ecosystem
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⌀ | details
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13.5k
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⌀ | modified_date
stringdate 2019-03-26 14:13:00
2022-05-10 08:46:52
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stringdate 2012-06-17 03:41:00
2022-05-10 08:46:50
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⌀ | cwe_id
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | PYSEC-2016-2 | null | Cross-site scripting (XSS) vulnerability in the dismissChangeRelatedObjectPopup function in contrib/admin/static/admin/js/admin/RelatedObjectLookups.js in Django before 1.8.14, 1.9.x before 1.9.8, and 1.10.x before 1.10rc1 allows remote attackers to inject arbitrary web script or HTML via vectors involving unsafe usage of Element.innerHTML. | {'CVE-2016-6186'} | 2021-09-01T08:35:44.164135Z | 2016-08-05T15:59:00Z | null | null | null | {'http://rhn.redhat.com/errata/RHSA-2016-1595.html', 'https://github.com/django/django/commit/d03bf6fe4e9bf5b07de62c1a271c4b41a7d3d158', 'http://packetstormsecurity.com/files/137965/Django-3.3.0-Script-Insertion.html', 'http://www.securityfocus.com/archive/1/538947/100/0/threaded', 'https://github.com/django/django/commit/f68e5a99164867ab0e071a936470958ed867479d', 'http://www.securitytracker.com/id/1036338', 'https://www.exploit-db.com/exploits/40129/', 'http://www.vulnerability-lab.com/get_content.php?id=1869', 'http://www.securityfocus.com/bid/92058', 'http://rhn.redhat.com/errata/RHSA-2016-1594.html', 'http://seclists.org/fulldisclosure/2016/Jul/53', 'https://www.djangoproject.com/weblog/2016/jul/18/security-releases/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/KHHPN6MISX5I6UTXQHYLPTLEEUE6WDXW/', 'http://www.ubuntu.com/usn/USN-3039-1', 'http://www.debian.org/security/2016/dsa-3622', 'http://rhn.redhat.com/errata/RHSA-2016-1596.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DMLLFAUT4J4IP4P2KI4NOVWRMHA22WUJ/'} | null | {'https://github.com/django/django/commit/f68e5a99164867ab0e071a936470958ed867479d', 'https://github.com/django/django/commit/d03bf6fe4e9bf5b07de62c1a271c4b41a7d3d158'} | {'https://github.com/django/django/commit/d03bf6fe4e9bf5b07de62c1a271c4b41a7d3d158', 'https://github.com/django/django/commit/f68e5a99164867ab0e071a936470958ed867479d'} |
PyPI | PYSEC-2021-802 | 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'} | 2021-12-09T06:35:40.308304Z | 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 | GHSA-8rh6-h94m-vj54 | Incorrect Comparison in cvxopt | Incomplete string comparison vulnerability exits in cvxopt.org cvxop <= 1.2.6 in APIs (cvxopt.cholmod.diag, cvxopt.cholmod.getfactor, cvxopt.cholmod.solve, cvxopt.cholmod.spsolve), which allows attackers to conduct Denial of Service attacks by construct fake Capsule objects. | {'CVE-2021-41500'} | 2022-03-03T05:12:30.492737Z | 2022-01-07T00:01:11Z | HIGH | null | {'CWE-697'} | {'https://github.com/cvxopt/cvxopt/issues/193', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41500', 'https://github.com/cvxopt/cvxopt', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/CXTPM3DGVYTYQ54OFCMXZVWVOMR7JM2D/', 'https://github.com/cvxopt/cvxopt/commit/d5a21cf1da62e4269176384b1ff62edac5579f94'} | null | {'https://github.com/cvxopt/cvxopt/commit/d5a21cf1da62e4269176384b1ff62edac5579f94'} | {'https://github.com/cvxopt/cvxopt/commit/d5a21cf1da62e4269176384b1ff62edac5579f94'} |
PyPI | GHSA-x823-j7c4-vpc5 | Cross-site scripting in sickrage | In SiCKRAGE, versions 9.3.54.dev1 to 10.0.11.dev1 are vulnerable to Reflected Cross-Site-Scripting (XSS) due to user input not being validated properly in the `quicksearch` feature. Therefore, an attacker can steal a user's sessionID to masquerade as a victim user, to carry out any actions in the context of the user. | {'CVE-2021-25926'} | 2022-03-03T05:13:33.888255Z | 2021-04-20T16:31:43Z | MODERATE | null | {'CWE-79'} | {'https://github.com/SiCKRAGE/SiCKRAGE/commit/9f42426727e16609ad3d1337f6637588b8ed28e4', 'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25926,', 'https://nvd.nist.gov/vuln/detail/CVE-2021-25926'} | null | {'https://github.com/SiCKRAGE/SiCKRAGE/commit/9f42426727e16609ad3d1337f6637588b8ed28e4'} | {'https://github.com/SiCKRAGE/SiCKRAGE/commit/9f42426727e16609ad3d1337f6637588b8ed28e4'} |
PyPI | PYSEC-2021-551 | null | TensorFlow is an end-to-end open source platform for machine learning. Sending invalid argument for `row_partition_types` of `tf.raw_ops.RaggedTensorToTensor` API results in a null pointer dereference and undefined behavior. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L328) accesses the first element of a user supplied list of values without validating that the provided list is not empty. We have patched the issue in GitHub commit 301ae88b331d37a2a16159b65b255f4f9eb39314. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-hwr7-8gxx-fj5p', 'CVE-2021-37638'} | 2021-12-09T06:35:02.233432Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hwr7-8gxx-fj5p'} | null | {'https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314'} | {'https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314'} |
PyPI | GHSA-8vj2-vxx3-667w | Arbitrary expression injection in Pillow | PIL.ImageMath.eval in Pillow before 9.0.0 allows evaluation of arbitrary expressions, such as ones that use the Python exec method `ImageMath.eval("exec(exit())")`.
While Pillow 9.0.0 restricted top-level builtins available to PIL.ImageMath.eval(), it did not prevent builtins available to lambda expressions. These are now also restricted in 9.0.1. | {'CVE-2022-22817'} | 2022-03-11T23:47:55.666690Z | 2022-01-12T20:07:33Z | CRITICAL | null | {'CWE-74'} | {'https://pillow.readthedocs.io/en/stable/releasenotes/9.0.1.html#security', 'https://nvd.nist.gov/vuln/detail/CVE-2022-22817', 'https://www.debian.org/security/2022/dsa-5053', 'https://pillow.readthedocs.io/en/stable/releasenotes/9.0.0.html#restrict-builtins-available-to-imagemath-eval', 'https://lists.debian.org/debian-lts-announce/2022/01/msg00018.html', 'https://github.com/python-pillow/Pillow/commit/8531b01d6cdf0b70f256f93092caa2a5d91afc11', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/8531b01d6cdf0b70f256f93092caa2a5d91afc11'} | {'https://github.com/python-pillow/Pillow/commit/8531b01d6cdf0b70f256f93092caa2a5d91afc11'} |
PyPI | PYSEC-2021-228 | null | TensorFlow is an end-to-end open source platform for machine learning. TFlite graphs must not have loops between nodes. However, this condition was not checked and an attacker could craft models that would result in infinite loop during evaluation. In certain cases, the infinite loop would be replaced by stack overflow due to too many recursive calls. For example, the `While` implementation(https://github.com/tensorflow/tensorflow/blob/106d8f4fb89335a2c52d7c895b7a7485465ca8d9/tensorflow/lite/kernels/while.cc) could be tricked into a scneario where both the body and the loop subgraphs are the same. Evaluating one of the subgraphs means calling the `Eval` function for the other and this quickly exhaust all stack space. 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. Please consult our security guide(https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2021-29591', 'GHSA-cwv3-863g-39vx'} | 2021-08-27T03:22:37.582991Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cwv3-863g-39vx', 'https://github.com/tensorflow/tensorflow/commit/c6173f5fe66cdbab74f4f869311fe6aae2ba35f4', 'https://github.com/tensorflow/tensorflow/commit/9c1dc920d8ffb4893d6c9d27d1f039607b326743'} | null | {'https://github.com/tensorflow/tensorflow/commit/c6173f5fe66cdbab74f4f869311fe6aae2ba35f4', 'https://github.com/tensorflow/tensorflow/commit/9c1dc920d8ffb4893d6c9d27d1f039607b326743'} | {'https://github.com/tensorflow/tensorflow/commit/c6173f5fe66cdbab74f4f869311fe6aae2ba35f4', 'https://github.com/tensorflow/tensorflow/commit/9c1dc920d8ffb4893d6c9d27d1f039607b326743'} |
PyPI | PYSEC-2021-678 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar<T>()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. 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-2021-29552', 'GHSA-jhq9-wm9m-cf89'} | 2021-12-09T06:35:23.792052Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jhq9-wm9m-cf89'} | null | {'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe'} | {'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe'} |
PyPI | PYSEC-2021-382 | null | qutebrowser is an open source keyboard-focused browser with a minimal GUI. Starting with qutebrowser v1.7.0, the Windows installer for qutebrowser registers a `qutebrowserurl:` URL handler. With certain applications, opening a specially crafted `qutebrowserurl:...` URL can lead to execution of qutebrowser commands, which in turn allows arbitrary code execution via commands such as `:spawn` or `:debug-pyeval`. Only Windows installs where qutebrowser is registered as URL handler are affected. The issue has been fixed in qutebrowser v2.4.0. The fix also adds additional hardening for potential similar issues on Linux (by adding the new --untrusted-args flag to the .desktop file), though no such vulnerabilities are known. | {'CVE-2021-41146', 'GHSA-vw27-fwjf-5qxm'} | 2021-10-28T05:27:07.120992Z | 2021-10-21T18:15:00Z | null | null | null | {'https://github.com/qutebrowser/qutebrowser/commit/8f46ba3f6dc7b18375f7aa63c48a1fe461190430', 'https://github.com/qutebrowser/qutebrowser/security/advisories/GHSA-vw27-fwjf-5qxm'} | null | {'https://github.com/qutebrowser/qutebrowser/commit/8f46ba3f6dc7b18375f7aa63c48a1fe461190430'} | {'https://github.com/qutebrowser/qutebrowser/commit/8f46ba3f6dc7b18375f7aa63c48a1fe461190430'} |
PyPI | GHSA-6f89-8j54-29xf | Heap buffer overflow in `FractionalAvgPoolGrad` | ### Impact
The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow:
```python
import tensorflow as tf
orig_input_tensor_shape = tf.constant([1, 3, 2, 3], shape=[4], dtype=tf.int64)
out_backprop = tf.constant([2], shape=[1, 1, 1, 1], dtype=tf.int64)
row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
tf.raw_ops.FractionalAvgPoolGrad(
orig_input_tensor_shape=orig_input_tensor_shape, out_backprop=out_backprop,
row_pooling_sequence=row_pooling_sequence,
col_pooling_sequence=col_pooling_sequence, overlapping=False)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape.
### Patches
We have patched the issue in GitHub commit [12c727cee857fa19be717f336943d95fca4ffe4f](https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29578'} | 2022-03-03T05:12:50.602818Z | 2021-05-21T14:26:21Z | LOW | null | {'CWE-787', 'CWE-119'} | {'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6f89-8j54-29xf', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29578'} | null | {'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f'} | {'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f'} |
PyPI | PYSEC-2021-697 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/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. 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. 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-2021-29571', 'GHSA-whr9-vfh2-7hm6'} | 2021-12-09T06:35:27.008570Z | 2021-05-14T20:15:00Z | null | null | null | {'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'} |
PyPI | PYSEC-2022-70 | null | Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions. 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-9c78-vcq7-7vxq', 'CVE-2022-23561'} | 2022-03-09T00:17:32.561735Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/6c0b2b70eeee588591680f5b7d5d38175fd7cdf6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c78-vcq7-7vxq'} | null | {'https://github.com/tensorflow/tensorflow/commit/6c0b2b70eeee588591680f5b7d5d38175fd7cdf6'} | {'https://github.com/tensorflow/tensorflow/commit/6c0b2b70eeee588591680f5b7d5d38175fd7cdf6'} |
PyPI | GHSA-4hvf-hxvg-f67v | Read and Write outside of bounds in TensorFlow | ### Impact
An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in [the conversion from sparse tensors to dense tensors](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/internal/utils/sparsity_format_converter.cc#L252-L293).
### Patches
We have patched the issue in GitHub commit [6364463d6f5b6254cac3d6aedf999b6a96225038](https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Wang Xuan of Qihoo 360 AIVul Team. | {'CVE-2022-23560'} | 2022-03-03T05:13:30.212098Z | 2022-02-09T23:53:30Z | HIGH | null | {'CWE-787', 'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-23560', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v', 'https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038', 'https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/internal/utils/sparsity_format_converter.cc#L252-L293'} | null | {'https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038'} | {'https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038'} |
PyPI | PYSEC-2021-443 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixDiag*` operations(https://github.com/tensorflow/tensorflow/blob/4c4f420e68f1cfaf8f4b6e8e3eb857e9e4c3ff33/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L195-L197) does not validate that the tensor arguments are non-empty. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29515', 'GHSA-hc6c-75p4-hmq4'} | 2021-12-09T06:34:45.694528Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hc6c-75p4-hmq4'} | null | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6'} | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6'} |
PyPI | PYSEC-2021-156 | null | TensorFlow is an end-to-end open source platform for machine learning. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-772j-h9xw-ffp5', 'CVE-2021-29519'} | 2021-08-27T03:22:24.765492Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772j-h9xw-ffp5'} | null | {'https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025'} | {'https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025'} |
PyPI | PYSEC-2021-506 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29578', 'GHSA-6f89-8j54-29xf'} | 2021-12-09T06:34:55.459344Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6f89-8j54-29xf'} | null | {'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f'} | {'https://github.com/tensorflow/tensorflow/commit/12c727cee857fa19be717f336943d95fca4ffe4f'} |
PyPI | GHSA-3hxh-8cp2-g4hg | Use after free and segfault in shape inference functions | ### Impact
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.
### Patches
We have patched the issue in GitHub commit [ee119d4a498979525046fba1c3dd3f13a039fbb1](https://github.com/tensorflow/tensorflow/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.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2021-37690'} | 2022-03-03T05:11:34.622229Z | 2021-08-25T14:39:22Z | MODERATE | null | {'CWE-416'} | {'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37690', 'https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3hxh-8cp2-g4hg'} | null | {'https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1'} | {'https://github.com/tensorflow/tensorflow/commit/ee119d4a498979525046fba1c3dd3f13a039fbb1'} |
PyPI | PYSEC-2021-60 | null | Tenable for Jira Cloud is an open source project designed to pull Tenable.io vulnerability data, then generate Jira Tasks and sub-tasks based on the vulnerabilities' current state. It published in pypi as "tenable-jira-cloud". In tenable-jira-cloud before version 1.1.21, it is possible to run arbitrary commands through the yaml.load() method. This could allow an attacker with local access to the host to run arbitrary code by running the application with a specially crafted YAML configuration file. This is fixed in version 1.1.21 by using yaml.safe_load() instead of yaml.load(). | {'GHSA-8278-88vv-x98r', 'CVE-2021-21371'} | 2021-03-18T20:38:00Z | 2021-03-10T22:15:00Z | null | null | null | {'https://github.com/tenable/integration-jira-cloud/security/advisories/GHSA-8278-88vv-x98r', 'https://github.com/tenable/integration-jira-cloud/commit/f8c2095fd529e664e7fa25403a0a4a85bb3907d0', 'https://pyyaml.docsforge.com/master/documentation/#loading-yaml', 'https://pypi.org/project/tenable-jira-cloud/'} | null | {'https://github.com/tenable/integration-jira-cloud/commit/f8c2095fd529e664e7fa25403a0a4a85bb3907d0'} | {'https://github.com/tenable/integration-jira-cloud/commit/f8c2095fd529e664e7fa25403a0a4a85bb3907d0'} |
PyPI | PYSEC-2021-785 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a segmentation fault in `tf.raw_ops.MaxPoolGrad` caused by missing validation. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/maxpooling_op.cc) misses some validation for the `orig_input` and `orig_output` tensors. The fixes for CVE-2021-29579 were incomplete. We have patched the issue in GitHub commit 136b51f10903e044308cf77117c0ed9871350475. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-7ghq-fvr3-pj2x', 'CVE-2021-37674'} | 2021-12-09T06:35:38.809791Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7ghq-fvr3-pj2x', 'https://github.com/tensorflow/tensorflow/commit/136b51f10903e044308cf77117c0ed9871350475', 'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-068.md'} | null | {'https://github.com/tensorflow/tensorflow/commit/136b51f10903e044308cf77117c0ed9871350475'} | {'https://github.com/tensorflow/tensorflow/commit/136b51f10903e044308cf77117c0ed9871350475'} |
PyPI | PYSEC-2020-290 | null | In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15210', 'GHSA-x9j7-x98r-r4w2'} | 2021-12-09T06:34:43.437178Z | 2020-09-25T19:15:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453'} | {'https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453'} |
PyPI | GHSA-hhvc-g5hv-48c6 | Write to immutable memory region in TensorFlow | ### Impact
The `tf.raw_ops.ImmutableConst` operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area:
```python
>>> import tensorflow as tf
>>> with open('/tmp/test.txt','w') as f: f.write('a'*128)
>>> tf.raw_ops.ImmutableConst(dtype=tf.string,shape=2,
memory_region_name='/tmp/test.txt')
```
If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault.
This is because the alocator used to return the buffer data is not marked as returning an opaque handle since the [needed virtual method](https://github.com/tensorflow/tensorflow/blob/c1e1fc899ad5f8c725dcbb6470069890b5060bc7/tensorflow/core/framework/typed_allocator.h#L78-L85) is [not overriden](https://github.com/tensorflow/tensorflow/blob/acdf3c04fcfa767ae8d109b9e1f727ef050dba4d/tensorflow/core/kernels/immutable_constant_op.cc).
### Patches
We have patched the issue in GitHub commit [c1e1fc899ad5f8c725dcbb6470069890b5060bc7](https://github.com/tensorflow/tensorflow/commit/c1e1fc899ad5f8c725dcbb6470069890b5060bc7) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2020-26268'} | 2022-03-03T05:12:48.750740Z | 2020-12-10T19:07:28Z | LOW | null | {'CWE-471'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hhvc-g5hv-48c6', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26268', 'https://github.com/tensorflow/tensorflow/commit/c1e1fc899ad5f8c725dcbb6470069890b5060bc7'} | null | {'https://github.com/tensorflow/tensorflow/commit/c1e1fc899ad5f8c725dcbb6470069890b5060bc7'} | {'https://github.com/tensorflow/tensorflow/commit/c1e1fc899ad5f8c725dcbb6470069890b5060bc7'} |
PyPI | PYSEC-2021-290 | 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'} | 2021-08-27T03:22:45.672870Z | 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 | GHSA-6jp6-9rf9-gc66 | Cross-site Scripting in Weblate | ### Impact
Due to improper neutralization, it was possible to perform cross-site scripting via crafted user and language names.
### Patches
The issues were fixed in the 4.11 release. The following commits are addressing it:
* f6753a1a1c63fade6ad418fbda827c6750ab0bda
* 9e19a8414337692cc90da2a91c9af5420f2952f1
* 22d577b1f1e88665a88b4569380148030e0f8389
### Workarounds
You can look for crafted user and language names to see if you were affected.
### References
* https://hackerone.com/reports/1486674
* https://hackerone.com/reports/1486718
* https://hackerone.com/reports/1485226
### For more information
If you have any questions or comments about this advisory:
* Open a topic in [discussions](https://github.com/WeblateOrg/weblate/discussions)
* Email us at [care@weblate.org](mailto:care@weblate.org) | {'CVE-2022-24710'} | 2022-03-09T21:16:52.565898Z | 2022-02-25T22:18:50Z | MODERATE | null | {'CWE-79'} | {'https://github.com/WeblateOrg/weblate/security/advisories/GHSA-6jp6-9rf9-gc66', 'https://github.com/WeblateOrg/weblate/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24710', 'https://github.com/WeblateOrg/weblate/commit/9e19a8414337692cc90da2a91c9af5420f2952f1', 'https://github.com/WeblateOrg/weblate/commit/22d577b1f1e88665a88b4569380148030e0f8389', 'https://github.com/WeblateOrg/weblate/commit/f6753a1a1c63fade6ad418fbda827c6750ab0bda'} | null | {'https://github.com/WeblateOrg/weblate/commit/22d577b1f1e88665a88b4569380148030e0f8389', 'https://github.com/WeblateOrg/weblate/commit/9e19a8414337692cc90da2a91c9af5420f2952f1', 'https://github.com/WeblateOrg/weblate/commit/f6753a1a1c63fade6ad418fbda827c6750ab0bda'} | {'https://github.com/WeblateOrg/weblate/commit/22d577b1f1e88665a88b4569380148030e0f8389', 'https://github.com/WeblateOrg/weblate/commit/9e19a8414337692cc90da2a91c9af5420f2952f1', 'https://github.com/WeblateOrg/weblate/commit/f6753a1a1c63fade6ad418fbda827c6750ab0bda'} |
PyPI | GHSA-fq86-3f29-px2c | `CHECK`-failures during Grappler's `IsSimplifiableReshape` in Tensorflow | ### Impact
The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a `SavedModel` such that [`IsSimplifiableReshape`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/constant_folding.cc#L1687-L1742) would trigger `CHECK` failures.
### Patches
We have patched the issue in GitHub commits [ebc1a2ffe5a7573d905e99bd0ee3568ee07c12c1](https://github.com/tensorflow/tensorflow/commit/ebc1a2ffe5a7573d905e99bd0ee3568ee07c12c1), [1fb27733f943295d874417630edd3b38b34ce082](https://github.com/tensorflow/tensorflow/commit/1fb27733f943295d874417630edd3b38b34ce082), and [240655511cd3e701155f944a972db71b6c0b1bb6](https://github.com/tensorflow/tensorflow/commit/240655511cd3e701155f944a972db71b6c0b1bb6).
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-2022-23581'} | 2022-03-03T05:13:59.913859Z | 2022-02-07T22:01:14Z | MODERATE | null | {'CWE-617'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-23581', 'https://github.com/tensorflow/tensorflow/commit/1fb27733f943295d874417630edd3b38b34ce082', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq86-3f29-px2c', 'https://github.com/tensorflow/tensorflow/commit/240655511cd3e701155f944a972db71b6c0b1bb6', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/constant_folding.cc#L1687-L1742', 'https://github.com/tensorflow/tensorflow/commit/ebc1a2ffe5a7573d905e99bd0ee3568ee07c12c1'} | null | {'https://github.com/tensorflow/tensorflow/commit/1fb27733f943295d874417630edd3b38b34ce082', 'https://github.com/tensorflow/tensorflow/commit/240655511cd3e701155f944a972db71b6c0b1bb6', 'https://github.com/tensorflow/tensorflow/commit/ebc1a2ffe5a7573d905e99bd0ee3568ee07c12c1'} | {'https://github.com/tensorflow/tensorflow/commit/240655511cd3e701155f944a972db71b6c0b1bb6', 'https://github.com/tensorflow/tensorflow/commit/ebc1a2ffe5a7573d905e99bd0ee3568ee07c12c1', 'https://github.com/tensorflow/tensorflow/commit/1fb27733f943295d874417630edd3b38b34ce082'} |
PyPI | PYSEC-2021-455 | 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.QuantizedConv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/00e9a4d67d76703fa1aee33dac582acf317e0e81/tensorflow/core/kernels/quantized_conv_ops.cc#L257-L259) 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-x4g7-fvjj-prg8', 'CVE-2021-29527'} | 2021-12-09T06:34:47.577181Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4g7-fvjj-prg8'} | null | {'https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b'} | {'https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b'} |
PyPI | PYSEC-2022-149 | null | Tensorflow is an Open Source Machine Learning Framework. When decoding PNG images 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. 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-fq6p-6334-8gr4', 'CVE-2022-23585'} | 2022-03-09T00:18:29.163401Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/image/decode_image_op.cc#L322-L416', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq6p-6334-8gr4', 'https://github.com/tensorflow/tensorflow/commit/ab51e5b813573dc9f51efa335aebcf2994125ee9'} | null | {'https://github.com/tensorflow/tensorflow/commit/ab51e5b813573dc9f51efa335aebcf2994125ee9'} | {'https://github.com/tensorflow/tensorflow/commit/ab51e5b813573dc9f51efa335aebcf2994125ee9'} |
PyPI | PYSEC-2022-66 | 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'} | 2022-03-09T00:17:32.048410Z | 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 | PYSEC-2019-106 | null | NLTK Downloader before 3.4.5 is vulnerable to a directory traversal, allowing attackers to write arbitrary files via a ../ (dot dot slash) in an NLTK package (ZIP archive) that is mishandled during extraction. | {'CVE-2019-14751', 'GHSA-mr7p-25v2-35wr'} | 2020-03-27T10:15:00Z | 2019-08-22T16:15:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2020-04/msg00001.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/QI4IJGLZQ5S7C5LNRNROHAO2P526XE3D/', 'https://github.com/mssalvatore/CVE-2019-14751_PoC', 'https://github.com/nltk/nltk/blob/3.4.5/ChangeLog', 'https://github.com/advisories/GHSA-mr7p-25v2-35wr', 'https://github.com/nltk/nltk/commit/f59d7ed8df2e0e957f7f247fe218032abdbe9a10', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/ZGZSSEJH7RHH3RBUEVWWYT75QU67J7SE/', 'http://lists.opensuse.org/opensuse-security-announce/2020-03/msg00054.html', 'https://salvatoresecurity.com/zip-slip-in-nltk-cve-2019-14751/'} | null | {'https://github.com/nltk/nltk/commit/f59d7ed8df2e0e957f7f247fe218032abdbe9a10'} | {'https://github.com/nltk/nltk/commit/f59d7ed8df2e0e957f7f247fe218032abdbe9a10'} |
PyPI | GHSA-mmq6-q8r3-48fm | Crash in `tf.strings.substr` due to `CHECK`-fail | ### Impact
An attacker can cause a denial of service via `CHECK`-fail in `tf.strings.substr` with invalid arguments:
```python
import tensorflow as tf
tf.strings.substr(input='abc', len=1, pos=[1,-1])
```
```python
import tensorflow as tf
tf.strings.substr(input='abc', len=1, pos=[1,2])
```
### Patches
We have received a patch for the issue in GitHub commit [890f7164b70354c57d40eda52dcdd7658677c09f](https://github.com/tensorflow/tensorflow/commit/890f7164b70354c57d40eda52dcdd7658677c09f).
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 in [#46900](https://github.com/tensorflow/issues/46900) and fixed in [#46974](https://github.com/tensorflow/issues/46974). | {'CVE-2021-29617'} | 2022-03-03T05:12:45.549998Z | 2021-05-21T14:28:50Z | LOW | null | {'CWE-755'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29617', '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 | PYSEC-2022-89 | null | Tensorflow is an Open Source Machine Learning Framework. During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user. 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-2022-23580', 'GHSA-627q-g293-49q7'} | 2022-03-09T00:17:34.891439Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-627q-g293-49q7', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.cc#L788-L790', 'https://github.com/tensorflow/tensorflow/commit/1361fb7e29449629e1df94d44e0427ebec8c83c7'} | null | {'https://github.com/tensorflow/tensorflow/commit/1361fb7e29449629e1df94d44e0427ebec8c83c7'} | {'https://github.com/tensorflow/tensorflow/commit/1361fb7e29449629e1df94d44e0427ebec8c83c7'} |
PyPI | GHSA-vj42-xq3r-hr3r | Out-of-bounds reads in Pillow | In libImaging/Jpeg2KDecode.c in Pillow before 7.0.0, there are multiple out-of-bounds reads via a crafted JP2 file. | {'CVE-2020-10994'} | 2022-03-03T05:14:03.052947Z | 2020-07-27T21:52:39Z | MODERATE | null | {'CWE-125'} | {'https://github.com/python-pillow/Pillow/pull/4505', 'https://github.com/python-pillow/Pillow/commits/master/src/libImaging/', 'https://nvd.nist.gov/vuln/detail/CVE-2020-10994', 'https://snyk.io/vuln/SNYK-PYTHON-PILLOW-574575', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/HOKHNWV2VS5GESY7IBD237E7C6T3I427/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/BEBCPE4F2VHTIT6EZA2YZQZLPVDEBJGD/', 'https://github.com/python-pillow/Pillow/pull/4538', 'https://usn.ubuntu.com/4430-2/', 'https://github.com/python-pillow/Pillow/commit/ff60894d697d1992147b791101ad53a8bf1352e4', 'https://github.com/python-pillow/Pillow/blob/master/docs/releasenotes/7.1.0.rst#security', 'https://pillow.readthedocs.io/en/stable/releasenotes/7.1.0.html', 'https://usn.ubuntu.com/4430-1/', 'https://pillow.readthedocs.io/en/stable/releasenotes/', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/ff60894d697d1992147b791101ad53a8bf1352e4'} | {'https://github.com/python-pillow/Pillow/commit/ff60894d697d1992147b791101ad53a8bf1352e4'} |
PyPI | GHSA-rrx2-r989-2c43 | Integer overflows in Tensorflow | ### Impact
The [implementations of `Sparse*Cwise*` ops](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc) are vulnerable to integer overflows. These can be used to trigger large allocations (so, OOM based denial of service) or `CHECK`-fails when building new `TensorShape` objects (so, assert failures based denial of service):
```python
import tensorflow as tf
import numpy as np
tf.raw_ops.SparseDenseCwiseDiv(
sp_indices=np.array([[9]]),
sp_values=np.array([5]),
sp_shape=np.array([92233720368., 92233720368]),
dense=np.array([4]))
```
We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. The latter is an instance of [TFSA-2021-198](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md) (CVE-2021-41197) and is easily fixed by replacing a call to `TensorShape` constructor with a call to `BuildTensorShape` static helper factory.
### Patches
We have patched the issue in GitHub commits [1b54cadd19391b60b6fcccd8d076426f7221d5e8](https://github.com/tensorflow/tensorflow/commit/1b54cadd19391b60b6fcccd8d076426f7221d5e8) and [e952a89b7026b98fe8cbe626514a93ed68b7c510](https://github.com/tensorflow/tensorflow/commit/e952a89b7026b98fe8cbe626514a93ed68b7c510).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Faysal Hossain Shezan from University of Virginia. | {'CVE-2022-23567'} | 2022-03-03T05:13:29.706090Z | 2022-02-09T23:39:33Z | MODERATE | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/commit/1b54cadd19391b60b6fcccd8d076426f7221d5e8', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23567', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/e952a89b7026b98fe8cbe626514a93ed68b7c510', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrx2-r989-2c43', 'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md'} | null | {'https://github.com/tensorflow/tensorflow/commit/1b54cadd19391b60b6fcccd8d076426f7221d5e8', 'https://github.com/tensorflow/tensorflow/commit/e952a89b7026b98fe8cbe626514a93ed68b7c510'} | {'https://github.com/tensorflow/tensorflow/commit/1b54cadd19391b60b6fcccd8d076426f7221d5e8', 'https://github.com/tensorflow/tensorflow/commit/e952a89b7026b98fe8cbe626514a93ed68b7c510'} |
PyPI | PYSEC-2017-6 | null | attic before 0.15 does not confirm unencrypted backups with the user, which allows remote attackers with read and write privileges for the encrypted repository to obtain potentially sensitive information by changing the manifest type byte of the repository to "unencrypted / without key file". | {'CVE-2015-4082'} | 2021-07-05T00:01:17.176184Z | 2017-08-18T16:29:00Z | null | null | null | {'https://github.com/jborg/attic/issues/271', 'http://www.openwall.com/lists/oss-security/2015/05/31/3', 'https://github.com/jborg/attic/commit/78f9ad1faba7193ca7f0acccbc13b1ff6ebf9072', 'http://www.securityfocus.com/bid/74821'} | null | {'https://github.com/jborg/attic/commit/78f9ad1faba7193ca7f0acccbc13b1ff6ebf9072'} | {'https://github.com/jborg/attic/commit/78f9ad1faba7193ca7f0acccbc13b1ff6ebf9072'} |
PyPI | PYSEC-2021-843 | null | TensorFlow is an open source platform for machine learning. In affected versions several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues. These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41206', 'GHSA-pgcq-h79j-2f69'} | 2021-12-13T06:21:24.834833Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/e7f497570abb6b4ae5af4970620cd880e4c0c904', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pgcq-h79j-2f69', 'https://github.com/tensorflow/tensorflow/commit/4d74d8a00b07441cba090a02e0dd9ed385145bf4', 'https://github.com/tensorflow/tensorflow/commit/68422b215e618df5ad375bcdc6d2052e9fd3080a', 'https://github.com/tensorflow/tensorflow/commit/da4aad5946be30e5f049920fa076e1f7ef021261', 'https://github.com/tensorflow/tensorflow/commit/579261dcd446385831fe4f7457d802a59685121d', 'https://github.com/tensorflow/tensorflow/commit/4dddb2fd0b01cdd196101afbba6518658a2c9e07'} | null | {'https://github.com/tensorflow/tensorflow/commit/68422b215e618df5ad375bcdc6d2052e9fd3080a', 'https://github.com/tensorflow/tensorflow/commit/e7f497570abb6b4ae5af4970620cd880e4c0c904', 'https://github.com/tensorflow/tensorflow/commit/4dddb2fd0b01cdd196101afbba6518658a2c9e07', 'https://github.com/tensorflow/tensorflow/commit/da4aad5946be30e5f049920fa076e1f7ef021261', 'https://github.com/tensorflow/tensorflow/commit/4d74d8a00b07441cba090a02e0dd9ed385145bf4', 'https://github.com/tensorflow/tensorflow/commit/579261dcd446385831fe4f7457d802a59685121d'} | {'https://github.com/tensorflow/tensorflow/commit/4d74d8a00b07441cba090a02e0dd9ed385145bf4', 'https://github.com/tensorflow/tensorflow/commit/4dddb2fd0b01cdd196101afbba6518658a2c9e07', 'https://github.com/tensorflow/tensorflow/commit/68422b215e618df5ad375bcdc6d2052e9fd3080a', 'https://github.com/tensorflow/tensorflow/commit/579261dcd446385831fe4f7457d802a59685121d', 'https://github.com/tensorflow/tensorflow/commit/e7f497570abb6b4ae5af4970620cd880e4c0c904', 'https://github.com/tensorflow/tensorflow/commit/da4aad5946be30e5f049920fa076e1f7ef021261'} |
PyPI | PYSEC-2020-140 | null | In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. 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-2020-26267', 'GHSA-c9f3-9wfr-wgh7'} | 2020-12-14T19:08:00Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c9f3-9wfr-wgh7', 'https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae'} | null | {'https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae'} | {'https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae'} |
PyPI | PYSEC-2021-510 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.Dequantize`, an attacker can trigger a read from outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/26003593aa94b1742f34dc22ce88a1e17776a67d/tensorflow/core/kernels/dequantize_op.cc#L106-L131) accesses the `min_range` and `max_range` tensors in parallel but fails to check that they have the same shape. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-c45w-2wxr-pp53', 'CVE-2021-29582'} | 2021-12-09T06:34:56.077512Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c45w-2wxr-pp53'} | null | {'https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1b'} | {'https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1b'} |
PyPI | PYSEC-2020-269 | null | TensorFlow before 1.7.0 has an integer overflow that causes an out-of-bounds read, possibly causing disclosure of the contents of process memory. This occurs in the DecodeBmp feature of the BMP decoder in core/kernels/decode_bmp_op.cc. | {'CVE-2018-21233'} | 2021-08-27T03:22:22.195752Z | 2020-05-04T15:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2018-001.md', 'https://github.com/tensorflow/tensorflow/commit/49f73c55d56edffebde4bca4a407ad69c1cae433'} | null | {'https://github.com/tensorflow/tensorflow/commit/49f73c55d56edffebde4bca4a407ad69c1cae433'} | {'https://github.com/tensorflow/tensorflow/commit/49f73c55d56edffebde4bca4a407ad69c1cae433'} |
PyPI | GHSA-772j-h9xw-ffp5 | CHECK-fail in SparseCross due to type confusion | ### Impact
The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service:
```python
import tensorflow as tf
hashed_output = False
num_buckets = 1949315406
hash_key = 1869835877
out_type = tf.string
internal_type = tf.string
indices_1 = tf.constant([0, 6], shape=[1, 2], dtype=tf.int64)
indices_2 = tf.constant([0, 0], shape=[1, 2], dtype=tf.int64)
indices = [indices_1, indices_2]
values_1 = tf.constant([0], dtype=tf.int64)
values_2 = tf.constant([72], dtype=tf.int64)
values = [values_1, values_2]
batch_size = 4
shape_1 = tf.constant([4, 122], dtype=tf.int64)
shape_2 = tf.constant([4, 188], dtype=tf.int64)
shapes = [shape_1, shape_2]
dense_1 = tf.constant([188, 127, 336, 0], shape=[4, 1], dtype=tf.int64)
dense_2 = tf.constant([341, 470, 470, 470], shape=[4, 1], dtype=tf.int64)
dense_3 = tf.constant([188, 188, 341, 922], shape=[4, 1], dtype=tf.int64)
denses = [dense_1, dense_2, dense_3]
tf.raw_ops.SparseCross(indices=indices, values=values, shapes=shapes, dense_inputs=denses, hashed_output=hashed_output,
num_buckets=num_buckets, hash_key=hash_key, out_type=out_type, internal_type=internal_type)
```
The above code will result in a `CHECK` fail in [`tensor.cc`](https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/framework/tensor.cc#L670-L675):
```cc
void Tensor::CheckTypeAndIsAligned(DataType expected_dtype) const {
CHECK_EQ(dtype(), expected_dtype)
<< " " << DataTypeString(expected_dtype) << " expected, got "
<< DataTypeString(dtype());
...
}
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements:
```cc
if (DT_STRING == values_.dtype())
return Fingerprint64(values_.vec<tstring>().data()[start + n]);
return values_.vec<int64>().data()[start + n];
```
Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue.
### Patches
We have patched the issue in GitHub commit [b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025](https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025).
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-2021-29519'} | 2022-03-03T05:13:05.642465Z | 2021-05-21T14:21:08Z | LOW | null | {'CWE-843'} | {'https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29519', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772j-h9xw-ffp5'} | null | {'https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025'} | {'https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025'} |
PyPI | GHSA-jq4v-f5q6-mjqq | Cross-Site Scripting in lxml | An XSS vulnerability was discovered in the python `lxml` clean module versions before 4.6.3. When disabling the safe_attrs_only and forms arguments, the Cleaner class does not remove the formaction attribute allowing for JS to bypass the sanitizer. A remote attacker could exploit this flaw to run arbitrary JS code on users who interact with incorrectly sanitized HTML. This issue is patched in `lxml` 4.6.3. | {'CVE-2021-28957'} | 2022-03-03T05:13:36.582021Z | 2021-03-22T16:53:53Z | MODERATE | null | {'CWE-79'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-28957', 'https://lists.debian.org/debian-lts-announce/2021/03/msg00031.html', 'https://github.com/lxml/lxml', 'https://bugs.launchpad.net/lxml/+bug/1888153', 'https://github.com/lxml/lxml/pull/316', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/3C2R44VDUY7FJVMAVRZ2WY7XYL4SVN45/', 'https://github.com/lxml/lxml/commit/a5f9cb52079dc57477c460dbe6ba0f775e14a999', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/XXN3QPWCTQVOGW4BMWV3AUUZZ4NRZNSQ/', 'https://security.netapp.com/advisory/ntap-20210521-0004/', 'https://www.oracle.com/security-alerts/cpuoct2021.html', 'https://github.com/lxml/lxml/pull/316/commits/10ec1b4e9f93713513a3264ed6158af22492f270', 'https://www.debian.org/security/2021/dsa-4880', 'https://github.com/lxml/lxml/commit/2d01a1ba8984e0483ce6619b972832377f208a0d', 'https://pypi.org/project/lxml'} | null | {'https://github.com/lxml/lxml/commit/a5f9cb52079dc57477c460dbe6ba0f775e14a999', 'https://github.com/lxml/lxml/pull/316/commits/10ec1b4e9f93713513a3264ed6158af22492f270', 'https://github.com/lxml/lxml/commit/2d01a1ba8984e0483ce6619b972832377f208a0d'} | {'https://github.com/lxml/lxml/commit/2d01a1ba8984e0483ce6619b972832377f208a0d', 'https://github.com/lxml/lxml/pull/316/commits/10ec1b4e9f93713513a3264ed6158af22492f270', 'https://github.com/lxml/lxml/commit/a5f9cb52079dc57477c460dbe6ba0f775e14a999'} |
PyPI | PYSEC-2021-269 | null | TensorFlow is an end-to-end open source platform for machine learning. When a user does not supply arguments that determine a valid sparse tensor, `tf.raw_ops.SparseTensorSliceDataset` implementation can be made to dereference a null pointer. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L240-L251) has some argument validation but fails to consider the case when either `indices` or `values` are provided for an empty sparse tensor when the other is not. If `indices` is empty, then [code that performs validation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/data/sparse_tensor_slice_dataset_op.cc#L260-L261) (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference. If `indices` as provided by the user is empty, then `indices` in the C++ code above is backed by an empty `std::vector`, hence calling `indices->dim_size(0)` results in null pointer dereferencing (same as calling `std::vector::at()` on an empty vector). We have patched the issue in GitHub commit 02cc160e29d20631de3859c6653184e3f876b9d7. 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-c5x2-p679-95wc', 'CVE-2021-37647'} | 2021-08-27T03:22:43.708163Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c5x2-p679-95wc', 'https://github.com/tensorflow/tensorflow/commit/02cc160e29d20631de3859c6653184e3f876b9d7'} | null | {'https://github.com/tensorflow/tensorflow/commit/02cc160e29d20631de3859c6653184e3f876b9d7'} | {'https://github.com/tensorflow/tensorflow/commit/02cc160e29d20631de3859c6653184e3f876b9d7'} |
PyPI | PYSEC-2021-793 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. [For example](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/depthwise_conv.cc#L198-L200). The issue stems from the fact that `quantization.params` is only valid if `quantization.type` is different that `kTfLiteNoQuantization`. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37682', 'GHSA-4c4g-crqm-xrxw'} | 2021-12-09T06:35:39.522019Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5', 'https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887', 'https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4c4g-crqm-xrxw'} | null | {'https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538', 'https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5', 'https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887'} | {'https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5', 'https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887', 'https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538'} |
PyPI | PYSEC-2021-639 | null | TensorFlow is an end-to-end open source platform for machine learning. Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array(https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion. 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-452g-f7fp-9jf7', 'CVE-2021-29513'} | 2021-12-09T06:35:17.206359Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/030af767d357d1b4088c4a25c72cb3906abac489', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-452g-f7fp-9jf7'} | null | {'https://github.com/tensorflow/tensorflow/commit/030af767d357d1b4088c4a25c72cb3906abac489'} | {'https://github.com/tensorflow/tensorflow/commit/030af767d357d1b4088c4a25c72cb3906abac489'} |
PyPI | PYSEC-2021-76 | null | aiohttp is an asynchronous HTTP client/server framework for asyncio and Python. In aiohttp before version 3.7.4 there is an open redirect vulnerability. A maliciously crafted link to an aiohttp-based web-server could redirect the browser to a different website. It is caused by a bug in the `aiohttp.web_middlewares.normalize_path_middleware` middleware. This security problem has been fixed in 3.7.4. Upgrade your dependency using pip as follows "pip install aiohttp >= 3.7.4". If upgrading is not an option for you, a workaround can be to avoid using `aiohttp.web_middlewares.normalize_path_middleware` in your applications. | {'GHSA-v6wp-4m6f-gcjg', 'CVE-2021-21330'} | 2021-03-26T20:01:00Z | 2021-02-26T03:15:00Z | null | null | null | {'https://pypi.org/project/aiohttp/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/FU7ENI54JNEK3PHEFGCE46DGMFNTVU6L/', 'https://www.debian.org/security/2021/dsa-4864', 'https://github.com/aio-libs/aiohttp/security/advisories/GHSA-v6wp-4m6f-gcjg', 'https://github.com/aio-libs/aiohttp/blob/master/CHANGES.rst#374-2021-02-25', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/JN3V7CZJRT4QFCVXB6LDPCJH7NAOFCA5/', 'https://github.com/aio-libs/aiohttp/commit/2545222a3853e31ace15d87ae0e2effb7da0c96b'} | null | {'https://github.com/aio-libs/aiohttp/commit/2545222a3853e31ace15d87ae0e2effb7da0c96b'} | {'https://github.com/aio-libs/aiohttp/commit/2545222a3853e31ace15d87ae0e2effb7da0c96b'} |
PyPI | PYSEC-2021-286 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378. 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-2021-37664', 'GHSA-r4c4-5fpq-56wg'} | 2021-08-27T03:22:45.297527Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r4c4-5fpq-56wg', 'https://github.com/tensorflow/tensorflow/commit/e84c975313e8e8e38bb2ea118196369c45c51378'} | null | {'https://github.com/tensorflow/tensorflow/commit/e84c975313e8e8e38bb2ea118196369c45c51378'} | {'https://github.com/tensorflow/tensorflow/commit/e84c975313e8e8e38bb2ea118196369c45c51378'} |
PyPI | PYSEC-2020-286 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15206', 'GHSA-w5gh-2wr2-pm6g'} | 2021-12-09T06:34:42.621580Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w5gh-2wr2-pm6g', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6'} | null | {'https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6'} | {'https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6'} |
PyPI | GHSA-vcjj-9vg7-vf68 | Null pointer dereference in TFLite | ### Impact
An attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service:
```python
import tensorflow as tf
model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(1, 2, 3)))
model.add(tf.keras.layers.Dense(0, activation='relu'))
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
interpreter.invoke()
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer.
```cc
if (y4 > 1) {
// ...
} else {
for (int i0 = 0; i0 < y0; ++i0) {
const T* input2_data_ptr = nullptr;
for (int i1 = 0; i1 < y1; ++i1) {
input2_data_ptr = input2_data_reset;
for (int i2 = 0; i2 < y2; ++i2) {
scalar_broadcast_f(y3, params, *input1_data_ptr, input2_data_ptr,
output_data_ptr);
}
}
}
}
```
### Patches
We have patched the issue in GitHub commit [15691e456c7dc9bd6be203b09765b063bf4a380c](https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang of Baidu Security. | {'CVE-2021-37688'} | 2021-08-24T17:57:25Z | 2021-08-25T14:39:54Z | HIGH | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37688', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vcjj-9vg7-vf68', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c'} | null | {'https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c'} | {'https://github.com/tensorflow/tensorflow/commit/15691e456c7dc9bd6be203b09765b063bf4a380c'} |
PyPI | PYSEC-2021-402 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for `SparseCountSparseOutput` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-m342-ff57-4jcc', 'CVE-2021-41210'} | 2021-11-13T06:52:43.758467Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m342-ff57-4jcc'} | null | {'https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2'} | {'https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2'} |
PyPI | PYSEC-2021-117 | null | This affects the package bikeshed before 3.0.0. This can occur when an untrusted source file containing include, include-code or include-raw block is processed. The contents of arbitrary files could be disclosed in the HTML output. | {'GHSA-hf6p-4rv2-9qrp', 'SNYK-PYTHON-BIKESHED-1537647', 'CVE-2021-23423'} | 2021-08-16T10:33:00.179750Z | 2021-08-16T08:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-hf6p-4rv2-9qrp', 'https://snyk.io/vuln/SNYK-PYTHON-BIKESHED-1537647', 'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd'} | null | {'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd'} | {'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd'} |
PyPI | PYSEC-2021-547 | 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'} | 2021-12-09T06:35:01.886365Z | 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-2020-117 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `SparseFillEmptyRowsGrad` implementation has incomplete validation of the shapes of its arguments. Although `reverse_index_map_t` and `grad_values_t` are accessed in a similar pattern, only `reverse_index_map_t` is validated to be of proper shape. Hence, malicious users can pass a bad `grad_values_t` to trigger an assertion failure in `vec`, causing denial of service in serving installations. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1." | {'CVE-2020-15194', 'GHSA-9mqp-7v2h-2382'} | 2020-12-23T18:33:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9mqp-7v2h-2382', 'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54'} | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54'} |
PyPI | PYSEC-2021-814 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `ParallelConcat` misses some input validation and can produce a division by 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-7v94-64hj-m82h', 'CVE-2021-41207'} | 2021-12-09T06:35:42.190672Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7v94-64hj-m82h', 'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} | null | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} |
PyPI | PYSEC-2021-394 | null | TensorFlow is an open source platform for machine learning. In affeced versions during execution, `EinsumHelper::ParseEquation()` is supposed to set the flags in `input_has_ellipsis` vector and `*output_has_ellipsis` boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to `true` and never assigns `false`. This results in unitialized variable access if callers assume that `EinsumHelper::ParseEquation()` always sets these flags. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41201', 'GHSA-j86v-p27c-73fm'} | 2021-11-13T06:52:42.499515Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j86v-p27c-73fm'} | null | {'https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6'} | {'https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6'} |
PyPI | PYSEC-2021-21 | null | Sydent is a reference Matrix identity server. 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 memory 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. This issue has been patched by releases 89071a1, 0523511, f56eee3. As a workaround request sizes can be limited in an HTTP reverse-proxy. There are no known workarounds for the problem with overlarge responses. | {'CVE-2021-29430', 'GHSA-wmg4-8cp2-hpg9'} | 2021-04-22T17:19:00Z | 2021-04-15T21:15:00Z | null | null | null | {'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://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 | GHSA-qfpc-5pjr-mh26 | Missing validation in shape inference for `Dequantize` | ### Impact
The shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments:
```python
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
tf.raw_ops.Dequantize(
input_tensor = tf.constant(-10.0, dtype=tf.float32),
input_tensor = tf.cast(input_tensor, dtype=tf.quint8),
min_range = tf.constant([], shape=[0], dtype=tf.float32),
max_range = tf.constant([], shape=[0], dtype=tf.float32),
mode = 'MIN_COMBINED',
narrow_range=False,
axis=-10,
dtype=tf.dtypes.float32)
```
The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values.
### Patches
We have patched the issue in GitHub commit [da857cfa0fde8f79ad0afdbc94e88b5d4bbec764](https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang of Baidu Security. | {'CVE-2021-37677'} | 2022-03-03T05:13:56.617475Z | 2021-08-25T14:41:23Z | MODERATE | null | {'CWE-20'} | {'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37677', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qfpc-5pjr-mh26', 'https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764'} | null | {'https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764'} | {'https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764'} |
PyPI | GHSA-844w-j86r-4x2j | Heap buffer overflow in `UnsortedSegmentSum` in TensorFlow | ### Impact
A heap buffer overflow in `UnsortedSegmentSum` can be produced when the `Index` template argument is `int32`. In this case `data_size` and `num_segments` fields are truncated from `int64` to `int32` and can produce negative numbers, resulting in accessing out of bounds heap memory.
This is unlikely to be exploitable and was detected and fixed internally. We are making the security advisory only to notify users that it is better to update to TensorFlow 1.15 or 2.0 or later as these versions already have this fixed.
### Patches
Patched by db4f9717c41bccc3ce10099ab61996b246099892 and released in all official releases after 1.15 and 2.0.
### For more information
Please consult [`SECURITY.md`](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-2019-16778'} | 2022-03-03T05:14:03.970552Z | 2019-12-16T20:17:10Z | LOW | null | {'CWE-122', 'CWE-681'} | {'https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892', 'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2019-002.md', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2019-16778'} | null | {'https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892'} | {'https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892'} |
PyPI | GHSA-9c8h-vvrj-w2p8 | Heap OOB in `RaggedGather` | ### Impact
If the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers.
```python
import tensorflow as tf
tf.raw_ops.RaggedGather(
params_nested_splits = [0,0,0],
params_dense_values = [1,1],
indices = [0,0,9,0,0],
OUTPUT_RAGGED_RANK=0)
```
In debug mode, the same code triggers a `CHECK` failure.
The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors.
### Patches
We have patched the issue in GitHub commit [a2b743f6017d7b97af1fe49087ae15f0ac634373](https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### 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-2021-37641'} | 2022-03-03T05:13:53.218448Z | 2021-08-25T14:43:59Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c8h-vvrj-w2p8', 'https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37641'} | null | {'https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373'} | {'https://github.com/tensorflow/tensorflow/commit/a2b743f6017d7b97af1fe49087ae15f0ac634373'} |
PyPI | PYSEC-2021-681 | 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.FusedBatchNorm`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/828f346274841fa7505f7020e88ca36c22e557ab/tensorflow/core/kernels/fused_batch_norm_op.cc#L295-L297) performs a division based on the last dimension of the `x` tensor. Since this is controlled by the user, an attacker can trigger a denial of service. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29555', 'GHSA-r35g-4525-29fq'} | 2021-12-09T06:35:24.280047Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r35g-4525-29fq'} | null | {'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d'} | {'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d'} |
PyPI | PYSEC-2021-496 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-4p4p-www8-8fv9', 'CVE-2021-29568'} | 2021-12-09T06:34:53.905703Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4p4p-www8-8fv9'} | null | {'https://github.com/tensorflow/tensorflow/commit/5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8'} | {'https://github.com/tensorflow/tensorflow/commit/5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8'} |
PyPI | PYSEC-2020-98 | null | Red Discord Bot Dashboard is an easy-to-use interactive web dashboard to control your Redbot. In Red Discord Bot before version 0.1.7a an RCE exploit has been discovered. This exploit allows Discord users with specially crafted Server names and Usernames/Nicknames to inject code into the webserver front-end code. By abusing this exploit, it's possible to perform destructive actions and/or access sensitive information. This high severity exploit has been fixed on version 0.1.7a. There are no workarounds, bot owners must upgrade their relevant packages (Dashboard module and Dashboard webserver) in order to patch this issue. | {'CVE-2020-26249', 'GHSA-hm45-mgqm-gjm4'} | 2020-12-10T20:15:00Z | 2020-12-09T00:15:00Z | null | null | null | {'https://github.com/Cog-Creators/Red-Dashboard/security/advisories/GHSA-hm45-mgqm-gjm4', 'https://pypi.org/project/Red-Dashboard', 'https://github.com/Cog-Creators/Red-Dashboard/commit/99d88b840674674166ce005b784ae8e31e955ab1', 'https://github.com/Cog-Creators/Red-Dashboard/commit/a6b9785338003ec87fb75305e7d1cc2d40c7ab91'} | null | {'https://github.com/Cog-Creators/Red-Dashboard/commit/a6b9785338003ec87fb75305e7d1cc2d40c7ab91', 'https://github.com/Cog-Creators/Red-Dashboard/commit/99d88b840674674166ce005b784ae8e31e955ab1'} | {'https://github.com/Cog-Creators/Red-Dashboard/commit/99d88b840674674166ce005b784ae8e31e955ab1', 'https://github.com/Cog-Creators/Red-Dashboard/commit/a6b9785338003ec87fb75305e7d1cc2d40c7ab91'} |
PyPI | PYSEC-2020-77 | null | In libImaging/PcxDecode.c in Pillow before 7.1.0, an out-of-bounds read can occur when reading PCX files where state->shuffle is instructed to read beyond state->buffer. | null | 2020-07-27T19:15:00Z | 2020-06-25T19:15:00Z | null | null | null | {'https://github.com/python-pillow/Pillow/commits/master/src/libImaging', 'https://github.com/python-pillow/Pillow#diff-9478f2787e3ae9668a15123b165c23ac/commit/6a83e4324738bb0452fbe8074a995b1c73f08de7', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/HOKHNWV2VS5GESY7IBD237E7C6T3I427/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/BEBCPE4F2VHTIT6EZA2YZQZLPVDEBJGD/', 'https://github.com/python-pillow/Pillow/commit/6a83e4324738bb0452fbe8074a995b1c73f08de7#diff-9478f2787e3ae9668a15123b165c23ac', 'https://usn.ubuntu.com/4430-2/', 'https://pillow.readthedocs.io/en/stable/releasenotes/7.1.0.html', 'https://usn.ubuntu.com/4430-1/', 'https://github.com/python-pillow/Pillow/pull/4538'} | null | {'https://github.com/python-pillow/Pillow#diff-9478f2787e3ae9668a15123b165c23ac/commit/6a83e4324738bb0452fbe8074a995b1c73f08de7', 'https://github.com/python-pillow/Pillow/commit/6a83e4324738bb0452fbe8074a995b1c73f08de7#diff-9478f2787e3ae9668a15123b165c23ac'} | {'https://github.com/python-pillow/Pillow/commit/6a83e4324738bb0452fbe8074a995b1c73f08de7#diff-9478f2787e3ae9668a15123b165c23ac', 'https://github.com/python-pillow/Pillow#diff-9478f2787e3ae9668a15123b165c23ac/commit/6a83e4324738bb0452fbe8074a995b1c73f08de7'} |
PyPI | GHSA-p5xh-vx83-mxcj | HTTP Request Smuggling in Twisted | In Twisted Web through 20.3.0, there was an HTTP request splitting vulnerability. When presented with a content-length and a chunked encoding header, the content-length took precedence and the remainder of the request body was interpreted as a pipelined request. | {'CVE-2020-10109'} | 2022-04-04T21:16:55.158716Z | 2020-03-31T15:40:12Z | CRITICAL | null | {'CWE-444'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/YW3NIL7VXSGJND2Q4BSXM3CFTAFU6T7D/', 'https://usn.ubuntu.com/4308-1/', 'https://know.bishopfox.com/advisories/twisted-version-19.10.0', 'https://security.gentoo.org/glsa/202007-24', 'https://lists.debian.org/debian-lts-announce/2022/02/msg00021.html', 'https://github.com/twisted/twisted/commit/4a7d22e490bb8ff836892cc99a1f54b85ccb0281', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/6ISMZFZBWW4EV6ETJGXAYIXN3AT7GBPL/', 'https://github.com/twisted/twisted/blob/6ff2c40e42416c83203422ff70dfc49d2681c8e2/NEWS.rst#twisted-2030-2020-03-13', 'https://nvd.nist.gov/vuln/detail/CVE-2020-10109', 'https://github.com/twisted/twisted', 'https://usn.ubuntu.com/4308-2/'} | null | {'https://github.com/twisted/twisted/commit/4a7d22e490bb8ff836892cc99a1f54b85ccb0281'} | {'https://github.com/twisted/twisted/commit/4a7d22e490bb8ff836892cc99a1f54b85ccb0281'} |
PyPI | PYSEC-2022-165 | null | The package guake before 3.8.5 are vulnerable to Exposed Dangerous Method or Function due to the exposure of execute_command and execute_command_by_uuid methods via the d-bus interface, which makes it possible for a malicious user to run an arbitrary command via the d-bus method. **Note:** Exploitation requires the user to have installed another malicious program that will be able to send dbus signals or run terminal commands. | {'CVE-2021-23556', 'GHSA-7x48-7466-3g33', 'SNYK-PYTHON-GUAKE-2386334'} | 2022-03-17T16:54:03.713303Z | 2022-03-17T12:15:00Z | null | null | null | {'https://github.com/Guake/guake/pull/2017/commits/e3d671120bfe7ba28f50e256cc5e8a629781b888', 'https://github.com/Guake/guake/pull/2017', 'https://github.com/Guake/guake/issues/1796', 'https://github.com/Guake/guake/releases', 'https://snyk.io/vuln/SNYK-PYTHON-GUAKE-2386334', 'https://github.com/advisories/GHSA-7x48-7466-3g33'} | null | {'https://github.com/Guake/guake/pull/2017/commits/e3d671120bfe7ba28f50e256cc5e8a629781b888'} | {'https://github.com/Guake/guake/pull/2017/commits/e3d671120bfe7ba28f50e256cc5e8a629781b888'} |
PyPI | PYSEC-2021-479 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixTriangularSolve`(https://github.com/tensorflow/tensorflow/blob/8cae746d8449c7dda5298327353d68613f16e798/tensorflow/core/kernels/linalg/matrix_triangular_solve_op_impl.h#L160-L240) fails to terminate kernel execution if one validation condition fails. 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-2021-29551', 'GHSA-vqw6-72r7-fgw7'} | 2021-12-09T06:34:51.250544Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/480641e3599775a8895254ffbc0fc45621334f68', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vqw6-72r7-fgw7'} | null | {'https://github.com/tensorflow/tensorflow/commit/480641e3599775a8895254ffbc0fc45621334f68'} | {'https://github.com/tensorflow/tensorflow/commit/480641e3599775a8895254ffbc0fc45621334f68'} |
PyPI | GHSA-7x48-7466-3g33 | Command injection in guake | Guake is a drop-down terminal for GNOME. The package guake before 3.8.5 is vulnerable to Exposed Dangerous Method or Function due to the exposure of execute_command and execute_command_by_uuid methods via the d-bus interface, which makes it possible for a malicious user to run an arbitrary command via the d-bus method. **Note:** Exploitation requires the user to have installed another malicious program that will be able to send dbus signals or run terminal commands. | {'CVE-2021-23556'} | 2022-04-05T19:00:31.638645Z | 2022-03-18T00:01:11Z | MODERATE | null | null | {'https://github.com/Guake/guake/pull/2017/commits/e3d671120bfe7ba28f50e256cc5e8a629781b888', 'https://github.com/Guake/guake/pull/2017', 'https://github.com/Guake/guake', 'https://github.com/Guake/guake/issues/1796', 'https://github.com/Guake/guake/releases', 'https://github.com/pypa/advisory-database/tree/main/vulns/guake/PYSEC-2022-165.yaml', 'https://snyk.io/vuln/SNYK-PYTHON-GUAKE-2386334', 'https://nvd.nist.gov/vuln/detail/CVE-2021-23556'} | null | {'https://github.com/Guake/guake/pull/2017/commits/e3d671120bfe7ba28f50e256cc5e8a629781b888'} | {'https://github.com/Guake/guake/pull/2017/commits/e3d671120bfe7ba28f50e256cc5e8a629781b888'} |
PyPI | PYSEC-2021-183 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger an integer division by zero undefined behavior in `tf.raw_ops.QuantizedBiasAdd`. This is because the implementation of the Eigen kernel(https://github.com/tensorflow/tensorflow/blob/61bca8bd5ba8a68b2d97435ddfafcdf2b85672cd/tensorflow/core/kernels/quantization_utils.h#L812-L849) does a division by the number of elements of the smaller input (based on shape) without checking that this is not 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-2021-29546', 'GHSA-m34j-p8rj-wjxq'} | 2021-08-27T03:22:29.613359Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m34j-p8rj-wjxq', 'https://github.com/tensorflow/tensorflow/commit/67784700869470d65d5f2ef20aeb5e97c31673cb'} | null | {'https://github.com/tensorflow/tensorflow/commit/67784700869470d65d5f2ef20aeb5e97c31673cb'} | {'https://github.com/tensorflow/tensorflow/commit/67784700869470d65d5f2ef20aeb5e97c31673cb'} |
PyPI | PYSEC-2020-300 | null | In TensorFlow release candidate versions 2.4.0rc*, the general implementation for matching filesystem paths to globbing pattern is vulnerable to an access out of bounds of the array holding the directories. There are multiple invariants and preconditions that are assumed by the parallel implementation of GetMatchingPaths but are not verified by the PRs introducing it (#40861 and #44310). Thus, we are completely rewriting the implementation to fully specify and validate these. This is patched in version 2.4.0. This issue only impacts master branch and the release candidates for TF version 2.4. The final release of the 2.4 release will be patched. | {'GHSA-9jjw-hf72-3mxw', 'CVE-2020-26269'} | 2020-12-14T17:42:00Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8b5b9dc96666a3a5d27fad7179ff215e3b74b67c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9jjw-hf72-3mxw'} | null | {'https://github.com/tensorflow/tensorflow/commit/8b5b9dc96666a3a5d27fad7179ff215e3b74b67c'} | {'https://github.com/tensorflow/tensorflow/commit/8b5b9dc96666a3a5d27fad7179ff215e3b74b67c'} |
PyPI | PYSEC-2021-750 | null | TensorFlow is an end-to-end open source platform for machine learning. When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read. We have patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-gh6x-4whr-2qv4', 'CVE-2021-37639'} | 2021-12-09T06:35:35.665255Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh6x-4whr-2qv4', 'https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622'} | null | {'https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622'} | {'https://github.com/tensorflow/tensorflow/commit/9e82dce6e6bd1f36a57e08fa85af213e2b2f2622'} |
PyPI | GHSA-x4g7-fvjj-prg8 | Division by 0 in `QuantizedConv2D` | ### Impact
An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedConv2D`:
```python
import tensorflow as tf
input = tf.zeros([1, 1, 1, 1], dtype=tf.quint8)
filter = tf.constant([], shape=[1, 0, 1, 1], dtype=tf.quint8)
min_input = tf.constant(0.0)
max_input = tf.constant(0.0001)
min_filter = tf.constant(0.0)
max_filter = tf.constant(0.0001)
strides = [1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.QuantizedConv2D(input=input, filter=filter, min_input=min_input, max_input=max_input, min_filter=min_filter, max_filter=max_filter, strides=strides, padding=padding)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/00e9a4d67d76703fa1aee33dac582acf317e0e81/tensorflow/core/kernels/quantized_conv_ops.cc#L257-L259) does a division by a quantity that is controlled by the caller:
```cc
const int filter_value_count = filter_width * filter_height * input_depth;
const int64 patches_per_chunk = kMaxChunkSize / (filter_value_count * sizeof(T1));
```
### Patches
We have patched the issue in GitHub commit [cfa91be9863a91d5105a3b4941096044ab32036b](https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29527'} | 2022-03-03T05:13:59.524394Z | 2021-05-21T14:21:59Z | LOW | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29527', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4g7-fvjj-prg8', 'https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b'} | null | {'https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b'} | {'https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b'} |
PyPI | PYSEC-2021-300 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/python/keras/saving/model_config.py#L66-L104) uses `yaml.unsafe_load` which can perform arbitrary code execution on the input. Given that YAML format support requires a significant amount of work, we have removed it for now. We have patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37678', 'GHSA-r6jx-9g48-2r5r'} | 2021-08-27T03:22:46.598549Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/23d6383eb6c14084a8fc3bdf164043b974818012', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r6jx-9g48-2r5r'} | null | {'https://github.com/tensorflow/tensorflow/commit/23d6383eb6c14084a8fc3bdf164043b974818012'} | {'https://github.com/tensorflow/tensorflow/commit/23d6383eb6c14084a8fc3bdf164043b974818012'} |
PyPI | GHSA-887w-45rq-vxgf | Moderate severity vulnerability that affects SQLAlchemy | SQLAlchemy through 1.2.17 and 1.3.x through 1.3.0b2 allows SQL Injection via the order_by parameter. | {'CVE-2019-7164'} | 2022-03-23T22:00:07.130247Z | 2019-04-16T15:50:41Z | CRITICAL | null | {'CWE-89'} | {'http://lists.opensuse.org/opensuse-security-announce/2019-09/msg00010.html', 'https://github.com/advisories/GHSA-887w-45rq-vxgf', 'https://github.com/sqlalchemy/sqlalchemy/issues/4481', 'https://github.com/sqlalchemy/sqlalchemy/commit/30307c4616ad67c01ddae2e1e8e34fabf6028414', 'https://nvd.nist.gov/vuln/detail/CVE-2019-7164', 'https://github.com/sqlalchemy/sqlalchemy', 'https://lists.debian.org/debian-lts-announce/2019/03/msg00020.html', 'https://access.redhat.com/errata/RHSA-2019:0984', 'https://lists.debian.org/debian-lts-announce/2021/11/msg00005.html', 'https://access.redhat.com/errata/RHSA-2019:0981', 'http://lists.opensuse.org/opensuse-security-announce/2019-09/msg00016.html', 'http://lists.opensuse.org/opensuse-security-announce/2019-08/msg00087.html', 'https://www.oracle.com/security-alerts/cpujan2021.html'} | null | {'https://github.com/sqlalchemy/sqlalchemy/commit/30307c4616ad67c01ddae2e1e8e34fabf6028414'} | {'https://github.com/sqlalchemy/sqlalchemy/commit/30307c4616ad67c01ddae2e1e8e34fabf6028414'} |
PyPI | PYSEC-2021-615 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for the `QuantizeAndDequantizeV*` operations can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41205', 'GHSA-49rx-x2rw-pc6f'} | 2021-12-09T06:35:08.369063Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f'} | null | {'https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d'} | {'https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d'} |
PyPI | GHSA-9c78-vcq7-7vxq | Out of bounds write in TFLite | ### Impact
An attacker can craft a TFLite model that would cause a write outside of bounds of an array in TFLite. In fact, the attacker can override the linked list used by the memory allocator. This can be leveraged for an arbitrary write primitive under certain conditions.
### Patches
We have patched the issue in GitHub commit [6c0b2b70eeee588591680f5b7d5d38175fd7cdf6](https://github.com/tensorflow/tensorflow/commit/6c0b2b70eeee588591680f5b7d5d38175fd7cdf6).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Wang Xuan of Qihoo 360 AIVul Team. | {'CVE-2022-23561'} | 2022-03-03T05:13:37.705682Z | 2022-02-09T23:53:47Z | HIGH | null | {'CWE-787'} | {'https://github.com/tensorflow/tensorflow/commit/6c0b2b70eeee588591680f5b7d5d38175fd7cdf6', 'https://github.com/tensorflow/tensorflow/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23561', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c78-vcq7-7vxq'} | null | {'https://github.com/tensorflow/tensorflow/commit/6c0b2b70eeee588591680f5b7d5d38175fd7cdf6'} | {'https://github.com/tensorflow/tensorflow/commit/6c0b2b70eeee588591680f5b7d5d38175fd7cdf6'} |
PyPI | PYSEC-2021-245 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-rgvq-pcvf-hx75', 'CVE-2021-29608'} | 2021-08-27T03:22:40.610515Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rgvq-pcvf-hx75', 'https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6', 'https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a'} | null | {'https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e', 'https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6', 'https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a'} | {'https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6', 'https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e', 'https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a'} |
PyPI | PYSEC-2021-838 | null | Invenio-Drafts-Resources is a submission/deposit module for Invenio, a software framework for research data management. Invenio-Drafts-Resources prior to versions 0.13.7 and 0.14.6 does not properly check permissions when a record is published. The vulnerability is exploitable in a default installation of InvenioRDM. An authenticated a user is able via REST API calls to publish draft records of other users if they know the record identifier and the draft validates (e.g. all require fields filled out). An attacker is not able to modify the data in the record, and thus e.g. *cannot* change a record from restricted to public. The problem is patched in Invenio-Drafts-Resources v0.13.7 and 0.14.6, which is part of InvenioRDM v6.0.1 and InvenioRDM v7.0 respectively. | {'CVE-2021-43781', 'GHSA-xr38-w74q-r8jv'} | 2021-12-10T06:37:24.899021Z | 2021-12-06T18:15:00Z | null | null | null | {'https://github.com/inveniosoftware/invenio-drafts-resources/security/advisories/GHSA-xr38-w74q-r8jv', 'https://github.com/inveniosoftware/invenio-drafts-resources/commit/039b0cff1ad4b952000f4d8c3a93f347108b6626'} | null | {'https://github.com/inveniosoftware/invenio-drafts-resources/commit/039b0cff1ad4b952000f4d8c3a93f347108b6626'} | {'https://github.com/inveniosoftware/invenio-drafts-resources/commit/039b0cff1ad4b952000f4d8c3a93f347108b6626'} |
PyPI | GHSA-q6j3-c4wc-63vw | CSRF tokens leaked in URL by canned query form | ### Impact
The HTML form for a read-only canned query includes the hidden CSRF token field added in #798 for writable canned queries (#698).
This means that submitting those read-only forms exposes the CSRF token in the URL - for example on https://latest.datasette.io/fixtures/neighborhood_search submitting the form took me to:
https://latest.datasette.io/fixtures/neighborhood_search?text=down&csrftoken=CSRFTOKEN-HERE
This token could potentially leak to an attacker if the resulting page has a link to an external site on it and the user clicks the link, since the token would be exposed in the referral logs.
### Patches
A fix for this issue has been released in Datasette 0.46.
### Workarounds
You can fix this issue in a Datasette instance without upgrading by copying the [0.46 query.html template](https://raw.githubusercontent.com/simonw/datasette/0.46/datasette/templates/query.html) into a custom `templates/` directory and running Datasette with the `--template-dir=templates/` option.
### References
Issue 918 discusses this in details: https://github.com/simonw/datasette/issues/918
### For more information
Contact swillison at gmail with any questions. | null | 2022-03-03T05:14:06.391514Z | 2020-08-11T14:54:40Z | MODERATE | null | {'CWE-200'} | {'https://snyk.io/vuln/SNYK-PYTHON-DATASETTE-598229', 'https://github.com/simonw/datasette/commit/7f10f0f7664d474c1be82bf668829e3b736a3d2b', 'https://github.com/simonw/datasette', 'https://github.com/simonw/datasette/security/advisories/GHSA-q6j3-c4wc-63vw', 'https://github.com/simonw/datasette/issues/918'} | null | {'https://github.com/simonw/datasette/commit/7f10f0f7664d474c1be82bf668829e3b736a3d2b'} | {'https://github.com/simonw/datasette/commit/7f10f0f7664d474c1be82bf668829e3b736a3d2b'} |
PyPI | GHSA-f8xq-q7px-wg8c | Improper Neutralization of Formula Elements in a CSV File in Gradio Flagging | ### Impact
The `gradio` library has a flagging functionality which saves input/output data into a CSV file on the developer's computer. This can allow a user to save arbitrary text into the CSV file, such as commands. If a program like MS Excel opens such a file, then it automatically runs these commands, which could lead to arbitrary commands running on the user's computer.
### Patches
The problem has been patched as of `2.8.11`, which escapes the data saved to the csv with single quotes.
### Workarounds
If you are using an older version of `gradio`, don't open csv files generated by `gradio` with Excel or similar spreadsheet programs.
| {'CVE-2022-24770'} | 2022-03-18T23:16:59.966123Z | 2022-03-18T23:11:43Z | HIGH | null | {'CWE-1236'} | {'https://github.com/gradio-app/gradio', 'https://github.com/gradio-app/gradio/security/advisories/GHSA-f8xq-q7px-wg8c', 'https://github.com/gradio-app/gradio/pull/817', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24770', 'https://github.com/gradio-app/gradio/commit/80fea89117358ee105973453fdc402398ae20239'} | null | {'https://github.com/gradio-app/gradio/commit/80fea89117358ee105973453fdc402398ae20239'} | {'https://github.com/gradio-app/gradio/commit/80fea89117358ee105973453fdc402398ae20239'} |
PyPI | PYSEC-2021-584 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.Map*` and `tf.raw_ops.OrderedMap*` operations. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L222-L248) has a check in place to ensure that `indices` is in ascending order, but does not check that `indices` is not empty. We have patched the issue in GitHub commit 532f5c5a547126c634fefd43bbad1dc6417678ac. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37671', 'GHSA-qr82-2c78-4m8h'} | 2021-12-09T06:35:05.048687Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qr82-2c78-4m8h', 'https://github.com/tensorflow/tensorflow/commit/532f5c5a547126c634fefd43bbad1dc6417678ac'} | null | {'https://github.com/tensorflow/tensorflow/commit/532f5c5a547126c634fefd43bbad1dc6417678ac'} | {'https://github.com/tensorflow/tensorflow/commit/532f5c5a547126c634fefd43bbad1dc6417678ac'} |
PyPI | PYSEC-2022-132 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `AddManySparseToTensorsMap` is vulnerable to an integer overflow which results in a `CHECK`-fail when building new `TensorShape` objects (so, an assert failure based denial of service). We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'CVE-2022-23568', 'GHSA-6445-fm66-fvq2'} | 2022-03-09T00:18:26.728990Z | 2022-02-03T12:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_tensors_map_ops.cc', 'https://github.com/tensorflow/tensorflow/commit/b51b82fe65ebace4475e3c54eb089c18a4403f1c', 'https://github.com/tensorflow/tensorflow/commit/a68f68061e263a88321c104a6c911fe5598050a8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6445-fm66-fvq2'} | null | {'https://github.com/tensorflow/tensorflow/commit/b51b82fe65ebace4475e3c54eb089c18a4403f1c', 'https://github.com/tensorflow/tensorflow/commit/a68f68061e263a88321c104a6c911fe5598050a8'} | {'https://github.com/tensorflow/tensorflow/commit/b51b82fe65ebace4475e3c54eb089c18a4403f1c', 'https://github.com/tensorflow/tensorflow/commit/a68f68061e263a88321c104a6c911fe5598050a8'} |
PyPI | GHSA-j8fq-86c5-5v2r | Remote code execution in dask | An issue was discovered in Dask (aka python-dask) through 2021.09.1. Single machine Dask clusters started with dask.distributed.LocalCluster or dask.distributed.Client (which defaults to using LocalCluster) would mistakenly configure their respective Dask workers to listen on external interfaces (typically with a randomly selected high port) rather than only on localhost. A Dask cluster created using this method (when running on a machine that has an applicable port exposed) could be used by a sophisticated attacker to achieve remote code execution. | {'CVE-2021-42343'} | 2022-03-21T20:00:06.928891Z | 2021-10-27T18:53:48Z | CRITICAL | null | {'CWE-668'} | {'https://github.com/dask/distributed/commit/afce4be8e05fb180e50a9d9e38465f1a82295e1b', 'https://github.com/dask/distributed/security/advisories/GHSA-hwqr-f3v9-hwxr', 'https://github.com/dask/distributed', 'https://nvd.nist.gov/vuln/detail/CVE-2021-42343', 'https://github.com/dask/distributed/pull/5427', 'https://docs.dask.org/en/latest/changelog.html'} | null | {'https://github.com/dask/distributed/commit/afce4be8e05fb180e50a9d9e38465f1a82295e1b'} | {'https://github.com/dask/distributed/commit/afce4be8e05fb180e50a9d9e38465f1a82295e1b'} |
PyPI | PYSEC-2021-707 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.CTCBeamSearchDecoder`, an attacker can trigger denial of service via segmentation faults. The implementation(https://github.com/tensorflow/tensorflow/blob/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7/tensorflow/core/kernels/ctc_decoder_ops.cc#L68-L79) fails to detect cases when the input tensor is empty and proceeds to read data from a null buffer. 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-2021-29581', 'GHSA-vq2r-5xvm-3hc3'} | 2021-12-09T06:35:28.711775Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vq2r-5xvm-3hc3', 'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} | null | {'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} | {'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} |
PyPI | GHSA-434h-p4gx-jm89 | Observable Response Discrepancy in Flask-AppBuilder | ### Impact
User enumeration in database authentication in Flask-AppBuilder <= 3.2.3. Allows for a non authenticated user to enumerate existing accounts by timing the response time from the server when you are logging in.
### Patches
Upgrade to 3.3.0
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Flask-AppBuilder](https://github.com/dpgaspar/Flask-AppBuilder)
| {'CVE-2021-29621'} | 2022-03-03T05:13:01.517970Z | 2021-05-27T18:38:36Z | MODERATE | null | {'CWE-203'} | {'https://github.com/dpgaspar/Flask-AppBuilder', 'https://github.com/dpgaspar/Flask-AppBuilder/commit/780bd0e8fbf2d36ada52edb769477e0a4edae580', 'https://lists.apache.org/thread.html/r5b754118ba4e996adf03863705d34168bffec202da5c6bdc9bf3add5@%3Cannounce.apache.org%3E', 'https://lists.apache.org/thread.html/r91067f953906d93aaa1c69fe2b5472754019cc6bd4f1ba81349d62a0@%3Ccommits.airflow.apache.org%3E', 'https://lists.apache.org/thread.html/r466759f377651f0a690475d5a52564d0e786e82c08d5a5730a4f8352@%3Cannounce.apache.org%3E', 'https://github.com/dpgaspar/Flask-AppBuilder/security/advisories/GHSA-434h-p4gx-jm89', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29621', 'https://pypi.org/project/Flask-AppBuilder/'} | null | {'https://github.com/dpgaspar/Flask-AppBuilder/commit/780bd0e8fbf2d36ada52edb769477e0a4edae580'} | {'https://github.com/dpgaspar/Flask-AppBuilder/commit/780bd0e8fbf2d36ada52edb769477e0a4edae580'} |
PyPI | PYSEC-2021-212 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.ReverseSequence` allows for stack overflow and/or `CHECK`-fail based denial of service. The implementation(https://github.com/tensorflow/tensorflow/blob/5b3b071975e01f0d250c928b2a8f901cd53b90a7/tensorflow/core/kernels/reverse_sequence_op.cc#L114-L118) fails to validate that `seq_dim` and `batch_dim` arguments are valid. Negative values for `seq_dim` can result in stack overflow or `CHECK`-failure, depending on the version of Eigen code used to implement the operation. Similar behavior can be exhibited by invalid values of `batch_dim`. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-6qgm-fv6v-rfpv', 'CVE-2021-29575'} | 2021-08-27T03:22:34.716646Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6qgm-fv6v-rfpv', 'https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23'} | null | {'https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23'} | {'https://github.com/tensorflow/tensorflow/commit/ecf768cbe50cedc0a45ce1ee223146a3d3d26d23'} |
PyPI | PYSEC-2021-642 | null | TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.RaggedTensorToVariant` with arguments specifying an invalid ragged tensor results in a null pointer dereference. The implementation of `RaggedTensorToVariant` operations(https://github.com/tensorflow/tensorflow/blob/904b3926ed1c6c70380d5313d282d248a776baa1/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L39-L40) does not validate that the ragged tensor argument is non-empty. Since `batched_ragged` contains no elements, `batched_ragged.splits` is a null vector, thus `batched_ragged.splits(0)` will result in dereferencing `nullptr`. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-84mw-34w6-2q43', 'CVE-2021-29516'} | 2021-12-09T06:35:17.688674Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/b055b9c474cd376259dde8779908f9eeaf097d93', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-84mw-34w6-2q43'} | null | {'https://github.com/tensorflow/tensorflow/commit/b055b9c474cd376259dde8779908f9eeaf097d93'} | {'https://github.com/tensorflow/tensorflow/commit/b055b9c474cd376259dde8779908f9eeaf097d93'} |
PyPI | GHSA-2xwp-m7mq-7q3r | CLI does not correctly implement strict mode | In the affected versions, the AWS Encryption CLI operated in "discovery mode" even when "strict mode" was specified. Although decryption only succeeded if the user had permission to decrypt with at least one of the CMKs, decryption could be successful using a CMK that was not included in the user-defined set when the CLI was operating in "strict mode."
Affected users should upgrade to Encryption CLI v1.8.x or v2.1.x as soon as possible. | null | 2022-03-03T05:12:12.871982Z | 2020-10-28T17:05:38Z | LOW | null | {'CWE-326'} | {'https://github.com/aws/aws-encryption-sdk-cli/security/advisories/GHSA-2xwp-m7mq-7q3r', 'https://github.com/aws/aws-encryption-sdk-cli/commit/7d21b8051cab9e52e056fe427d2bff19cf146460'} | null | {'https://github.com/aws/aws-encryption-sdk-cli/commit/7d21b8051cab9e52e056fe427d2bff19cf146460'} | {'https://github.com/aws/aws-encryption-sdk-cli/commit/7d21b8051cab9e52e056fe427d2bff19cf146460'} |
PyPI | GHSA-xrqm-fpgr-6hhx | Overflow/crash in `tf.range` | ### Impact
While calculating the size of the output within the `tf.range` kernel, there is a conditional statement of type `int64 = condition ? int64 : double`. Due to C++ implicit conversion rules, both branches of the condition will be cast to `double` and the result would be truncated before the assignment. This result in overflows:
```python
import tensorflow as tf
tf.sparse.eye(num_rows=9223372036854775807, num_columns=None)
```
Similarly, `tf.range` would result in crashes due to overflows if the start or end point are too large.
```python
import tensorflow as tf
tf.range(start=-1e+38, limit=1)
```
### Patches
We have patched the issue in GitHub commits [6d94002a09711d297dbba90390d5482b76113899](https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899) (merging [#51359](https://github.com/tensorflow/tensorflow/pull/51359)) and [1b0e0ec27e7895b9985076eab32445026ae5ca94](https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94) (merging [#51711](https://github.com/tensorflow/tensorflow/pull/51711)).
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/46912), [GitHub issue](https://github.com/tensorflow/tensorflow/issues/46899) and [GitHub issue](https://github.com/tensorflow/tensorflow/issues/46889).
| {'CVE-2021-41202'} | 2022-03-03T05:14:05.060099Z | 2021-11-10T19:13:16Z | MODERATE | null | {'CWE-681'} | {'https://github.com/tensorflow/tensorflow/issues/46889', 'https://github.com/tensorflow/tensorflow/issues/46912', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xrqm-fpgr-6hhx', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41202', 'https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899', 'https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94'} | null | {'https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94', 'https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899'} | {'https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899', 'https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94'} |
PyPI | PYSEC-2022-124 | null | Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors. The fix is 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. Users are advised to upgrade as soon as possible. | {'GHSA-4hvf-hxvg-f67v', 'CVE-2022-23560'} | 2022-03-09T00:18:25.643457Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v', 'https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038', 'https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/internal/utils/sparsity_format_converter.cc#L252-L293'} | null | {'https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038'} | {'https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038'} |
PyPI | PYSEC-2021-592 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37679', 'GHSA-g8wg-cjwc-xhhp'} | 2021-12-09T06:35:05.737030Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp', 'https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12'} | null | {'https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12'} | {'https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12'} |
PyPI | PYSEC-2021-438 | null | django-helpdesk is vulnerable to Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') | {'GHSA-2v5j-q74q-r53f', 'CVE-2021-3994'} | 2021-12-02T21:26:01.187346Z | 2021-12-01T11:15:00Z | null | null | null | {'https://huntr.dev/bounties/be7f211d-4bfd-44fd-91e8-682329906fbd', 'https://github.com/advisories/GHSA-2v5j-q74q-r53f', 'https://github.com/django-helpdesk/django-helpdesk/commit/a22eb0673fe0b7784f99c6b5fd343b64a6700f06'} | null | {'https://github.com/django-helpdesk/django-helpdesk/commit/a22eb0673fe0b7784f99c6b5fd343b64a6700f06'} | {'https://github.com/django-helpdesk/django-helpdesk/commit/a22eb0673fe0b7784f99c6b5fd343b64a6700f06'} |
PyPI | GHSA-62gx-355r-9fhg | Session operations in eager mode lead to null pointer dereferences | ### Impact
In eager mode (default in TF 2.0 and later), session operations are invalid. However, users could still call the raw ops associated with them and trigger a null pointer dereference:
```python
import tensorflow as tf
tf.raw_ops.GetSessionTensor(handle=['\x12\x1a\x07'],dtype=4)
```
```python
import tensorflow as tf
tf.raw_ops.DeleteSessionTensor(handle=['\x12\x1a\x07'])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/eebb96c2830d48597d055d247c0e9aebaea94cd5/tensorflow/core/kernels/session_ops.cc#L104) dereferences the session state pointer without checking if it is valid:
```cc
OP_REQUIRES_OK(ctx, ctx->session_state()->GetTensor(name, &val));
```
Thus, in eager mode, `ctx->session_state()` is nullptr and the call of the member function is undefined behavior.
### Patches
We have patched the issue in GitHub commit [ff70c47a396ef1e3cb73c90513da4f5cb71bebba](https://github.com/tensorflow/tensorflow/commit/ff70c47a396ef1e3cb73c90513da4f5cb71bebba).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-29518'} | 2022-03-03T05:13:04.427750Z | 2021-05-21T14:21:05Z | LOW | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-62gx-355r-9fhg', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29518', 'https://github.com/tensorflow/tensorflow/commit/ff70c47a396ef1e3cb73c90513da4f5cb71bebba'} | null | {'https://github.com/tensorflow/tensorflow/commit/ff70c47a396ef1e3cb73c90513da4f5cb71bebba'} | {'https://github.com/tensorflow/tensorflow/commit/ff70c47a396ef1e3cb73c90513da4f5cb71bebba'} |
PyPI | GHSA-393f-2jr3-cp69 | CHECK-fail in DrawBoundingBoxes | ### Impact
An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`:
```python
import tensorflow as tf
images = tf.fill([53, 0, 48, 1], 0.)
boxes = tf.fill([53, 31, 4], 0.)
boxes = tf.Variable(boxes)
boxes[0, 0, 0].assign(3.90621)
tf.raw_ops.DrawBoundingBoxes(images=images, boxes=boxes)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to validate user controlled inputs. Whereas `OP_REQUIRES` allows returning an error condition back to the user, the `CHECK_*` macros result in a crash if the condition is false, similar to `assert`.
```cc
const int64 max_box_row_clamp = std::min<int64>(max_box_row, height - 1);
...
CHECK_GE(max_box_row_clamp, 0);
```
In this case, `height` is 0 from the `images` input. This results in `max_box_row_clamp` being negative and the assertion being falsified, followed by aborting program execution.
### Patches
We have patched the issue in GitHub commit [b432a38fe0e1b4b904a6c222cbce794c39703e87](https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87).
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-2021-29533'} | 2022-03-03T05:13:02.902950Z | 2021-05-21T14:22:21Z | LOW | null | {'CWE-754'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-393f-2jr3-cp69', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29533', 'https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87'} | null | {'https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87'} | {'https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87'} |
PyPI | PYSEC-2021-711 | null | TensorFlow is an end-to-end open source platform for machine learning. The TFLite computation for size of output after padding, `ComputeOutSize`(https://github.com/tensorflow/tensorflow/blob/0c9692ae7b1671c983569e5d3de5565843d500cf/tensorflow/lite/kernels/padding.h#L43-L55), does not check that the `stride` argument is not 0 before doing the division. Users can craft special models such that `ComputeOutSize` is called with `stride` set to 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-mv78-g7wq-mhp4', 'CVE-2021-29585'} | 2021-12-09T06:35:29.363788Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/49847ae69a4e1a97ae7f2db5e217c77721e37948', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mv78-g7wq-mhp4'} | null | {'https://github.com/tensorflow/tensorflow/commit/49847ae69a4e1a97ae7f2db5e217c77721e37948'} | {'https://github.com/tensorflow/tensorflow/commit/49847ae69a4e1a97ae7f2db5e217c77721e37948'} |
PyPI | PYSEC-2021-654 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedMul`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55900e961ed4a23b438392024912154a2c2f5e85/tensorflow/core/kernels/quantized_mul_op.cc#L188-L198) does a division by a quantity that is controlled by the caller. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29528', 'GHSA-6f84-42vf-ppwp'} | 2021-12-09T06:35:19.582800Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6f84-42vf-ppwp', 'https://github.com/tensorflow/tensorflow/commit/a1b11d2fdd1e51bfe18bb1ede804f60abfa92da6'} | null | {'https://github.com/tensorflow/tensorflow/commit/a1b11d2fdd1e51bfe18bb1ede804f60abfa92da6'} | {'https://github.com/tensorflow/tensorflow/commit/a1b11d2fdd1e51bfe18bb1ede804f60abfa92da6'} |
PyPI | PYSEC-2021-204 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.SparseDenseCwiseMul`, an attacker can trigger denial of service via `CHECK`-fails or accesses to outside the bounds of heap allocated data. Since the implementation(https://github.com/tensorflow/tensorflow/blob/38178a2f7a681a7835bb0912702a134bfe3b4d84/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L68-L80) only validates the rank of the input arguments but no constraints between dimensions(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseDenseCwiseMul), an attacker can abuse them to trigger internal `CHECK` assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29567', 'GHSA-wp3c-xw9g-gpcg'} | 2021-08-27T03:22:33.334705Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp3c-xw9g-gpcg', 'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc'} | null | {'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc'} | {'https://github.com/tensorflow/tensorflow/commit/7ae2af34087fb4b5c8915279efd03da3b81028bc'} |
PyPI | GHSA-4c4g-crqm-xrxw | Use of unitialized value in TFLite | ### Impact
All TFLite operations that use quantization can be made to use unitialized values. [For example](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/depthwise_conv.cc#L198-L200):
```cc
const auto* affine_quantization =
reinterpret_cast<TfLiteAffineQuantization*>(
filter->quantization.params);
```
The issue stems from the fact that `quantization.params` is only valid if `quantization.type` is different that `kTfLiteNoQuantization`. However, these checks are missing in large parts of the code.
### Patches
We have patched the issue in GitHub commits [537bc7c723439b9194a358f64d871dd326c18887](https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887),
[4a91f2069f7145aab6ba2d8cfe41be8a110c18a5](https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5) and [8933b8a21280696ab119b63263babdb54c298538](https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### 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-2021-37682'} | 2021-08-24T16:41:39Z | 2021-08-25T14:40:32Z | MODERATE | null | {'CWE-908'} | {'https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887', 'https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37682', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4c4g-crqm-xrxw'} | null | {'https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538', 'https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5', 'https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887'} | {'https://github.com/tensorflow/tensorflow/commit/4a91f2069f7145aab6ba2d8cfe41be8a110c18a5', 'https://github.com/tensorflow/tensorflow/commit/537bc7c723439b9194a358f64d871dd326c18887', 'https://github.com/tensorflow/tensorflow/commit/8933b8a21280696ab119b63263babdb54c298538'} |
PyPI | GHSA-mjcr-rqjg-rhg3 | Implementation trusts the "me" field returned by the authorization server without verifying it | ### Impact
A malicious user can sign in as a user with any IndieAuth identifier. This is because the implementation does not verify that the final `"me"` URL value returned by the authorization server belongs to the same domain as the initial value entered by the user.
### Patches
Version 1.1 fixes this issue.
### Workarounds
There is no workaround. Upgrade to 1.1 immediately.
### References
- [Security Considerations: Differing User Profile URLs](https://indieauth.spec.indieweb.org/#differing-user-profile-urls-li-1) in the IndieAuth specification.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [simonw/datasette-indieauth](https://github.com/simonw/datasette-indieauth/issues) | null | 2022-03-21T20:04:49Z | 2020-11-24T21:21:04Z | CRITICAL | null | {'CWE-290'} | {'https://github.com/simonw/datasette-indieauth', 'https://pypi.org/project/datasette-indieauth/', 'https://github.com/simonw/datasette-indieauth/security/advisories/GHSA-mjcr-rqjg-rhg3', 'https://github.com/simonw/datasette-indieauth/commit/376c8804c6b0811852049229a24336fe5eb6a439'} | null | {'https://github.com/simonw/datasette-indieauth/commit/376c8804c6b0811852049229a24336fe5eb6a439'} | {'https://github.com/simonw/datasette-indieauth/commit/376c8804c6b0811852049229a24336fe5eb6a439'} |
PyPI | PYSEC-2020-204 | null | Ansible before 1.6.7 does not prevent inventory data with "{{" and "lookup" substrings, and does not prevent remote data with "{{" substrings, which allows remote attackers to execute arbitrary code via (1) crafted lookup('pipe') calls or (2) crafted Jinja2 data. | {'CVE-2014-4966'} | 2021-07-02T02:41:33.333300Z | 2020-02-18T15:15:00Z | null | null | null | {'https://github.com/ansible/ansible/commit/62a1295a3e08cb6c3e9f1b2a1e6e5dcaeab32527', 'http://www.ocert.org/advisories/ocert-2014-004.html'} | null | {'https://github.com/ansible/ansible/commit/62a1295a3e08cb6c3e9f1b2a1e6e5dcaeab32527'} | {'https://github.com/ansible/ansible/commit/62a1295a3e08cb6c3e9f1b2a1e6e5dcaeab32527'} |
PyPI | PYSEC-2016-16 | null | The password hasher in contrib/auth/hashers.py in Django before 1.8.10 and 1.9.x before 1.9.3 allows remote attackers to enumerate users via a timing attack involving login requests. | {'CVE-2016-2513'} | 2021-07-15T02:22:10.225115Z | 2016-04-08T15:59:00Z | null | null | null | {'http://www.ubuntu.com/usn/USN-2915-2', 'http://rhn.redhat.com/errata/RHSA-2016-0504.html', 'http://www.debian.org/security/2016/dsa-3544', 'https://github.com/django/django/commit/67b46ba7016da2d259c1ecc7d666d11f5e1cfaab', 'http://www.oracle.com/technetwork/topics/security/bulletinapr2016-2952098.html', 'http://rhn.redhat.com/errata/RHSA-2016-0506.html', 'http://www.ubuntu.com/usn/USN-2915-3', 'http://www.securityfocus.com/bid/83878', 'https://www.djangoproject.com/weblog/2016/mar/01/security-releases/', 'http://rhn.redhat.com/errata/RHSA-2016-0502.html', 'http://rhn.redhat.com/errata/RHSA-2016-0505.html', 'http://www.ubuntu.com/usn/USN-2915-1', 'http://www.securitytracker.com/id/1035152'} | null | {'https://github.com/django/django/commit/67b46ba7016da2d259c1ecc7d666d11f5e1cfaab'} | {'https://github.com/django/django/commit/67b46ba7016da2d259c1ecc7d666d11f5e1cfaab'} |
PyPI | PYSEC-2021-480 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar<T>()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. 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-2021-29552', 'GHSA-jhq9-wm9m-cf89'} | 2021-12-09T06:34:51.420468Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jhq9-wm9m-cf89'} | null | {'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe'} | {'https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe'} |
PyPI | PYSEC-2021-195 | 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'} | 2021-08-27T03:22:31.758663Z | 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-2020-61 | null | In lookatme (python/pypi package) versions prior to 2.3.0, the package automatically loaded the built-in "terminal" and "file_loader" extensions. Users that use lookatme to render untrusted markdown may have malicious shell commands automatically run on their system. This is fixed in version 2.3.0. As a workaround, the `lookatme/contrib/terminal.py` and `lookatme/contrib/file_loader.py` files may be manually deleted. Additionally, it is always recommended to be aware of what is being rendered with lookatme. | {'GHSA-c84h-w6cr-5v8q', 'CVE-2020-15271'} | 2020-11-13T16:40:00Z | 2020-10-26T18:15:00Z | null | null | null | {'https://github.com/d0c-s4vage/lookatme/commit/72fe36b784b234548d49dae60b840c37f0eb8d84', 'https://pypi.org/project/lookatme/#history', 'https://github.com/d0c-s4vage/lookatme/releases/tag/v2.3.0', 'https://github.com/d0c-s4vage/lookatme/pull/110', 'https://github.com/d0c-s4vage/lookatme/security/advisories/GHSA-c84h-w6cr-5v8q'} | null | {'https://github.com/d0c-s4vage/lookatme/commit/72fe36b784b234548d49dae60b840c37f0eb8d84'} | {'https://github.com/d0c-s4vage/lookatme/commit/72fe36b784b234548d49dae60b840c37f0eb8d84'} |
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