416'} | null | {'https://github.com/tensorflow/tensorflow/commit/ab51e5b813573dc9f51efa335aebcf2994125ee9'} | {'https://github.com/tensorflow/tensorflow/commit/ab51e5b813573dc9f51efa335aebcf2994125ee9'} |
PyPI | PYSEC-2017-28 | null | python-jose before 1.3.2 allows attackers to have unspecified impact by leveraging failure to use a constant time comparison for HMAC keys. | {'CVE-2016-7036'} | 2021-07-05T00:01:25.423622Z | 2017-01-23T21:59:00Z | null | null | null | {'http://www.securityfocus.com/bid/95845', 'https://github.com/mpdavis/python-jose/pull/35/commits/89b46353b9f611e9da38de3d2fedf52331167b93', 'https://github.com/mpdavis/python-jose/releases/tag/1.3.2'} | null | {'https://github.com/mpdavis/python-jose/pull/35/commits/89b46353b9f611e9da38de3d2fedf52331167b93'} | {'https://github.com/mpdavis/python-jose/pull/35/commits/89b46353b9f611e9da38de3d2fedf52331167b93'} |
PyPI | PYSEC-2020-305 | 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'} | 2021-12-09T06:35:12.169887Z | 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-2021-755 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions providing a negative element to `num_elements` list argument of `tf.raw_ops.TensorListReserve` causes the runtime to abort the process due to reallocating a `std::vector` to have a negative number of elements. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/list_kernels.cc#L312) calls `std::vector.resize()` with the new size controlled by input given by the user, without checking that this input is valid. We have patched the issue in GitHub commit 8a6e874437670045e6c7dc6154c7412b4a2135e2. 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-27j5-4p9v-pp67', 'CVE-2021-37644'} | 2021-12-09T06:35:36.131748Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27j5-4p9v-pp67'} | null | {'https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2'} | {'https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2'} |
PyPI | PYSEC-2021-305 | 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'} | 2021-08-27T03:22:47.052583Z | 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-2022-137 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `AssignOp` can result in copying uninitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized. 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-2022-23573', 'GHSA-q85f-69q7-55h2'} | 2022-03-09T00:18:27.416732Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q85f-69q7-55h2', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/assign_op.h#L30-L143', 'https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b'} | null | {'https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b'} | {'https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b'} |
PyPI | PYSEC-2021-581 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.UnravelIndex` by triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unravel_index_op.cc#L36) does not check that the tensor subsumed by `dims` is not empty. Hence, if one element of `dims` is 0, the implementation does a division by 0. We have patched the issue in GitHub commit a776040a5e7ebf76eeb7eb923bf1ae417dd4d233. 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-2021-37668', 'GHSA-2wmv-37vq-52g5'} | 2021-12-09T06:35:04.799643Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2wmv-37vq-52g5', 'https://github.com/tensorflow/tensorflow/commit/a776040a5e7ebf76eeb7eb923bf1ae417dd4d233'} | null | {'https://github.com/tensorflow/tensorflow/commit/a776040a5e7ebf76eeb7eb923bf1ae417dd4d233'} | {'https://github.com/tensorflow/tensorflow/commit/a776040a5e7ebf76eeb7eb923bf1ae417dd4d233'} |
PyPI | PYSEC-2022-18 | null | Cross-site Scripting (XSS) - Reflected in Pypi calibreweb prior to 0.6.16. | {'CVE-2022-0352', 'GHSA-h56g-v4vp-q9q6'} | 2022-02-03T02:16:29.312073Z | 2022-01-28T22:15:00Z | null | null | null | {'https://huntr.dev/bounties/a577ff17-2ded-4c41-84ae-6ac02440f717', 'https://github.com/advisories/GHSA-h56g-v4vp-q9q6', 'https://github.com/janeczku/calibre-web/commit/6bf07539788004513c3692c074ebc7ba4ce005e1'} | null | {'https://github.com/janeczku/calibre-web/commit/6bf07539788004513c3692c074ebc7ba4ce005e1'} | {'https://github.com/janeczku/calibre-web/commit/6bf07539788004513c3692c074ebc7ba4ce005e1'} |
PyPI | PYSEC-2012-11 | null | Buffer overflow in the fribidi_utf8_to_unicode function in PyFriBidi before 0.11.0 allows remote attackers to cause a denial of service (application crash) via a 4-byte utf-8 sequence. | {'CVE-2012-1176'} | 2021-07-25T23:34:50.398385Z | 2012-08-26T20:55:00Z | null | null | null | {'https://github.com/pediapress/pyfribidi/issues/2%29:', 'http://lists.fedoraproject.org/pipermail/package-announce/2012-March/076038.html', 'http://www.securityfocus.com/bid/52451', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/74001', 'http://www.openwall.com/lists/oss-security/2012/03/14/4', 'https://bugzilla.wikimedia.org/show_bug.cgi?id=35055', 'http://www.openwall.com/lists/oss-security/2012/03/14/9', 'http://lists.fedoraproject.org/pipermail/package-announce/2012-March/076053.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2012-March/075293.html', 'https://bugzilla.redhat.com/show_bug.cgi?id=801896', 'https://github.com/pediapress/pyfribidi/commit/d2860c655357975e7b32d84e6b45e98f0dcecd7a', 'http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=663189', 'http://groups.google.com/group/linux.debian.bugs.dist/browse_thread/thread/aacd036037217998/8d095f85f3665bff?lnk=raot'} | null | {'https://github.com/pediapress/pyfribidi/commit/d2860c655357975e7b32d84e6b45e98f0dcecd7a'} | {'https://github.com/pediapress/pyfribidi/commit/d2860c655357975e7b32d84e6b45e98f0dcecd7a'} |
PyPI | PYSEC-2020-217 | null | Git before 1.8.5.6, 1.9.x before 1.9.5, 2.0.x before 2.0.5, 2.1.x before 2.1.4, and 2.2.x before 2.2.1 on Windows and OS X; Mercurial before 3.2.3 on Windows and OS X; Apple Xcode before 6.2 beta 3; mine all versions before 08-12-2014; libgit2 all versions up to 0.21.2; Egit all versions before 08-12-2014; and JGit all versions before 08-12-2014 allow remote Git servers to execute arbitrary commands via a tree containing a crafted .git/config file with (1) an ignorable Unicode codepoint, (2) a git~1/config representation, or (3) mixed case that is improperly handled on a case-insensitive filesystem. | {'CVE-2014-9390'} | 2021-07-25T23:34:39.934367Z | 2020-02-12T02:15:00Z | null | null | null | {'https://libgit2.org/security/', 'http://git-blame.blogspot.com/2014/12/git-1856-195-205-214-and-221-and.html', 'https://github.com/blog/1938-git-client-vulnerability-announced', 'https://news.ycombinator.com/item?id=8769667', 'https://github.com/libgit2/libgit2/commit/928429c5c96a701bcbcafacb2421a82602b36915', 'http://securitytracker.com/id?1031404', 'http://support.apple.com/kb/HT204147', 'http://article.gmane.org/gmane.linux.kernel/1853266', 'http://mercurial.selenic.com/wiki/WhatsNew'} | null | {'https://github.com/libgit2/libgit2/commit/928429c5c96a701bcbcafacb2421a82602b36915'} | {'https://github.com/libgit2/libgit2/commit/928429c5c96a701bcbcafacb2421a82602b36915'} |
PyPI | PYSEC-2021-217 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29580', 'GHSA-x8h6-xgqx-jqgp'} | 2021-08-27T03:22:35.567916Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp'} | null | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925'} | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925'} |
PyPI | GHSA-3hfw-x7gx-437c | Path traversal in Matrix Synapse | ### Impact
Synapse instances with the media repository enabled can be tricked into downloading a file from a remote server into an arbitrary directory, potentially outside the media store directory.
The last two directories and file name of the path are chosen randomly by Synapse and cannot be controlled by an attacker, which limits the impact.
Homeservers with the media repository disabled are unaffected. Homeservers configured with a federation whitelist are also unaffected.
### Patches
Server administrators should upgrade to 1.47.1 or later.
### Workarounds
Server administrators using a reverse proxy could, at the expense of losing media functionality, block the following endpoints:
* `/_matrix/media/r0/download/{serverName}/{mediaId}`
* `/_matrix/media/r0/download/{serverName}/{mediaId}/{fileName}`
* `/_matrix/media/r0/thumbnail/{serverName}/{mediaId}`
Alternatively, non-containerized deployments can be adapted to use the hardened systemd config, located at `contrib/systemd/override-hardened.conf`.
### References
n/a
### For more information
If you have any questions or comments about this advisory, e-mail us at security@matrix.org.
| {'CVE-2021-41281'} | 2022-03-03T05:13:03.790930Z | 2021-11-23T21:58:56Z | HIGH | null | {'CWE-22'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/EU7QRE55U4IUEDLKT5IYPWL3UXMELFAS/', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-3hfw-x7gx-437c', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41281', 'https://github.com/matrix-org/synapse/commit/91f2bd090', 'https://github.com/matrix-org/synapse', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/N3WY56LCEZ4ZECLWV5KMAXF2PSMUB4F2/', 'https://github.com/matrix-org/synapse/releases/tag/v1.47.1'} | null | {'https://github.com/matrix-org/synapse/commit/91f2bd090'} | {'https://github.com/matrix-org/synapse/commit/91f2bd090'} |
PyPI | PYSEC-2021-647 | null | TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., `std::vector<absl::flat_hash_map<int64,T>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3. | {'CVE-2021-29521', 'GHSA-hr84-fqvp-48mm'} | 2021-12-09T06:35:18.423070Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm'} | null | {'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5'} | {'https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5'} |
PyPI | PYSEC-2021-702 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully. Since the constructor(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values. 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-7cqx-92hp-x6wh', 'CVE-2021-29576'} | 2021-12-09T06:35:27.875893Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7cqx-92hp-x6wh'} | null | {'https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09'} | {'https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09'} |
PyPI | GHSA-m38j-pmg3-v5x5 | Timing attack on django-basic-auth-ip-whitelist | ### Impact
Potential timing attack exists on websites where the basic authentication is used or configured, i.e. `BASIC_AUTH_LOGIN` and `BASIC_AUTH_PASSWORD` is set. Currently the string comparison between configured credentials and the ones provided by users is performed through a character-by-character string comparison. This enables a possibility that attacker may time the time it takes the server to validate different usernames and password, and use this knowledge to work out the valid credentials. This attack is understood not to be realistic over the Internet. However, it may be achieved from within local networks where the website is hosted, e.g. from inside a data centre where a website's server is located.
Sites protected by IP address whitelisting only are unaffected by this vulnerability.
### Patches
This vulnerability has been fixed on version 0.3.4 of django-basic-auth-ip-whitelist. Update to version 0.3.4 as soon as possible and change basic authentication username and password configured on a Django project using this package.
### Workarounds
Stop using basic authentication and use the IP whitelisting component only. It can be achieved by not setting `BASIC_AUTH_LOGIN` and `BASIC_AUTH_PASSWORD` in Django project settings.
### References
- [Django mailing list discussion](https://groups.google.com/forum/#!msg/django-developers/iAaq0pvHXuA/fpUuwjK3i2wJ)
### For more information
If you have any questions or comments about this advisory:
* Open an issue at https://github.com/tm-kn/django-basic-auth-ip-whitelist/issues
* Email us at the email specified on the [security policy](https://github.com/tm-kn/django-basic-auth-ip-whitelist/security/policy).
### Acknowledgements
Thanks to Thibaud Colas for reporting this. | {'CVE-2020-4071'} | 2022-03-03T05:13:38.865573Z | 2020-06-23T19:58:27Z | LOW | null | {'CWE-208'} | {'https://github.com/tm-kn/django-basic-auth-ip-whitelist/security/advisories/GHSA-m38j-pmg3-v5x5', 'https://groups.google.com/forum/#!msg/django-developers/iAaq0pvHXuA/fpUuwjK3i2wJ', 'https://nvd.nist.gov/vuln/detail/CVE-2020-4071', 'https://github.com/tm-kn/django-basic-auth-ip-whitelist/commit/effe05ed1ed9e1ccc675a65b69d36217e5c5dfc6'} | null | {'https://github.com/tm-kn/django-basic-auth-ip-whitelist/commit/effe05ed1ed9e1ccc675a65b69d36217e5c5dfc6'} | {'https://github.com/tm-kn/django-basic-auth-ip-whitelist/commit/effe05ed1ed9e1ccc675a65b69d36217e5c5dfc6'} |
PyPI | PYSEC-2022-121 | null | Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would trigger a division by zero in `BiasAndClamp` implementation. There is no check that the `bias_size` is non zero. 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-gf2j-f278-xh4v', 'CVE-2022-23557'} | 2022-03-09T00:18:25.246505Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8c6f391a2282684a25cbfec7687bd5d35261a209', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/lite/kernels/internal/common.h#L75', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf2j-f278-xh4v'} | null | {'https://github.com/tensorflow/tensorflow/commit/8c6f391a2282684a25cbfec7687bd5d35261a209'} | {'https://github.com/tensorflow/tensorflow/commit/8c6f391a2282684a25cbfec7687bd5d35261a209'} |
PyPI | GHSA-cvcq-gmc3-q6m8 | Plain text storage of passwords in Apache Airflow | In Airflow versions prior to 1.10.13, when creating a user using airflow CLI, the password gets logged in plain text in the Log table in Airflow Metadatase. Same happened when creating a Connection with a password field. | {'CVE-2020-17511'} | 2022-03-03T05:13:05.111214Z | 2020-12-17T21:00:56Z | MODERATE | null | {'CWE-312'} | {'https://lists.apache.org/thread.html/ree782a29d927b96bf0b39fb92e2f1f09ea3112a985f7a08ce93765ac%40%3Cusers.airflow.apache.org%3E', 'https://nvd.nist.gov/vuln/detail/CVE-2020-17511', 'https://github.com/apache/airflow/commit/4e32546faf227a6497ce8b282fff7450cae6f665'} | null | {'https://github.com/apache/airflow/commit/4e32546faf227a6497ce8b282fff7450cae6f665'} | {'https://github.com/apache/airflow/commit/4e32546faf227a6497ce8b282fff7450cae6f665'} |
PyPI | PYSEC-2021-578 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the `input` tensor. A similar issue occurs in `MklRequantizePerChannelOp`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments. We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9. 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-2021-37665', 'GHSA-v82p-hv3v-p6qp'} | 2021-12-09T06:35:04.523711Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v82p-hv3v-p6qp', 'https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9'} | null | {'https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69', 'https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9'} | {'https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9', 'https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69'} |
PyPI | PYSEC-2014-79 | null | Cross-site scripting (XSS) vulnerability in gravatars/templatetags/gravatars.py in Djblets before 0.7.30 and 0.8.x before 0.8.3 for Django allows remote attackers to inject arbitrary web script or HTML via a user display name. | {'CVE-2014-3995'} | 2021-08-27T03:22:03.303468Z | 2014-06-16T18:55:00Z | null | null | null | {'http://secunia.com/advisories/58691', 'https://github.com/djblets/djblets/commit/e2c79117efd925636acd871a5f473512602243cf', 'https://github.com/djblets/djblets/commit/77ac64642ad530bf69e390c51fc6fdcb8914c8e7', 'http://seclists.org/oss-sec/2014/q2/494', 'http://seclists.org/oss-sec/2014/q2/498', '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/77ac64642ad530bf69e390c51fc6fdcb8914c8e7'} | {'https://github.com/djblets/djblets/commit/50000d0bbb983fa8c097b588d06b64df8df483bd', 'https://github.com/djblets/djblets/commit/e2c79117efd925636acd871a5f473512602243cf', 'https://github.com/djblets/djblets/commit/77ac64642ad530bf69e390c51fc6fdcb8914c8e7'} |
PyPI | PYSEC-2020-128 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory In the linked code snippet, all the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. The issue is patched in commit 0462de5b544ed4731aa2fb23946ac22c01856b80, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15205', 'GHSA-g7p5-5759-qv46'} | 2020-10-29T16:15:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g7p5-5759-qv46', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/0462de5b544ed4731aa2fb23946ac22c01856b80', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/0462de5b544ed4731aa2fb23946ac22c01856b80'} | {'https://github.com/tensorflow/tensorflow/commit/0462de5b544ed4731aa2fb23946ac22c01856b80'} |
PyPI | PYSEC-2021-651 | 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.Conv2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b40060c9f697b044e3107917c797ba052f4506ab/tensorflow/core/kernels/conv_grad_input_ops.h#L625-L655) does a division by a quantity that is controlled by the caller. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-xm2v-8rrw-w9pm', 'CVE-2021-29525'} | 2021-12-09T06:35:19.088447Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xm2v-8rrw-w9pm', 'https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535'} | null | {'https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535'} | {'https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535'} |
PyPI | GHSA-q5vh-6whw-x745 | Improper Authorization and Origin Validation Error in OneFuzz | ## Impact
Starting with OneFuzz 2.12.0 or greater, an incomplete authorization check allows an authenticated user from any Azure Active Directory tenant to make authorized API calls to a vulnerable OneFuzz instance.
To be vulnerable, a OneFuzz deployment must be:
* Version 2.12.0 or greater
* Deployed with the non-default [`--multi_tenant_domain`](https://github.com/microsoft/onefuzz/blob/2.30.0/src/deployment/deploy.py#L1021) option
This can result in read/write access to private data such as:
* Software vulnerability and crash information
* Security testing tools
* Proprietary code and symbols
Via authorized API calls, this also enables tampering with existing data and unauthorized code execution on Azure compute resources.
## Patches
This issue is resolved starting in release 2.31.0, via the addition of application-level check of the bearer token's `issuer` against an administrator-configured allowlist.
## Workarounds
Users can restrict access to the tenant of a deployed OneFuzz instance < 2.31.0 by redeploying in the default configuration, which omits the `--multi_tenant_domain` option.
## References
You can find an overview of the Microsoft Identity Platform [here](https://docs.microsoft.com/en-us/azure/active-directory/develop/v2-overview). This vulnerability applies to the multi-tenant application pattern, as described [here](https://docs.microsoft.com/en-us/azure/active-directory/develop/howto-convert-app-to-be-multi-tenant).
## For more information
If you have any questions or comments about this advisory:
* Open an issue in [OneFuzz](https://github.com/microsoft/onefuzz)
* Email us at [fuzzing@microsoft.com](mailto:fuzzing@microsoft.com) | {'CVE-2021-37705'} | 2022-03-03T05:12:25.126928Z | 2021-08-13T20:16:32Z | CRITICAL | null | {'CWE-346', 'CWE-285'} | {'https://github.com/microsoft/onefuzz/security/advisories/GHSA-q5vh-6whw-x745', 'https://github.com/microsoft/onefuzz/commit/2fcb4998887959b4fa11894a068d689189742cb1', 'https://github.com/microsoft/onefuzz/releases/tag/2.31.0', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37705', 'https://pypi.org/project/onefuzz/', 'https://github.com/microsoft/onefuzz/pull/1153', 'https://github.com/microsoft/onefuzz/'} | null | {'https://github.com/microsoft/onefuzz/commit/2fcb4998887959b4fa11894a068d689189742cb1'} | {'https://github.com/microsoft/onefuzz/commit/2fcb4998887959b4fa11894a068d689189742cb1'} |
PyPI | PYSEC-2021-201 | 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'} | 2021-08-27T03:22:32.823380Z | 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 | GHSA-wcv5-vrvr-3rx2 | Integer Overflow or Wraparound in TensorFlow | ### Impact
The Grappler component of TensorFlow is vulnerable to a denial of service via `CHECK`-failure (assertion failure) in [constant folding](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/constant_folding.cc#L963-L1035):
```cc
for (const auto& output_prop : output_props) {
const PartialTensorShape output_shape(output_prop.shape());
// ...
}
```
The `output_prop` tensor has a shape that is controlled by user input and this can result in triggering one of the `CHECK`s in the `PartialTensorShape` constructor. This is an instance of [TFSA-2021-198](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md) (CVE-2021-41197).
### Patches
We have patched the issue in GitHub commit [be7b286d40bc68cb0b56f702186cc4837d508058](https://github.com/tensorflow/tensorflow/commit/be7b286d40bc68cb0b56f702186cc4837d508058).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | null | 2022-03-03T05:13:17.731658Z | 2022-02-09T23:34:06Z | MODERATE | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wcv5-vrvr-3rx2', 'https://github.com/tensorflow/tensorflow/commit/be7b286d40bc68cb0b56f702186cc4837d508058', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-prcg-wp5q-rv7p'} | null | {'https://github.com/tensorflow/tensorflow/commit/be7b286d40bc68cb0b56f702186cc4837d508058'} | {'https://github.com/tensorflow/tensorflow/commit/be7b286d40bc68cb0b56f702186cc4837d508058'} |
PyPI | OSV-2021-449 | UNKNOWN READ in std::pair<absl::lts_NUMBER_02_25::container_internal::raw_hash_set<absl::lts_NUM | OSS-Fuzz report: https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=31393
```
Crash type: UNKNOWN READ
Crash state:
std::pair<absl::lts_NUMBER_02_25::container_internal::raw_hash_set<absl::lts_NUM
tensorflow::SparseCount<long, long>::Compute
tensorflow::KernelAndDeviceOp::Run
```
| null | 2022-04-13T03:04:32.371909Z | 2021-02-27T00:00:29.109256Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/7677422a56f18c10e3827e29dc8da8a7b23fbede', 'https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=31393'} | {'https://github.com/tensorflow/tensorflow/commit/0834086910de00f55876040943975e388b645e7a'} | {'https://github.com/tensorflow/tensorflow/commit/7677422a56f18c10e3827e29dc8da8a7b23fbede'} | {'https://github.com/tensorflow/tensorflow/commit/7677422a56f18c10e3827e29dc8da8a7b23fbede'} |
PyPI | GHSA-p6rw-44q7-3fw4 | Stored XSS in Jupyter nbdime | ### Impact
Improper handling of user controlled input caused a stored cross-site scripting (XSS) vulnerability. All previous versions of nbdime are affected.
### Patches
Security patches will be released for each of the major versions of the nbdime packages since version 1.x of the nbdime python package.
#### Python
- nbdime 1.x: Patched in v. 1.1.1
- nbdime 2.x: Patched in v. 2.1.1
- nbdime 3.x: Patched in v. 3.1.1
#### npm
- nbdime 6.x version: Patched in 6.1.2
- nbdime 5.x version: Patched in 5.0.2
- nbdime-jupyterlab 1.x version: Patched in 1.0.1
- nbdime-jupyterlab 2.x version: Patched in 2.1.1
### For more information
If you have any questions or comments about this advisory email us at [security@ipython.org](mailto:security@ipython.org).
| {'CVE-2021-41134'} | 2022-03-03T05:14:20.151154Z | 2021-11-08T18:09:27Z | HIGH | null | {'CWE-79'} | {'https://github.com/jupyter/nbdime/security/advisories/GHSA-p6rw-44q7-3fw4', 'https://github.com/jupyter/nbdime/commit/e44a5cc7677f24b45ebafc756db49058c2f750ea', 'https://github.com/jupyter/nbdime', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41134'} | null | {'https://github.com/jupyter/nbdime/commit/e44a5cc7677f24b45ebafc756db49058c2f750ea'} | {'https://github.com/jupyter/nbdime/commit/e44a5cc7677f24b45ebafc756db49058c2f750ea'} |
PyPI | PYSEC-2015-2 | null | ceph-deploy before 1.5.23 uses weak permissions (644) for ceph/ceph.client.admin.keyring, which allows local users to obtain sensitive information by reading the file. | {'CVE-2015-3010'} | 2021-07-05T00:01:17.275200Z | 2015-06-16T16:59:00Z | null | null | null | {'http://lists.fedoraproject.org/pipermail/package-announce/2015-April/155576.html', 'https://bugzilla.suse.com/show_bug.cgi?id=920926', 'http://www.securityfocus.com/bid/74043', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-April/155631.html', 'https://github.com/ceph/ceph-deploy/commit/eee56770393bf19ed2dd5389226c6190c08dee3f', 'http://www.openwall.com/lists/oss-security/2015/04/09/9', 'http://rhn.redhat.com/errata/RHSA-2015-1092.html', 'http://www.openwall.com/lists/oss-security/2015/04/09/11', 'https://github.com/ceph/ceph-deploy/pull/272'} | null | {'https://github.com/ceph/ceph-deploy/commit/eee56770393bf19ed2dd5389226c6190c08dee3f'} | {'https://github.com/ceph/ceph-deploy/commit/eee56770393bf19ed2dd5389226c6190c08dee3f'} |
PyPI | PYSEC-2021-714 | null | TensorFlow is an end-to-end open source platform for machine learning. 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). An attacker can craft a model such that `stride_{h,w}` values are 0. Code calling this function must validate these arguments. 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-vfr4-x8j2-3rf9', 'CVE-2021-29588'} | 2021-12-09T06:35:30.034082Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vfr4-x8j2-3rf9'} | null | {'https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d'} | {'https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d'} |
PyPI | PYSEC-2021-344 | null | OneFuzz is an open source self-hosted Fuzzing-As-A-Service platform. Starting with OneFuzz 2.12.0 or greater, an incomplete authorization check allows an authenticated user from any Azure Active Directory tenant to make authorized API calls to a vulnerable OneFuzz instance. To be vulnerable, a OneFuzz deployment must be both version 2.12.0 or greater and deployed with the non-default --multi_tenant_domain option. This can result in read/write access to private data such as software vulnerability and crash information, security testing tools and proprietary code and symbols. Via authorized API calls, this also enables tampering with existing data and unauthorized code execution on Azure compute resources. This issue is resolved starting in release 2.31.0, via the addition of application-level check of the bearer token's `issuer` against an administrator-configured allowlist. As a workaround users can restrict access to the tenant of a deployed OneFuzz instance < 2.31.0 by redeploying in the default configuration, which omits the `--multi_tenant_domain` option. | {'GHSA-q5vh-6whw-x745', 'CVE-2021-37705'} | 2021-09-26T23:32:40.198740Z | 2021-08-13T21:15:00Z | null | null | null | {'https://github.com/microsoft/onefuzz/security/advisories/GHSA-q5vh-6whw-x745', 'https://github.com/microsoft/onefuzz/commit/2fcb4998887959b4fa11894a068d689189742cb1', 'https://github.com/microsoft/onefuzz/releases/tag/2.31.0', 'https://github.com/microsoft/onefuzz/pull/1153', 'https://pypi.org/project/onefuzz/'} | null | {'https://github.com/microsoft/onefuzz/commit/2fcb4998887959b4fa11894a068d689189742cb1'} | {'https://github.com/microsoft/onefuzz/commit/2fcb4998887959b4fa11894a068d689189742cb1'} |
PyPI | PYSEC-2021-190 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can read data outside of bounds of heap allocated buffer in `tf.raw_ops.QuantizeAndDequantizeV3`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/11ff7f80667e6490d7b5174aa6bf5e01886e770f/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L237) does not validate the value of user supplied `axis` attribute before using it to index in the array backing the `input` 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. | {'CVE-2021-29553', 'GHSA-h9px-9vqg-222h'} | 2021-08-27T03:22:30.834118Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h9px-9vqg-222h', 'https://github.com/tensorflow/tensorflow/commit/99085e8ff02c3763a0ec2263e44daec416f6a387'} | null | {'https://github.com/tensorflow/tensorflow/commit/99085e8ff02c3763a0ec2263e44daec416f6a387'} | {'https://github.com/tensorflow/tensorflow/commit/99085e8ff02c3763a0ec2263e44daec416f6a387'} |
PyPI | GHSA-h6jh-7gv5-28vg | Bad alloc in `StringNGrams` caused by integer conversion | ### Impact
The implementation of `tf.raw_ops.StringNGrams` 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.StringNGrams(
data=['',''],
data_splits=[0,2],
separator=' '*100,
ngram_widths=[-80,0,0,-60],
left_pad=' ',
right_pad=' ',
pad_width=100,
preserve_short_sequences=False)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls `reserve` on a `tstring` with a value that sometimes can be negative if user supplies negative `ngram_widths`. The `reserve` method calls `TF_TString_Reserve` which has an `unsigned long` argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer.
### Patches
We have patched the issue in GitHub commit [c283e542a3f422420cfdb332414543b62fc4e4a5](https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5).
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-2021-37646'} | 2022-03-03T05:12:51.681527Z | 2021-08-25T14:43:34Z | MODERATE | null | {'CWE-681'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6jh-7gv5-28vg', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37646', 'https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5'} | null | {'https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5'} | {'https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5'} |
PyPI | PYSEC-2022-59 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `FractionalMaxPool` can be made to crash a TensorFlow process via a division by 0. 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-2022-21735', 'GHSA-87v6-crgm-2gfj'} | 2022-03-09T00:17:31.174803Z | 2022-02-03T13:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-87v6-crgm-2gfj', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/fractional_max_pool_op.cc#L36-L192'} | null | {'https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb'} | {'https://github.com/tensorflow/tensorflow/commit/ba4e8ac4dc2991e350d5cc407f8598c8d4ee70fb'} |
PyPI | PYSEC-2021-485 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.SparseMatMul`. The division by 0 occurs deep in Eigen code because the `b` tensor is empty. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-xw93-v57j-fcgh', 'CVE-2021-29557'} | 2021-12-09T06:34:52.215362Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/7f283ff806b2031f407db64c4d3edcda8fb9f9f5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xw93-v57j-fcgh'} | null | {'https://github.com/tensorflow/tensorflow/commit/7f283ff806b2031f407db64c4d3edcda8fb9f9f5'} | {'https://github.com/tensorflow/tensorflow/commit/7f283ff806b2031f407db64c4d3edcda8fb9f9f5'} |
PyPI | PYSEC-2020-256 | null | In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | {'CVE-2020-26270', 'GHSA-m648-33qf-v3gp'} | 2021-08-27T03:22:23.120464Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m648-33qf-v3gp', 'https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3'} | null | {'https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3'} | {'https://github.com/tensorflow/tensorflow/commit/14755416e364f17fb1870882fa778c7fec7f16e3'} |
PyPI | GHSA-prcg-wp5q-rv7p | Crashes due to overflow and `CHECK`-fail in ops with large tensor shapes | ### Impact
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.
For example [`AddDim`](https://github.com/tensorflow/tensorflow/blob/0b6b491d21d6a4eb5fbab1cca565bc1e94ca9543/tensorflow/core/framework/tensor_shape.cc#L395-L408) calls should be replaced by [`AddDimWithStatus`](https://github.com/tensorflow/tensorflow/blob/0b6b491d21d6a4eb5fbab1cca565bc1e94ca9543/tensorflow/core/framework/tensor_shape.cc#L410-L440).
This is similar to [CVE-2021-29584](https://github.com/tensorflow/tensorflow/blob/3a74f0307236fe206b046689c4d76f57c9b74eee/tensorflow/security/advisory/tfsa-2021-071.md) (and similar other reported vulnerabilities in TensorFlow, localized to specific APIs).
### Patches
We have patched the issue in GitHub commits [7c1692bd417eb4f9b33ead749a41166d6080af85](https://github.com/tensorflow/tensorflow/commit/7c1692bd417eb4f9b33ead749a41166d6080af85) (merging [#51732](https://github.com/tensorflow/tensorflow/pull/51732)), [d81b1351da3e8c884ff836b64458d94e4a157c15](https://github.com/tensorflow/tensorflow/commit/d81b1351da3e8c884ff836b64458d94e4a157c15) (merging [#51717](https://github.com/tensorflow/tensorflow/pull/51717)), [a871989d7b6c18cdebf2fb4f0e5c5b62fbc19edf](https://github.com/tensorflow/tensorflow/commit/a871989d7b6c18cdebf2fb4f0e5c5b62fbc19edf) (merging [#51658](https://github.com/tensorflow/tensorflow/pull/51658)), and [d81b1351da3e8c884ff836b64458d94e4a157c15](https://github.com/tensorflow/tensorflow/commit/d81b1351da3e8c884ff836b64458d94e4a157c15) (merging [#51973](https://github.com/tensorflow/tensorflow/pull/51973)). It is possible that other similar instances exist in TensorFlow, we will issue fixes as these are discovered.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported externally via [GitHub issue](https://github.com/tensorflow/tensorflow/issues/46890), [GitHub issue](https://github.com/tensorflow/tensorflow/issues/51618) and [GitHub issue](https://github.com/tensorflow/tensorflow/issues/51908). | {'CVE-2021-41197'} | 2022-03-03T05:13:16.338365Z | 2021-11-10T19:35:35Z | MODERATE | null | {'CWE-190'} | {'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://nvd.nist.gov/vuln/detail/CVE-2021-41197', 'https://github.com/tensorflow/tensorflow/issues/51908', 'https://github.com/tensorflow/tensorflow'} | 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 | PYSEC-2017-101 | null | Google gRPC before 2017-03-29 has an out-of-bounds write caused by a heap-based use-after-free related to the grpc_call_destroy function in core/lib/surface/call.c. | {'CVE-2017-8359'} | 2021-12-14T08:16:23.996016Z | 2017-04-30T17:59:00Z | null | null | null | {'https://pypi.org/project/grpcio', 'http://www.securityfocus.com/bid/98280', 'https://github.com/grpc/grpc/commit/6544a2d5d9ecdb64214da1d228886a7d15bbf5c7', 'https://github.com/grpc/grpc/pull/10353', 'https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=726', 'https://nvd.nist.gov/vuln/detail/CVE-2017-8359'} | null | {'https://github.com/grpc/grpc/commit/6544a2d5d9ecdb64214da1d228886a7d15bbf5c7'} | {'https://github.com/grpc/grpc/commit/6544a2d5d9ecdb64214da1d228886a7d15bbf5c7'} |
PyPI | GHSA-pjc4-3w99-j7v4 | Open redirect in Flask-Unchained | This affects the package Flask-Unchained before 0.9.0. When using the the _validate_redirect_url function, it is possible to bypass URL validation and redirect a user to an arbitrary URL by providing multiple back slashes such as \\\evil.com/path. This vulnerability is only exploitable if an alternative WSGI server other than Werkzeug is used, or the default behaviour of Werkzeug is modified using 'autocorrect_location_header=False. | {'CVE-2021-23393'} | 2022-03-03T05:14:13.059215Z | 2021-06-15T15:56:59Z | MODERATE | null | {'CWE-601'} | {'https://github.com/briancappello/flask-unchained/commit/71e36b28166f9ffbe0a991f51127f0984f7e6a40', 'https://nvd.nist.gov/vuln/detail/CVE-2021-23393', 'https://github.com/briancappello/flask-unchained/commit/2bfeedf1bc31df851cab8c66df0c432b10406aad', 'https://snyk.io/vuln/SNYK-PYTHON-FLASKUNCHAINED-1293189'} | null | {'https://github.com/briancappello/flask-unchained/commit/71e36b28166f9ffbe0a991f51127f0984f7e6a40', 'https://github.com/briancappello/flask-unchained/commit/2bfeedf1bc31df851cab8c66df0c432b10406aad'} | {'https://github.com/briancappello/flask-unchained/commit/71e36b28166f9ffbe0a991f51127f0984f7e6a40', 'https://github.com/briancappello/flask-unchained/commit/2bfeedf1bc31df851cab8c66df0c432b10406aad'} |
PyPI | PYSEC-2021-256 | null | TensorFlow is an end-to-end open source platform for machine learning. Passing invalid arguments (e.g., discovered via fuzzing) to `tf.raw_ops.SparseCountSparseOutput` results in segfault. 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-2021-29619', 'GHSA-wvjw-p9f5-vq28'} | 2021-08-27T03:22:42.523296Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/82e6203221865de4008445b13c69b6826d2b28d9', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wvjw-p9f5-vq28'} | null | {'https://github.com/tensorflow/tensorflow/commit/82e6203221865de4008445b13c69b6826d2b28d9'} | {'https://github.com/tensorflow/tensorflow/commit/82e6203221865de4008445b13c69b6826d2b28d9'} |
PyPI | PYSEC-2021-49 | null | PySAML2 is a pure python implementation of SAML Version 2 Standard. PySAML2 before 6.5.0 has an improper verification of cryptographic signature vulnerability. Users of pysaml2 that use the default CryptoBackendXmlSec1 backend and need to verify signed SAML documents are impacted. PySAML2 does not ensure that a signed SAML document is correctly signed. The default CryptoBackendXmlSec1 backend is using the xmlsec1 binary to verify the signature of signed SAML documents, but by default xmlsec1 accepts any type of key found within the given document. xmlsec1 needs to be configured explicitly to only use only _x509 certificates_ for the verification process of the SAML document signature. This is fixed in PySAML2 6.5.0. | {'GHSA-5p3x-r448-pc62', 'CVE-2021-21239'} | 2021-03-10T21:00:00Z | 2021-01-21T15:15:00Z | null | null | null | {'https://github.com/IdentityPython/pysaml2/commit/46578df0695269a16f1c94171f1429873f90ed99', 'https://github.com/IdentityPython/pysaml2/security/advisories/GHSA-5p3x-r448-pc62', 'https://pypi.org/project/pysaml2', 'https://www.aleksey.com/pipermail/xmlsec/2013/009717.html', 'https://github.com/IdentityPython/pysaml2/releases/tag/v6.5.0', 'https://lists.debian.org/debian-lts-announce/2021/02/msg00038.html'} | null | {'https://github.com/IdentityPython/pysaml2/commit/46578df0695269a16f1c94171f1429873f90ed99'} | {'https://github.com/IdentityPython/pysaml2/commit/46578df0695269a16f1c94171f1429873f90ed99'} |
PyPI | PYSEC-2021-606 | null | TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. 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-m539-j985-hcr8', 'CVE-2021-41196'} | 2021-12-09T06:35:06.985505Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m539-j985-hcr8', 'https://github.com/tensorflow/tensorflow/issues/51936', 'https://github.com/tensorflow/tensorflow/commit/12b1ff82b3f26ff8de17e58703231d5a02ef1b8b'} | null | {'https://github.com/tensorflow/tensorflow/commit/12b1ff82b3f26ff8de17e58703231d5a02ef1b8b'} | {'https://github.com/tensorflow/tensorflow/commit/12b1ff82b3f26ff8de17e58703231d5a02ef1b8b'} |
PyPI | PYSEC-2021-313 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a division by zero error in LSH [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/lsh_projection.cc#L118). We have patched the issue in GitHub commit 0575b640091680cfb70f4dd93e70658de43b94f9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick thiscommit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37691', 'GHSA-27qf-jwm8-g7f3'} | 2021-08-27T03:22:47.774010Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27qf-jwm8-g7f3'} | null | {'https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9'} | {'https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9'} |
PyPI | PYSEC-2021-743 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via `CHECK`-fail in `tf.strings.substr` with invalid arguments. 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-2021-29617', 'GHSA-mmq6-q8r3-48fm'} | 2021-12-09T06:35:34.887813Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/issues/46900', 'https://github.com/tensorflow/issues/46974', 'https://github.com/tensorflow/tensorflow/commit/890f7164b70354c57d40eda52dcdd7658677c09f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mmq6-q8r3-48fm'} | null | {'https://github.com/tensorflow/tensorflow/commit/890f7164b70354c57d40eda52dcdd7658677c09f'} | {'https://github.com/tensorflow/tensorflow/commit/890f7164b70354c57d40eda52dcdd7658677c09f'} |
PyPI | GHSA-4r65-35qq-ch8j | Ansible discloses sensitive information in traceback error message | Ansible is an IT automation system that handles configuration management, application deployment, cloud provisioning, ad-hoc task execution, network automation, and multi-node orchestration. A flaw was found in Ansible Engine's ansible-connection module where sensitive information, such as the Ansible user credentials, is disclosed by default in the traceback error message when Ansible receives an unexpected response from `set_options`. The highest threat from this vulnerability is confidentiality. | {'CVE-2021-3620'} | 2022-04-05T20:05:40.783294Z | 2022-03-04T00:00:17Z | MODERATE | null | {'CWE-209'} | {'https://github.com/ansible/ansible', 'https://nvd.nist.gov/vuln/detail/CVE-2021-3620', 'https://github.com/pypa/advisory-database/tree/main/vulns/ansible/PYSEC-2022-164.yaml', 'https://github.com/ansible/ansible/commit/fe28767970c8ec62aabe493c46b53a5de1e5fac0', 'https://github.com/ansible/ansible/blob/stable-2.9/changelogs/CHANGELOG-v2.9.rst#security-fixes', 'https://bugzilla.redhat.com/show_bug.cgi?id=1975767'} | null | {'https://github.com/ansible/ansible/commit/fe28767970c8ec62aabe493c46b53a5de1e5fac0'} | {'https://github.com/ansible/ansible/commit/fe28767970c8ec62aabe493c46b53a5de1e5fac0'} |
PyPI | PYSEC-2020-313 | null | In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has the same shape as the `values` one. The values in these tensors are always accessed in parallel. Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'CVE-2020-15198', 'GHSA-jc87-6vpp-7ff3'} | 2021-12-09T06:35:13.097348Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jc87-6vpp-7ff3', 'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | GHSA-hxf9-7h4c-f5jv | Django-Anymail prone to a timing attack | webhooks/base.py in Anymail (aka django-anymail) before 1.2.1 is prone to a timing attack vulnerability on the WEBHOOK_AUTHORIZATION secret, which allows remote attackers to post arbitrary e-mail tracking events. | {'CVE-2018-6596'} | 2022-04-26T18:17:09.377946Z | 2018-07-12T20:30:40Z | CRITICAL | null | {'CWE-200'} | {'https://github.com/anymail/django-anymail/releases/tag/v1.2.1', 'https://github.com/advisories/GHSA-hxf9-7h4c-f5jv', 'https://nvd.nist.gov/vuln/detail/CVE-2018-6596', 'https://github.com/anymail/django-anymail', 'https://github.com/anymail/django-anymail/commit/c07998304b4a31df4c61deddcb03d3607a04691b', 'https://github.com/anymail/django-anymail/releases/tag/v1.3', 'https://bugs.debian.org/889450', 'https://github.com/anymail/django-anymail/commit/db586ede1fbb41dce21310ea28ae15a1cf1286c5', 'https://www.debian.org/security/2018/dsa-4107'} | null | {'https://github.com/anymail/django-anymail/commit/c07998304b4a31df4c61deddcb03d3607a04691b', 'https://github.com/anymail/django-anymail/commit/db586ede1fbb41dce21310ea28ae15a1cf1286c5'} | {'https://github.com/anymail/django-anymail/commit/c07998304b4a31df4c61deddcb03d3607a04691b', 'https://github.com/anymail/django-anymail/commit/db586ede1fbb41dce21310ea28ae15a1cf1286c5'} |
PyPI | PYSEC-2020-257 | null | In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | {'CVE-2020-26271', 'GHSA-q263-fvxm-m5mw'} | 2021-08-27T03:22:23.329750Z | 2020-12-10T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q263-fvxm-m5mw', 'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b'} | null | {'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b'} | {'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b'} |
PyPI | PYSEC-2017-100 | null | Cross-site scripting (XSS) vulnerability in Cherry Music before 0.36.0 allows remote authenticated users to inject arbitrary web script or HTML via the playlistname field when creating a new playlist. | {'CVE-2015-8310'} | 2021-11-16T21:20:28.264266Z | 2017-03-27T15:59:00Z | null | null | null | {'https://github.com/devsnd/cherrymusic/commit/62dec34a1ea0741400dd6b6c660d303dcd651e86', 'http://www.securityfocus.com/bid/97148', 'http://www.fomori.org/cherrymusic/Changes.html', 'https://github.com/devsnd/cherrymusic/issues/598'} | null | {'https://github.com/devsnd/cherrymusic/commit/62dec34a1ea0741400dd6b6c660d303dcd651e86'} | {'https://github.com/devsnd/cherrymusic/commit/62dec34a1ea0741400dd6b6c660d303dcd651e86'} |
PyPI | PYSEC-2021-257 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-cgfm-62j4-v4rf', 'CVE-2021-37635'} | 2021-08-27T03:22:42.637508Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cgfm-62j4-v4rf'} | null | {'https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750'} | {'https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750'} |
PyPI | PYSEC-2021-48 | null | PySAML2 is a pure python implementation of SAML Version 2 Standard. PySAML2 before 6.5.0 has an improper verification of cryptographic signature vulnerability. All users of pysaml2 that need to validate signed SAML documents are impacted. The vulnerability is a variant of XML Signature wrapping because it did not validate the SAML document against an XML schema. This allowed invalid XML documents to be processed and such a document can trick pysaml2 with a wrapped signature. This is fixed in PySAML2 6.5.0. | {'CVE-2021-21238', 'GHSA-f4g9-h89h-jgv9'} | 2021-01-29T17:58:00Z | 2021-01-21T15:15:00Z | null | null | null | {'https://pypi.org/project/pysaml2', 'https://github.com/IdentityPython/pysaml2/releases/tag/v6.5.0', 'https://github.com/IdentityPython/pysaml2/security/advisories/GHSA-f4g9-h89h-jgv9', 'https://github.com/IdentityPython/pysaml2/commit/1d8fd268f5bf887480a403a7a5ef8f048157cc14'} | null | {'https://github.com/IdentityPython/pysaml2/commit/1d8fd268f5bf887480a403a7a5ef8f048157cc14'} | {'https://github.com/IdentityPython/pysaml2/commit/1d8fd268f5bf887480a403a7a5ef8f048157cc14'} |
PyPI | PYSEC-2021-607 | 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'} | 2021-12-09T06:35:07.147316Z | 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 | PYSEC-2021-312 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions (such as `MutableHashTableShape`) produce extra output information in the form of a `ShapeAndType` struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. `ShapeRefiner` is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37690', 'GHSA-3hxh-8cp2-g4hg'} | 2021-08-27T03:22:47.685921Z | 2021-08-13T00:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3hxh-8cp2-g4hg'} | null | {'https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1'} | {'https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1'} |
PyPI | PYSEC-2019-21 | null | An issue was discovered in Donfig 0.3.0. There is a vulnerability in the collect_yaml method in config_obj.py. It can execute arbitrary Python commands, resulting in command execution. | {'CVE-2019-7537'} | 2019-03-26T14:13:00Z | 2019-03-21T20:29:00Z | null | null | null | {'https://github.com/pytroll/donfig/issues/5', 'https://github.com/pytroll/donfig/commits/master'} | null | {'https://github.com/pytroll/donfig/commits/master'} | {'https://github.com/pytroll/donfig/commits/master'} |
PyPI | PYSEC-2021-742 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of TrySimplify(https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that result in optimizing a node with no inputs. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-4hvv-7x94-7vq8', 'CVE-2021-29616'} | 2021-12-09T06:35:34.728611Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvv-7x94-7vq8'} | null | {'https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3'} | {'https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3'} |
PyPI | PYSEC-2018-7 | null | webhooks/base.py in Anymail (aka django-anymail) before 1.2.1 is prone to a timing attack vulnerability on the WEBHOOK_AUTHORIZATION secret, which allows remote attackers to post arbitrary e-mail tracking events. | {'GHSA-hxf9-7h4c-f5jv', 'CVE-2018-6596'} | 2021-06-16T00:03:22.915097Z | 2018-02-03T21:29:00Z | null | null | null | {'https://github.com/anymail/django-anymail/releases/tag/v1.3', 'https://github.com/anymail/django-anymail/releases/tag/v1.2.1', 'https://github.com/advisories/GHSA-hxf9-7h4c-f5jv', 'https://github.com/anymail/django-anymail/commit/db586ede1fbb41dce21310ea28ae15a1cf1286c5', 'https://github.com/anymail/django-anymail/commit/c07998304b4a31df4c61deddcb03d3607a04691b', 'https://bugs.debian.org/889450', 'https://www.debian.org/security/2018/dsa-4107'} | null | {'https://github.com/anymail/django-anymail/commit/c07998304b4a31df4c61deddcb03d3607a04691b', 'https://github.com/anymail/django-anymail/commit/db586ede1fbb41dce21310ea28ae15a1cf1286c5'} | {'https://github.com/anymail/django-anymail/commit/c07998304b4a31df4c61deddcb03d3607a04691b', 'https://github.com/anymail/django-anymail/commit/db586ede1fbb41dce21310ea28ae15a1cf1286c5'} |
PyPI | GHSA-wxr6-29pv-ch68 | calibre-web is vulnerable to Cross-Site Request Forgery (CSRF) | calibre-web is vulnerable to Cross-Site Request Forgery (CSRF) | {'CVE-2021-4164'} | 2022-03-03T05:13:15.355954Z | 2022-01-21T23:44:17Z | HIGH | null | {'CWE-352'} | {'https://huntr.dev/bounties/2debace1-a0f3-45c1-95fa-9d0512680758', 'https://nvd.nist.gov/vuln/detail/CVE-2021-4164', 'https://github.com/janeczku/calibre-web', 'https://github.com/janeczku/calibre-web/commit/785726deee13b4d56f6c3503dd57c1e3eb7d6f30'} | null | {'https://github.com/janeczku/calibre-web/commit/785726deee13b4d56f6c3503dd57c1e3eb7d6f30'} | {'https://github.com/janeczku/calibre-web/commit/785726deee13b4d56f6c3503dd57c1e3eb7d6f30'} |
PyPI | PYSEC-2020-312 | null | In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'CVE-2020-15197', 'GHSA-qc53-44cj-vfvx'} | 2021-12-09T06:35:13.010994Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qc53-44cj-vfvx', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | PYSEC-2021-191 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division. Since `data` is given by the `values` argument, `num_batch_elements` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected. | {'GHSA-qg48-85hg-mqc5', 'CVE-2021-29554'} | 2021-08-27T03:22:31.001831Z | 2021-05-14T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qg48-85hg-mqc5', 'https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f'} | null | {'https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f'} | {'https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f'} |
PyPI | GHSA-x84v-xcm2-53pg | Moderate severity vulnerability that affects requests | The Requests package through 2.19.1 before 2018-09-14 for Python sends an HTTP Authorization header to an http URI upon receiving a same-hostname https-to-http redirect, which makes it easier for remote attackers to discover credentials by sniffing the network. | {'CVE-2018-18074'} | 2022-03-03T05:13:34.123639Z | 2018-10-29T19:06:46Z | HIGH | null | {'CWE-522'} | {'http://lists.opensuse.org/opensuse-security-announce/2019-07/msg00024.html', 'https://github.com/requests/requests/pull/4718', 'https://usn.ubuntu.com/3790-2/', 'https://github.com/requests/requests/issues/4716', 'https://github.com/requests/requests/commit/c45d7c49ea75133e52ab22a8e9e13173938e36ff', 'https://usn.ubuntu.com/3790-1/', 'https://bugs.debian.org/910766', 'http://docs.python-requests.org/en/master/community/updates/#release-and-version-history', 'https://nvd.nist.gov/vuln/detail/CVE-2018-18074', 'https://github.com/advisories/GHSA-x84v-xcm2-53pg', 'https://github.com/requests/requests', 'https://access.redhat.com/errata/RHSA-2019:2035'} | null | {'https://github.com/requests/requests/commit/c45d7c49ea75133e52ab22a8e9e13173938e36ff'} | {'https://github.com/requests/requests/commit/c45d7c49ea75133e52ab22a8e9e13173938e36ff'} |
PyPI | GHSA-whr9-vfh2-7hm6 | Memory corruption in `DrawBoundingBoxesV2` | ### Impact
The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:
```python
import tensorflow as tf
images = tf.fill([10, 96, 0, 1], 0.)
boxes = tf.fill([10, 53, 0], 0.)
colors = tf.fill([0, 1], 0.)
tf.raw_ops.DrawBoundingBoxesV2(images=images, boxes=boxes, colors=colors)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption:
```cc
const auto tboxes = boxes.tensor<T, 3>();
for (int64 bb = 0; bb < num_boxes; ++bb) {
...
const int64 min_box_row = static_cast<float>(tboxes(b, bb, 0)) * (height - 1);
const int64 max_box_row = static_cast<float>(tboxes(b, bb, 2)) * (height - 1);
const int64 min_box_col = static_cast<float>(tboxes(b, bb, 1)) * (width - 1);
const int64 max_box_col = static_cast<float>(tboxes(b, bb, 3)) * (width - 1);
...
}
```
If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices.
### Patches
We have patched the issue in GitHub commit [79865b542f9ffdc9caeb255631f7c56f1d4b6517](https://github.com/tensorflow/tensorflow/commit/79865b542f9ffdc9caeb255631f7c56f1d4b6517).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. | {'CVE-2021-29571'} | 2022-03-03T05:13:08.896921Z | 2021-05-21T14:25:28Z | MODERATE | null | {'CWE-787'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29571', 'https://github.com/tensorflow/tensorflow/commit/79865b542f9ffdc9caeb255631f7c56f1d4b6517', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-whr9-vfh2-7hm6'} | null | {'https://github.com/tensorflow/tensorflow/commit/79865b542f9ffdc9caeb255631f7c56f1d4b6517'} | {'https://github.com/tensorflow/tensorflow/commit/79865b542f9ffdc9caeb255631f7c56f1d4b6517'} |
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