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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | GHSA-hmr4-m2h5-33qx | SQL injection in Django | Django 1.11 before 1.11.28, 2.2 before 2.2.10, and 3.0 before 3.0.3 allows SQL Injection if untrusted data is used as a StringAgg delimiter (e.g., in Django applications that offer downloads of data as a series of rows with a user-specified column delimiter). By passing a suitably crafted delimiter to a contrib.postgres.aggregates.StringAgg instance, it was possible to break escaping and inject malicious SQL. | {'CVE-2020-7471'} | 2022-03-21T20:45:05.942353Z | 2020-02-11T21:03:20Z | CRITICAL | null | {'CWE-89'} | {'https://github.com/django/django/commit/eb31d845323618d688ad429479c6dda973056136', 'https://security.netapp.com/advisory/ntap-20200221-0006/', 'https://www.djangoproject.com/weblog/2020/feb/03/security-releases/', 'https://www.openwall.com/lists/oss-security/2020/02/03/1', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/4A2AP4T7RKPBCLTI2NNQG3T6MINDUUMZ/', 'https://nvd.nist.gov/vuln/detail/CVE-2020-7471', 'https://groups.google.com/forum/#!topic/django-announce/X45S86X5bZI', 'https://security.gentoo.org/glsa/202004-17', 'http://www.openwall.com/lists/oss-security/2020/02/03/1', 'https://github.com/django/django', 'https://usn.ubuntu.com/4264-1/', 'https://seclists.org/bugtraq/2020/Feb/30', 'https://www.debian.org/security/2020/dsa-4629'} | null | {'https://github.com/django/django/commit/eb31d845323618d688ad429479c6dda973056136'} | {'https://github.com/django/django/commit/eb31d845323618d688ad429479c6dda973056136'} |
PyPI | GHSA-6cc5-2vg4-cc7m | Injection in Twisted | In Twisted before 19.2.1, twisted.web did not validate or sanitize URIs or HTTP methods, allowing an attacker to inject invalid characters such as CRLF. | {'CVE-2019-12387'} | 2022-03-03T05:13:04.554292Z | 2019-06-10T18:05:06Z | MODERATE | null | {'CWE-74'} | {'http://lists.opensuse.org/opensuse-security-announce/2019-07/msg00030.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2G5RPDQ4BNB336HL6WW5ZJ344MAWNN7N/', 'https://usn.ubuntu.com/4308-1/', 'https://www.oracle.com/security-alerts/cpuapr2020.html', 'http://lists.opensuse.org/opensuse-security-announce/2019-07/msg00042.html', 'https://github.com/twisted/twisted/commit/6c61fc4503ae39ab8ecee52d10f10ee2c371d7e2', 'https://nvd.nist.gov/vuln/detail/CVE-2019-12387', 'https://twistedmatrix.com/pipermail/twisted-python/2019-June/032352.html', 'https://labs.twistedmatrix.com/2019/06/twisted-1921-released.html', 'https://usn.ubuntu.com/4308-2/'} | null | {'https://github.com/twisted/twisted/commit/6c61fc4503ae39ab8ecee52d10f10ee2c371d7e2'} | {'https://github.com/twisted/twisted/commit/6c61fc4503ae39ab8ecee52d10f10ee2c371d7e2'} |
PyPI | PYSEC-2021-612 | null | TensorFlow is an open source platform for machine learning. In affected versions while calculating the size of the output within the `tf.range` kernel, there is a conditional statement of type `int64 = condition ? int64 : double`. Due to C++ implicit conversion rules, both branches of the condition will be cast to `double` and the result would be truncated before the assignment. This result in overflows. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41202', 'GHSA-xrqm-fpgr-6hhx'} | 2021-12-09T06:35:07.917442Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/46889', 'https://github.com/tensorflow/tensorflow/issues/46912', 'https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xrqm-fpgr-6hhx', 'https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94'} | null | {'https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94', 'https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899'} | {'https://github.com/tensorflow/tensorflow/commit/6d94002a09711d297dbba90390d5482b76113899', 'https://github.com/tensorflow/tensorflow/commit/1b0e0ec27e7895b9985076eab32445026ae5ca94'} |
PyPI | PYSEC-2021-242 | null | TensorFlow is an end-to-end open source platform for machine learning. The TFLite code for allocating `TFLiteIntArray`s is vulnerable to an integer overflow issue(https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L24-L27). An attacker can craft a model such that the `size` multiplier is so large that the return value overflows the `int` datatype and becomes negative. In turn, this results in invalid value being given to `malloc`(https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L47-L52). In this case, `ret->size` would dereference an invalid pointer. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-jf7h-7m85-w2v2', 'CVE-2021-29605'} | 2021-08-27T03:22:40.058012Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jf7h-7m85-w2v2'} | null | {'https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5'} | {'https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5'} |
PyPI | PYSEC-2021-868 | null | An issue was discovered in split_region in uc.c in Unicorn Engine before 2.0.0-rc5. It allows local attackers to escape the sandbox. An attacker must first obtain the ability to execute crafted code in the target sandbox in order to exploit this vulnerability. The specific flaw exists within the virtual memory manager. The issue results from the faulty comparison of GVA and GPA while calling uc_mem_map_ptr to free part of a claimed memory block. An attacker can leverage this vulnerability to escape the sandbox and execute arbitrary code on the host machine. | {'CVE-2021-44078'} | 2022-01-07T19:23:22.112610Z | 2021-12-26T05:15:00Z | null | null | null | {'https://github.com/jwang-a/CTF/blob/master/MyChallenges/Pwn/Unicorns_Aisle/UnicornsAisle.pdf', 'https://github.com/unicorn-engine/unicorn/compare/2.0.0-rc4...2.0.0-rc5', 'https://github.com/unicorn-engine/unicorn/commit/c733bbada356b0373fa8aa72c044574bb855fd24', 'https://gist.github.com/jwang-a/cb4b6e9551457aa299066076b836a2cd', 'https://www.unicorn-engine.org/changelog/'} | null | {'https://github.com/unicorn-engine/unicorn/commit/c733bbada356b0373fa8aa72c044574bb855fd24'} | {'https://github.com/unicorn-engine/unicorn/commit/c733bbada356b0373fa8aa72c044574bb855fd24'} |
PyPI | PYSEC-2018-19 | null | transport.py in the SSH server implementation of Paramiko before 1.17.6, 1.18.x before 1.18.5, 2.0.x before 2.0.8, 2.1.x before 2.1.5, 2.2.x before 2.2.3, 2.3.x before 2.3.2, and 2.4.x before 2.4.1 does not properly check whether authentication is completed before processing other requests, as demonstrated by channel-open. A customized SSH client can simply skip the authentication step. | {'GHSA-232r-66cg-79px', 'CVE-2018-7750'} | 2021-06-10T06:50:48.065198Z | 2018-03-13T18:29:00Z | null | null | null | {'https://access.redhat.com/errata/RHSA-2018:1274', 'http://www.securityfocus.com/bid/103713', 'https://lists.debian.org/debian-lts-announce/2018/10/msg00018.html', 'https://usn.ubuntu.com/3603-2/', 'https://github.com/advisories/GHSA-232r-66cg-79px', 'https://access.redhat.com/errata/RHSA-2018:1972', 'https://access.redhat.com/errata/RHSA-2018:0591', 'https://access.redhat.com/errata/RHSA-2018:1328', 'https://access.redhat.com/errata/RHSA-2018:1124', 'https://access.redhat.com/errata/RHSA-2018:1125', 'https://usn.ubuntu.com/3603-1/', 'https://github.com/paramiko/paramiko/commit/fa29bd8446c8eab237f5187d28787727b4610516', 'https://access.redhat.com/errata/RHSA-2018:0646', 'https://github.com/paramiko/paramiko/issues/1175', 'https://www.exploit-db.com/exploits/45712/', 'https://github.com/paramiko/paramiko/blob/master/sites/www/changelog.rst', 'https://access.redhat.com/errata/RHSA-2018:1213', 'https://access.redhat.com/errata/RHSA-2018:1525'} | null | {'https://github.com/paramiko/paramiko/commit/fa29bd8446c8eab237f5187d28787727b4610516'} | {'https://github.com/paramiko/paramiko/commit/fa29bd8446c8eab237f5187d28787727b4610516'} |
PyPI | GHSA-vcqg-3p29-xw73 | Integer overflow in Pillow | libImaging/TiffDecode.c in Pillow before 6.2.2 has a TIFF decoding integer overflow, related to realloc. | {'CVE-2020-5310'} | 2022-03-03T05:12:56.420256Z | 2021-11-03T18:04:41Z | MODERATE | null | {'CWE-190'} | {'https://github.com/python-pillow/Pillow/commit/4e2def2539ec13e53a82e06c4b3daf00454100c4', 'https://github.com/pypa/advisory-db/blob/7872b0a91b4d980f749e6d75a81f8cc1af32829f/vulns/pillow/PYSEC-2020-81.yaml', 'https://nvd.nist.gov/vuln/detail/CVE-2020-5310', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2MMU3WT2X64GS5WHDPKKC2WZA7UIIQ3A/', 'https://pillow.readthedocs.io/en/stable/releasenotes/6.2.2.html', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/4e2def2539ec13e53a82e06c4b3daf00454100c4'} | {'https://github.com/python-pillow/Pillow/commit/4e2def2539ec13e53a82e06c4b3daf00454100c4'} |
PyPI | PYSEC-2021-491 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.RFFT`. Eigen code operating on an empty matrix can trigger on an assertion and will cause program termination. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-ph87-fvjr-v33w', 'CVE-2021-29563'} | 2021-12-09T06:34:53.142665Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-ph87-fvjr-v33w'} | null | {'https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1'} | {'https://github.com/tensorflow/tensorflow/commit/31bd5026304677faa8a0b77602c6154171b9aec1'} |
PyPI | PYSEC-2022-162 | null | Weblate is a web based localization tool with tight version control integration. Prior to version 4.11.1, Weblate didn't properly sanitize some arguments passed to Git and Mercurial, allowing them to change their behavior in an unintended way. Instances where untrusted users cannot create new components are not affected. The issues were fixed in the 4.11.1 release. | {'GHSA-3872-f48p-pxqj', 'CVE-2022-24727'} | 2022-03-11T17:35:01.661733Z | 2022-03-04T17:15:00Z | null | null | null | {'https://github.com/WeblateOrg/weblate/security/advisories/GHSA-3872-f48p-pxqj', 'https://github.com/WeblateOrg/weblate/commit/d83672a3e7415da1490334e2c9431e5da1966842', 'https://github.com/WeblateOrg/weblate/commit/35d59f1f040541c358cece0a8d4a63183ca919b8'} | null | {'https://github.com/WeblateOrg/weblate/commit/35d59f1f040541c358cece0a8d4a63183ca919b8', 'https://github.com/WeblateOrg/weblate/commit/d83672a3e7415da1490334e2c9431e5da1966842'} | {'https://github.com/WeblateOrg/weblate/commit/35d59f1f040541c358cece0a8d4a63183ca919b8', 'https://github.com/WeblateOrg/weblate/commit/d83672a3e7415da1490334e2c9431e5da1966842'} |
PyPI | PYSEC-2021-184 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-4fg4-p75j-w5xj', 'CVE-2021-29547'} | 2021-08-27T03:22:29.791310Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj', 'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} | null | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} |
PyPI | PYSEC-2020-70 | null | In openapi-python-client before version 0.5.3, there is a path traversal vulnerability. If a user generated a client using a maliciously crafted OpenAPI document, it is possible for generated files to be placed in arbitrary locations on disk. | {'CVE-2020-15141', 'GHSA-7wgr-7666-7pwj'} | 2020-08-20T18:02:00Z | 2020-08-14T17:15:00Z | null | null | null | {'https://github.com/triaxtec/openapi-python-client/blob/main/CHANGELOG.md#053---2020-08-13', 'https://pypi.org/project/openapi-python-client', 'https://github.com/triaxtec/openapi-python-client/commit/3e7dfae5d0b3685abf1ede1bc6c086a116ac4746', 'https://github.com/triaxtec/openapi-python-client/security/advisories/GHSA-7wgr-7666-7pwj'} | null | {'https://github.com/triaxtec/openapi-python-client/commit/3e7dfae5d0b3685abf1ede1bc6c086a116ac4746'} | {'https://github.com/triaxtec/openapi-python-client/commit/3e7dfae5d0b3685abf1ede1bc6c086a116ac4746'} |
PyPI | PYSEC-2021-669 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.CTCGreedyDecoder`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1615440b17b364b875eb06f43d087381f1460a65/tensorflow/core/kernels/ctc_decoder_ops.cc#L37-L50) has a `CHECK_LT` inserted to validate some invariants. When this condition is false, the program aborts, instead of returning a valid error to the user. This abnormal termination can be weaponized in denial of service attacks. 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-fphq-gw9m-ghrv', 'CVE-2021-29543'} | 2021-12-09T06:35:22.170510Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fphq-gw9m-ghrv', 'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2'} | null | {'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2'} | {'https://github.com/tensorflow/tensorflow/commit/ea3b43e98c32c97b35d52b4c66f9107452ca8fb2'} |
PyPI | PYSEC-2021-393 | null | TensorFlow is an open source platform for machine learning. In affected versions if `tf.summary.create_file_writer` is called with non-scalar arguments code crashes due to a `CHECK`-fail. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-gh8h-7j2j-qv4f', 'CVE-2021-41200'} | 2021-11-13T06:52:42.348013Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/46909', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh8h-7j2j-qv4f', 'https://github.com/tensorflow/tensorflow/commit/874bda09e6702cd50bac90b453b50bcc65b2769e'} | null | {'https://github.com/tensorflow/tensorflow/commit/874bda09e6702cd50bac90b453b50bcc65b2769e'} | {'https://github.com/tensorflow/tensorflow/commit/874bda09e6702cd50bac90b453b50bcc65b2769e'} |
PyPI | PYSEC-2021-239 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `DepthwiseConv` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/depthwise_conv.cc#L287-L288). An attacker can craft a model such that `input`'s fourth dimension would be 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-rf3h-xgv5-2q39', 'CVE-2021-29602'} | 2021-08-27T03:22:39.570829Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/cbda3c6b2dbbd3fbdc482ff8c0170a78ec2e97d0', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rf3h-xgv5-2q39'} | null | {'https://github.com/tensorflow/tensorflow/commit/cbda3c6b2dbbd3fbdc482ff8c0170a78ec2e97d0'} | {'https://github.com/tensorflow/tensorflow/commit/cbda3c6b2dbbd3fbdc482ff8c0170a78ec2e97d0'} |
PyPI | GHSA-84mw-34w6-2q43 | Null pointer dereference via invalid Ragged Tensors | ### Impact
Calling `tf.raw_ops.RaggedTensorToVariant` with arguments specifying an invalid ragged tensor results in a null pointer dereference:
```python
import tensorflow as tf
input_tensor = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)
filter_tensor = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)
tf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 56, 56, 56, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 23, 1])
```
```python
import tensorflow as tf
input_tensor = tf.constant([], shape=[2, 2, 2, 2, 0], dtype=tf.float32)
filter_tensor = tf.constant([], shape=[0, 0, 2, 6, 2], dtype=tf.float32)
tf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 56, 39, 34, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])
```
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:
```cc
int ragged_rank = batched_ragged.ragged_rank();
auto batched_splits_top_vec = batched_ragged.splits(0).vec<SPLIT_TYPE>();
```
Since `batched_ragged` contains no elements, `batched_ragged.splits` is a null vector, thus `batched_ragged.splits(0)` will result in dereferencing `nullptr`.
### Patches
We have patched the issue in GitHub commit [b055b9c474cd376259dde8779908f9eeaf097d93](https://github.com/tensorflow/tensorflow/commit/b055b9c474cd376259dde8779908f9eeaf097d93).
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-2021-29516'} | 2022-03-03T05:14:02.406102Z | 2021-05-21T14:20:58Z | LOW | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/commit/b055b9c474cd376259dde8779908f9eeaf097d93', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-84mw-34w6-2q43', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29516'} | null | {'https://github.com/tensorflow/tensorflow/commit/b055b9c474cd376259dde8779908f9eeaf097d93'} | {'https://github.com/tensorflow/tensorflow/commit/b055b9c474cd376259dde8779908f9eeaf097d93'} |
PyPI | PYSEC-2021-686 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel. Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when `parent_output_index` is shorter than `row_split`. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-8gv3-57p6-g35r', 'CVE-2021-29560'} | 2021-12-09T06:35:25.121902Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8gv3-57p6-g35r'} | null | {'https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121'} | {'https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121'} |
PyPI | PYSEC-2022-119 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseCountSparseOutput` is vulnerable to a heap overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'CVE-2022-21740', 'GHSA-44qp-9wwf-734r'} | 2022-03-09T00:18:24.990899Z | 2022-02-03T15:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/adbbabdb0d3abb3cdeac69e38a96de1d678b24b3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-44qp-9wwf-734r', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/count_ops.cc#L168-L273', 'https://github.com/tensorflow/tensorflow/commit/2b7100d6cdff36aa21010a82269bc05a6d1cc74a'} | null | {'https://github.com/tensorflow/tensorflow/commit/adbbabdb0d3abb3cdeac69e38a96de1d678b24b3', 'https://github.com/tensorflow/tensorflow/commit/2b7100d6cdff36aa21010a82269bc05a6d1cc74a'} | {'https://github.com/tensorflow/tensorflow/commit/adbbabdb0d3abb3cdeac69e38a96de1d678b24b3', 'https://github.com/tensorflow/tensorflow/commit/2b7100d6cdff36aa21010a82269bc05a6d1cc74a'} |
PyPI | GHSA-68w8-qjq3-2gfm | Path Traversal in Django | Django before 2.2.24, 3.x before 3.1.12, and 3.2.x before 3.2.4 has a potential directory traversal via django.contrib.admindocs. Staff members could use the TemplateDetailView view to check the existence of arbitrary files. Additionally, if (and only if) the default admindocs templates have been customized by application developers to also show file contents, then not only the existence but also the file contents would have been exposed. In other words, there is directory traversal outside of the template root directories. | {'CVE-2021-33203'} | 2022-03-03T05:13:08.484696Z | 2021-06-10T17:21:00Z | MODERATE | null | {'CWE-22'} | {'https://www.djangoproject.com/weblog/2021/jun/02/security-releases/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-33203', 'https://github.com/django/django', 'https://github.com/django/django/commit/053cc9534d174dc89daba36724ed2dcb36755b90', 'https://security.netapp.com/advisory/ntap-20210727-0004/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/B4SQG2EAF4WCI2SLRL6XRDJ3RPK3ZRDV/', 'https://groups.google.com/forum/#!forum/django-announce', 'https://docs.djangoproject.com/en/3.2/releases/security/'} | null | {'https://github.com/django/django/commit/053cc9534d174dc89daba36724ed2dcb36755b90'} | {'https://github.com/django/django/commit/053cc9534d174dc89daba36724ed2dcb36755b90'} |
PyPI | PYSEC-2021-405 | null | TensorFlow is an open source platform for machine learning. In affected versions the code behind `tf.function` API can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive. This occurs due to using a non-reentrant `Lock` Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario. 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-h67m-xg8f-fxcf', 'CVE-2021-41213'} | 2021-11-13T06:52:44.160284Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf', 'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7'} | null | {'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7'} | {'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7'} |
PyPI | PYSEC-2019-156 | null | The scipy.weave component in SciPy before 0.12.1 creates insecure temporary directories. | {'CVE-2013-4251'} | 2021-07-05T00:01:27.093286Z | 2019-11-04T20:15:00Z | null | null | null | {'https://security-tracker.debian.org/tracker/CVE-2013-4251', 'http://lists.fedoraproject.org/pipermail/package-announce/2013-October/119771.html', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/88052', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2013-4251', 'https://bugzilla.suse.com/show_bug.cgi?id=CVE-2013-4251', 'http://www.securityfocus.com/bid/63008', 'https://access.redhat.com/security/cve/cve-2013-4251', 'http://lists.fedoraproject.org/pipermail/package-announce/2013-October/119759.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2013-November/120696.html', 'https://github.com/scipy/scipy/commit/bd296e0336420b840fcd2faabb97084fd252a973'} | null | {'https://github.com/scipy/scipy/commit/bd296e0336420b840fcd2faabb97084fd252a973'} | {'https://github.com/scipy/scipy/commit/bd296e0336420b840fcd2faabb97084fd252a973'} |
PyPI | GHSA-qjj8-32p7-h289 | Division by 0 in `ResourceGather` | ### Impact
An attacker can trigger a crash via a floating point exception in `tf.raw_ops.ResourceGather`:
```python
import tensorflow as tf
tensor = tf.constant(value=[[]],shape=(0,1),dtype=tf.uint32)
v = tf.Variable(tensor)
tf.raw_ops.ResourceGather(
resource=v.handle,
indices=[0],
dtype=tf.uint32,
batch_dims=1,
validate_indices=False)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L725-L731) computes the value of a value, `batch_size`, and then divides by it without checking that this value is not 0.
### Patches
We have patched the issue in GitHub commit [ac117ee8a8ea57b73d34665cdf00ef3303bc0b11](https://github.com/tensorflow/tensorflow/commit/ac117ee8a8ea57b73d34665cdf00ef3303bc0b11).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### 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-2021-37653'} | 2022-03-03T05:13:31.939194Z | 2021-08-25T14:43:04Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qjj8-32p7-h289', 'https://github.com/tensorflow/tensorflow/commit/ac117ee8a8ea57b73d34665cdf00ef3303bc0b11', 'https://github.com/tensorflow/tensorflow/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37653'} | null | {'https://github.com/tensorflow/tensorflow/commit/ac117ee8a8ea57b73d34665cdf00ef3303bc0b11'} | {'https://github.com/tensorflow/tensorflow/commit/ac117ee8a8ea57b73d34665cdf00ef3303bc0b11'} |
PyPI | PYSEC-2021-813 | 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'} | 2021-12-09T06:35:42.034732Z | 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-m7fm-4jfh-jrg6 | Use after free in boosted trees creation | ### Impact
The implementation for `tf.raw_ops.BoostedTreesCreateEnsemble` can result in a use after free error if an attacker supplies specially crafted arguments:
```python
import tensorflow as tf
v= tf.Variable([0.0])
tf.raw_ops.BoostedTreesCreateEnsemble(
tree_ensemble_handle=v.handle,
stamp_token=[0],
tree_ensemble_serialized=['0'])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent `free`-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed.
### Patches
We have patched the issue in GitHub commit [5ecec9c6fbdbc6be03295685190a45e7eee726ab](https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab).
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-2021-37652'} | 2022-03-03T05:13:39.615885Z | 2021-08-25T14:43:07Z | HIGH | null | {'CWE-416'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37652', 'https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m7fm-4jfh-jrg6'} | null | {'https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab'} | {'https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab'} |
PyPI | PYSEC-2021-540 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`. The implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty. Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen in this op's implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29612', 'GHSA-2xgj-xhgf-ggjv'} | 2021-12-09T06:35:00.791095Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2xgj-xhgf-ggjv', 'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2', 'https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0'} | null | {'https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0', 'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2'} | {'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2', 'https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0'} |
PyPI | GHSA-vhr6-pvjm-9qwf | User passwords are stored in clear text in the Django session | ### Impact
django-two-factor-auth versions 1.11 and before store the user's password in clear text in the user session (base64-encoded). The password is stored in the session when the user submits their username and password, and is removed once they complete authentication by entering a two-factor authentication code. This means that the password is stored in clear text in the session for an arbitrary amount of time, and potentially forever if the user begins the login process by entering their username and password, and then leaves before entering their two-factor authentication code.
The severity of this issue depends on which type of session storage you have configured: in the worst case, if you're using Django's default database session storage, then users' password are stored in clear text in your database. In the best case, if you're using Django's signed cookie session, then users' passwords are only stored in clear text within their browser's cookie store. In the common case of using Django's cache session store, the users' password are stored in clear text in whatever cache storage you have configured (typically Memcached or Redis).
### Patches
Upgrade to version 1.12 to resolve this issue.
After upgrading, users should be sure to delete any clear text passwords that have been stored. For example, if you're using the database session backend, you'll likely want to delete any session record from the database and purge that data from any database backups or replicas.
In addition, affected organizations who have suffered a database breach while using an affected version should inform their users that their clear text passwords have been compromised. All organizations should encourage users whose passwords were insecurely stored to change these passwords on any sites where they were used.
### Workarounds
Switching Django's session storage to use signed cookies instead of the database or cache lessens the impact of this issue, but should not be done without a thorough understanding of the security tradeoffs of using signed cookies rather than a server-side session storage. There is no way to fully mitigate the issue without upgrading.
### References
For an explanation of why storing cleartext password is a substantial vulnerability: [Hashing Passwords: One-Way Road to Security](https://auth0.com/blog/hashing-passwords-one-way-road-to-security/).
For documentation on configuring the Django session storage engine: [Django session documentation](https://docs.djangoproject.com/en/3.0/topics/http/sessions/).
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [the repo](https://github.com/Bouke/django-two-factor-auth) | {'CVE-2020-15105'} | 2022-03-03T05:13:41.560571Z | 2020-07-10T20:55:00Z | HIGH | null | {'CWE-312'} | {'https://github.com/Bouke/django-two-factor-auth/blob/master/CHANGELOG.md#112---2020-07-08', 'https://github.com/Bouke/django-two-factor-auth/security/advisories/GHSA-vhr6-pvjm-9qwf', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15105', 'https://github.com/Bouke/django-two-factor-auth/commit/454fd9842fa6e8bb772dbf0943976bc8e3335359'} | null | {'https://github.com/Bouke/django-two-factor-auth/commit/454fd9842fa6e8bb772dbf0943976bc8e3335359'} | {'https://github.com/Bouke/django-two-factor-auth/commit/454fd9842fa6e8bb772dbf0943976bc8e3335359'} |
PyPI | PYSEC-2021-794 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of division in TFLite is [vulnerable to a division by 0 error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/div.cc). There is no check that the divisor tensor does not contain zero elements. We have patched the issue in GitHub commit 1e206baedf8bef0334cca3eb92bab134ef525a28. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37683', 'GHSA-rhrq-64mq-hf9h'} | 2021-12-09T06:35:39.607152Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1e206baedf8bef0334cca3eb92bab134ef525a28', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rhrq-64mq-hf9h'} | null | {'https://github.com/tensorflow/tensorflow/commit/1e206baedf8bef0334cca3eb92bab134ef525a28'} | {'https://github.com/tensorflow/tensorflow/commit/1e206baedf8bef0334cca3eb92bab134ef525a28'} |
PyPI | GHSA-xpm8-98mx-h4c5 | Critical severity vulnerability that affects MLAlchemy | An exploitable vulnerability exists in the YAML parsing functionality in the parse_yaml_query method in parser.py in MLAlchemy before 0.2.2. When processing YAML-Based queries for data, a YAML parser can execute arbitrary Python commands resulting in command execution because load is used where safe_load should have been used. An attacker can insert Python into loaded YAML to trigger this vulnerability. | {'CVE-2017-16615'} | 2022-03-22T20:47:02.968039Z | 2018-07-13T16:01:07Z | CRITICAL | null | null | {'https://github.com/thanethomson/MLAlchemy/commit/bc795757febdcce430d89f9d08f75c32d6989d3c', 'https://github.com/thanethomson/MLAlchemy/issues/1', 'https://github.com/thanethomson/MLAlchemy', 'https://joel-malwarebenchmark.github.io/blog/2017/11/08/cve-2017-16615-critical-restful-web-applications-vulnerability/', 'https://github.com/advisories/GHSA-xpm8-98mx-h4c5', 'https://nvd.nist.gov/vuln/detail/CVE-2017-16615'} | null | {'https://github.com/thanethomson/MLAlchemy/commit/bc795757febdcce430d89f9d08f75c32d6989d3c'} | {'https://github.com/thanethomson/MLAlchemy/commit/bc795757febdcce430d89f9d08f75c32d6989d3c'} |
PyPI | GHSA-g8q7-xv52-hf9f | Feedgen Vulnerable to XML Denial of Service Attacks | ### Impact
The *feedgen* library allows supplying XML as content for some of the available fields. This XML will be parsed and integrated into the existing XML tree. During this process, feedgen is vulnerable to [XML Denial of Service Attacks](https://docs.microsoft.com/en-us/archive/msdn-magazine/2009/november/xml-denial-of-service-attacks-and-defenses) (e.g. XML Bomb).
This becomes a concern in particular if feedgen is used to include content from untrused sources and if XML (including XHTML) is directly included instead of providing plain tex content only.
### Patches
This problem has been fixed in feedgen 0.9.0 which disallows XML entity expansion and external resources.
### Workarounds
Updating is strongly recommended and should not be problematic. Nevertheless, as a workaround, avoid providing XML directly to feedgen or ensure that no entity expansion is part of the XML.
### References
- [Security Briefs - XML Denial of Service Attacks and Defenses](https://docs.microsoft.com/en-us/archive/msdn-magazine/2009/november/xml-denial-of-service-attacks-and-defenses)
- [Billion laughs attack](https://en.wikipedia.org/wiki/Billion_laughs_attack#cite_note-2)
### For more information
If you have any questions or comments about this advisory:
- Open an issue in [lkiesow/python-feedgen](https://github.com/lkiesow/python-feedgen/issues)
- Send an email to security@lkiesow.de | {'CVE-2020-5227'} | 2022-03-03T05:14:08.476905Z | 2020-01-28T22:37:50Z | HIGH | null | {'CWE-776'} | {'https://github.com/lkiesow/python-feedgen/security/advisories/GHSA-g8q7-xv52-hf9f', 'https://docs.microsoft.com/en-us/archive/msdn-magazine/2009/november/xml-denial-of-service-attacks-and-defenses', 'https://github.com/lkiesow/python-feedgen/commit/f57a01b20fa4aaaeccfa417f28e66b4084b9d0cf', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/T6I5ENUYGFNMIH6ZQ62FZ6VU2WD3SIOI/', 'https://nvd.nist.gov/vuln/detail/CVE-2020-5227'} | null | {'https://github.com/lkiesow/python-feedgen/commit/f57a01b20fa4aaaeccfa417f28e66b4084b9d0cf'} | {'https://github.com/lkiesow/python-feedgen/commit/f57a01b20fa4aaaeccfa417f28e66b4084b9d0cf'} |
PyPI | PYSEC-2020-281 | null | In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'CVE-2020-15201', 'GHSA-p5f8-gfw5-33w4'} | 2021-12-09T06:34:41.679840Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | GHSA-hjmq-236j-8m87 | Denial of service in tensorflow-lite | ### Impact
In TensorFlow Lite models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44
### Patches
We have patched the issue in 204945b and will release patch releases for all affected versions.
We recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1.
### Workarounds
A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps.
However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been discovered from a variant analysis of [GHSA-p2cq-cprg-frvm](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2cq-cprg-frvm). | {'CVE-2020-15213'} | 2021-08-26T15:19:56Z | 2020-09-25T18:28:53Z | MODERATE | null | {'CWE-119', 'CWE-770'} | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15213', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} |
PyPI | PYSEC-2021-281 | 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 all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec. 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-2021-37659', 'GHSA-q3g3-h9r4-prrc'} | 2021-08-27T03:22:44.808272Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q3g3-h9r4-prrc', 'https://github.com/tensorflow/tensorflow/commit/93f428fd1768df147171ed674fee1fc5ab8309ec'} | null | {'https://github.com/tensorflow/tensorflow/commit/93f428fd1768df147171ed674fee1fc5ab8309ec'} | {'https://github.com/tensorflow/tensorflow/commit/93f428fd1768df147171ed674fee1fc5ab8309ec'} |
PyPI | GHSA-8r7c-3cm2-3h8f | Memory leak in Tensorflow | ### Impact
If a graph node is invalid, TensorFlow can leak memory in the [implementation of `ImmutableExecutorState::Initialize`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/common_runtime/immutable_executor_state.cc#L84-L262):
```cc
Status s = params_.create_kernel(n->properties(), &item->kernel);
if (!s.ok()) {
item->kernel = nullptr;
s = AttachDef(s, *n);
return s;
}
```
Here, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak.
### Patches
We have patched the issue in GitHub commit [c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd](https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd).
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-2022-23578'} | 2022-03-03T05:12:43.766858Z | 2022-02-10T00:33:13Z | MODERATE | null | {'CWE-401'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8r7c-3cm2-3h8f', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/common_runtime/immutable_executor_state.cc#L84-L262', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23578'} | null | {'https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd'} | {'https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd'} |
PyPI | PYSEC-2022-61 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated. 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-2022-21737', 'GHSA-f2vv-v9cg-qhh7'} | 2022-03-09T00:17:31.433747Z | 2022-02-03T14:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/bincount_op.cc', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2vv-v9cg-qhh7', 'https://github.com/tensorflow/tensorflow/commit/7019ce4f68925fd01cdafde26f8d8c938f47e6f9'} | null | {'https://github.com/tensorflow/tensorflow/commit/7019ce4f68925fd01cdafde26f8d8c938f47e6f9'} | {'https://github.com/tensorflow/tensorflow/commit/7019ce4f68925fd01cdafde26f8d8c938f47e6f9'} |
PyPI | PYSEC-2021-452 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/496c2630e51c1a478f095b084329acedb253db6b/tensorflow/core/kernels/conv_grad_shape_utils.cc#L130) does a modulus operation where the divisor is controlled by the caller. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-r4pj-74mg-8868', 'CVE-2021-29524'} | 2021-12-09T06:34:47.095784Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r4pj-74mg-8868'} | null | {'https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a'} | {'https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a'} |
PyPI | PYSEC-2021-517 | null | TensorFlow is an end-to-end open source platform for machine learning. The reference implementation of the `GatherNd` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/reference_ops.h#L966). An attacker can craft a model such that `params` input would be an empty tensor. In turn, `params_shape.Dims(.)` would be zero, in at least one dimension. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-3w67-q784-6w7c', 'CVE-2021-29589'} | 2021-12-09T06:34:57.180094Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3w67-q784-6w7c', 'https://github.com/tensorflow/tensorflow/commit/8e45822aa0b9f5df4b4c64f221e64dc930a70a9d'} | null | {'https://github.com/tensorflow/tensorflow/commit/8e45822aa0b9f5df4b4c64f221e64dc930a70a9d'} | {'https://github.com/tensorflow/tensorflow/commit/8e45822aa0b9f5df4b4c64f221e64dc930a70a9d'} |
PyPI | PYSEC-2021-147 | null | in SiCKRAGE, versions 4.2.0 to 10.0.11.dev1 are vulnerable to Stored Cross-Site-Scripting (XSS) due to user input not being validated properly when processed by the server. Therefore, an attacker can inject arbitrary JavaScript code inside the application, and possibly steal a user’s sensitive information. | {'CVE-2021-25925', 'GHSA-rmp7-f2vp-3rq4'} | 2021-08-27T03:22:21.507681Z | 2021-04-12T14:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-rmp7-f2vp-3rq4', 'https://github.com/SiCKRAGE/SiCKRAGE/commit/9f42426727e16609ad3d1337f6637588b8ed28e4', 'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25925'} | null | {'https://github.com/SiCKRAGE/SiCKRAGE/commit/9f42426727e16609ad3d1337f6637588b8ed28e4'} | {'https://github.com/SiCKRAGE/SiCKRAGE/commit/9f42426727e16609ad3d1337f6637588b8ed28e4'} |
PyPI | GHSA-4278-2v5v-65r4 | Heap buffer overflow in `RaggedBinCount` | ### Impact
If the `splits` argument of `RaggedBincount` does not specify a valid [`SparseTensor`](https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow:
```python
import tensorflow as tf
tf.raw_ops.RaggedBincount(splits=[0], values=[1,1,1,1,1], size=5, weights=[1,2,3,4], binary_output=False)
```
This will cause a read from outside the bounds of the `splits` tensor buffer in the [implementation of the `RaggedBincount` op](https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L433):
```cc
for (int idx = 0; idx < num_values; ++idx) {
while (idx >= splits(batch_idx)) {
batch_idx++;
}
...
}
```
Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds.
### Patches
We have patched the issue in GitHub commit [eebb96c2830d48597d055d247c0e9aebaea94cd5](https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.
### 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-2021-29512'} | 2022-03-03T05:13:33.208875Z | 2021-05-21T14:20:40Z | LOW | null | {'CWE-120', 'CWE-787'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29512', 'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4278-2v5v-65r4'} | null | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} |
PyPI | GHSA-rfw2-x9f8-2f6m | Cross-Site Scripting | LinkedIn Oncall through 1.4.0 allows reflected XSS via /query because of mishandling of the "No results found for" message in the search bar. | {'CVE-2021-26722'} | 2022-03-03T05:13:54.478783Z | 2021-04-30T17:27:53Z | MODERATE | null | {'CWE-79'} | {'https://github.com/linkedin/oncall/issues/341', 'https://pypi.org/project/oncall/', 'https://github.com/linkedin/oncall/commit/843bc106a1c1b1699e9e52b6b0d01c7efe1d6225', 'https://nvd.nist.gov/vuln/detail/CVE-2021-26722'} | null | {'https://github.com/linkedin/oncall/commit/843bc106a1c1b1699e9e52b6b0d01c7efe1d6225'} | {'https://github.com/linkedin/oncall/commit/843bc106a1c1b1699e9e52b6b0d01c7efe1d6225'} |
PyPI | PYSEC-2021-844 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `tf.math.segment_*` operations results in a `CHECK`-fail related abort (and denial of service) if a segment id in `segment_ids` is large. This is similar to CVE-2021-29584 (and similar other reported vulnerabilities in TensorFlow, localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using `AddDim`. However, if the number of elements in the tensor overflows an `int64_t` value, `AddDim` results in a `CHECK` failure which provokes a `std::abort`. Instead, code should use `AddDimWithStatus`. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-cq76-mxrc-vchh', 'CVE-2021-41195'} | 2021-12-13T06:20:24.247166Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/46888', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cq76-mxrc-vchh', 'https://github.com/tensorflow/tensorflow/pull/51733', 'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429'} | null | {'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429'} | {'https://github.com/tensorflow/tensorflow/commit/e9c81c1e1a9cd8dd31f4e83676cab61b60658429'} |
PyPI | PYSEC-2020-278 | null | In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has the same shape as the `values` one. The values in these tensors are always accessed in parallel. Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'CVE-2020-15198', 'GHSA-jc87-6vpp-7ff3'} | 2021-12-09T06:34:41.523521Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jc87-6vpp-7ff3', 'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | PYSEC-2021-628 | null | TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41219', 'GHSA-4f99-p9c2-3j8x'} | 2021-12-09T06:35:10.275299Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x'} | null | {'https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae'} | {'https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae'} |
PyPI | PYSEC-2021-278 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToSparse`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/ragged_tensor_to_sparse_kernel.cc#L30) has an incomplete validation of the splits values: it does not check that they are in increasing order. We have patched the issue in GitHub commit 1071f554dbd09f7e101324d366eec5f4fe5a3ece. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-4xfp-4pfp-89wg', 'CVE-2021-37656'} | 2021-08-27T03:22:44.528249Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4xfp-4pfp-89wg'} | null | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece'} | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece'} |
PyPI | PYSEC-2021-782 | 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'} | 2021-12-09T06:35:38.525134Z | 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-2021-297 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions most implementations of convolution operators in TensorFlow are affected by a division by 0 vulnerability where an attacker can trigger a denial of service via a crash. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/framework/common_shape_fns.cc#L577) is missing several validations before doing divisions and modulo operations. We have patched the issue in GitHub commit 8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4. 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-2021-37675', 'GHSA-9c8h-2mv3-49ww'} | 2021-08-27T03:22:46.293986Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c8h-2mv3-49ww'} | null | {'https://github.com/tensorflow/tensorflow/commit/8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4'} | {'https://github.com/tensorflow/tensorflow/commit/8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4'} |
PyPI | PYSEC-2021-88 | null | Zope is an open-source web application server. In Zope versions prior to 4.6 and 5.2, users can access untrusted modules indirectly through Python modules that are available for direct use. By default, only users with the Manager role can add or edit Zope Page Templates through the web, but sites that allow untrusted users to add/edit Zope Page Templates through the web are at risk from this vulnerability. The problem has been fixed in Zope 5.2 and 4.6. As a workaround, a site administrator can restrict adding/editing Zope Page Templates through the web using the standard Zope user/role permission mechanisms. Untrusted users should not be assigned the Zope Manager role and adding/editing Zope Page Templates through the web should be restricted to trusted users only. | {'GHSA-962m-m8jw-8wrr', 'GHSA-5pr9-v234-jw36', 'CVE-2021-32633'} | 2021-06-02T03:47:57.190321Z | 2021-05-21T14:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-962m-m8jw-8wrr', 'http://www.openwall.com/lists/oss-security/2021/05/22/1', 'http://www.openwall.com/lists/oss-security/2021/05/21/1', 'https://github.com/zopefoundation/Zope/security/advisories/GHSA-5pr9-v234-jw36', 'https://github.com/zopefoundation/Zope/commit/1f8456bf1f908ea46012537d52bd7e752a532c91'} | null | {'https://github.com/zopefoundation/Zope/commit/1f8456bf1f908ea46012537d52bd7e752a532c91'} | {'https://github.com/zopefoundation/Zope/commit/1f8456bf1f908ea46012537d52bd7e752a532c91'} |
PyPI | PYSEC-2020-297 | null | In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | {'GHSA-qhxx-j73r-qpm2', 'CVE-2020-26266'} | 2021-12-09T06:34:44.221678Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qhxx-j73r-qpm2'} | null | {'https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2'} | {'https://github.com/tensorflow/tensorflow/commit/ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2'} |
PyPI | GHSA-gg84-qgv9-w4pq | CRLF injection in httplib2 | ### Impact
Attacker controlling unescaped part of uri for `httplib2.Http.request()` could change request headers and body, send additional hidden requests to same server.
Impacts software that uses httplib2 with uri constructed by string concatenation, as opposed to proper urllib building with escaping.
### Patches
Problem has been fixed in 0.18.0
Space, CR, LF characters are now quoted before any use.
This solution should not impact any valid usage of httplib2 library, that is uri constructed by urllib.
### Workarounds
Create URI with `urllib.parse` family functions: `urlencode`, `urlunsplit`.
```diff
user_input = " HTTP/1.1\r\ninjected: attack\r\nignore-http:"
-uri = "https://api.server/?q={}".format(user_input)
+uri = urllib.parse.urlunsplit(("https", "api.server", "/v1", urllib.parse.urlencode({"q": user_input}), ""))
http.request(uri)
```
### References
https://cwe.mitre.org/data/definitions/93.html
https://docs.python.org/3/library/urllib.parse.html
Thanks to Recar https://github.com/Ciyfly for finding vulnerability and discrete notification.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [httplib2](https://github.com/httplib2/httplib2/issues/new)
* Email [current maintainer at 2020-05](mailto:temotor@gmail.com) | {'CVE-2020-11078'} | 2022-03-03T05:13:27.348443Z | 2020-05-20T15:55:47Z | LOW | null | {'CWE-93'} | {'https://lists.debian.org/debian-lts-announce/2020/06/msg00000.html', 'https://lists.apache.org/thread.html/rad8872fc99f670958c2774e2bf84ee32a3a0562a0c787465cf3dfa23@%3Cissues.beam.apache.org%3E', 'https://lists.apache.org/thread.html/r23711190c2e98152cb6f216b95090d5eeb978543bb7e0bad22ce47fc@%3Cissues.beam.apache.org%3E', 'https://lists.apache.org/thread.html/rc9eff9572946142b657c900fe63ea4bbd3535911e8d4ce4d08fe4b89@%3Ccommits.allura.apache.org%3E', 'https://lists.apache.org/thread.html/r7f364000066748299b331b615ba51c62f55ab5b201ddce9a22d98202@%3Cissues.beam.apache.org%3E', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/IXCX2AWROGWGY5GXR7VN3BKF34A2FO6J/', 'https://lists.apache.org/thread.html/r69a462e690b5f2c3d418a288a2c98ae764d58587bd0b5d6ab141f25f@%3Cissues.beam.apache.org%3E', 'https://lists.apache.org/thread.html/r4d35dac106fab979f0db75a07fc4e320ad848b722103e79667ff99e1@%3Cissues.beam.apache.org%3E', 'https://nvd.nist.gov/vuln/detail/CVE-2020-11078', 'https://github.com/httplib2/httplib2/security/advisories/GHSA-gg84-qgv9-w4pq', 'https://github.com/httplib2/httplib2/commit/a1457cc31f3206cf691d11d2bf34e98865873e9e', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/PZJ3D6JSM7CFZESZZKGUW2VX55BOSOXI/'} | null | {'https://github.com/httplib2/httplib2/commit/a1457cc31f3206cf691d11d2bf34e98865873e9e'} | {'https://github.com/httplib2/httplib2/commit/a1457cc31f3206cf691d11d2bf34e98865873e9e'} |
PyPI | GHSA-8fxw-76px-3rxv | Memory leak in Tensorflow | ### Impact
If a user passes a list of strings to `dlpack.to_dlpack` there is a memory leak following an expected validation failure:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L100-L104
The allocated memory is from
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L256
The issue occurs because the `status` argument during validation failures is not properly checked:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L265-L267
Since each of the above methods can return an error status, the `status` value must be checked before continuing.
### Patches
We have patched the issue in 22e07fb204386768e5bcbea563641ea11f96ceb8 and will release a patch release for all affected versions.
We recommend users to upgrade to TensorFlow 2.2.1 or 2.3.1.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been discovered during variant analysis of [GHSA-rjjg-hgv6-h69v](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v). | {'CVE-2020-15192'} | 2021-08-26T15:09:35Z | 2020-09-25T18:28:17Z | MODERATE | null | {'CWE-20'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15192', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fxw-76px-3rxv', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} |
PyPI | PYSEC-2022-158 | null | Tensorflow is an Open Source Machine Learning Framework. When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so `flr->config_proto` is `nullptr`. 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-fpcp-9h7m-ffpx', 'CVE-2022-23595'} | 2022-03-09T00:18:30.220756Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/jit/xla_platform_info.cc#L43-L104', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fpcp-9h7m-ffpx', 'https://github.com/tensorflow/tensorflow/commit/e21af685e1828f7ca65038307df5cc06de4479e8'} | null | {'https://github.com/tensorflow/tensorflow/commit/e21af685e1828f7ca65038307df5cc06de4479e8'} | {'https://github.com/tensorflow/tensorflow/commit/e21af685e1828f7ca65038307df5cc06de4479e8'} |
PyPI | PYSEC-2021-444 | 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'} | 2021-12-09T06:34:45.869282Z | 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 | PYSEC-2022-77 | 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'} | 2022-03-09T00:17:33.439630Z | 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 | PYSEC-2022-98 | null | Tensorflow is an Open Source Machine Learning Framework. Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a `SavedModel` file (fixing the first one would trigger the same dereference in the second place). First, during constant folding, the `GraphDef` might not have the required nodes for the binary operation. If a node is missing, the correposning `mul_*child` would be null, and the dereference in the subsequent line would be incorrect. We have a similar issue during `IsIdentityConsumingSwitch`. 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-2022-23589', 'GHSA-9px9-73fg-3fqp'} | 2022-03-09T00:17:36.051133Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/mutable_graph_view.cc#L59-L74', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/constant_folding.cc#L3466-L3497', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9px9-73fg-3fqp', 'https://github.com/tensorflow/tensorflow/commit/045deec1cbdebb27d817008ad5df94d96a08b1bf', 'https://github.com/tensorflow/tensorflow/commit/0a365c029e437be0349c31f8d4c9926b69fa3fa1'} | null | {'https://github.com/tensorflow/tensorflow/commit/045deec1cbdebb27d817008ad5df94d96a08b1bf', 'https://github.com/tensorflow/tensorflow/commit/0a365c029e437be0349c31f8d4c9926b69fa3fa1'} | {'https://github.com/tensorflow/tensorflow/commit/045deec1cbdebb27d817008ad5df94d96a08b1bf', 'https://github.com/tensorflow/tensorflow/commit/0a365c029e437be0349c31f8d4c9926b69fa3fa1'} |
PyPI | PYSEC-2021-151 | null | TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L446). Before the `for` loop, `batch_idx` is set to 0. The attacker sets `splits(0)` to be 7, hence the `while` loop does not execute and `batch_idx` remains 0. This then results in writing to `out(-1, bin)`, which is before the heap allocated buffer for the output tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected. | {'CVE-2021-29514', 'GHSA-8h46-5m9h-7553'} | 2021-08-27T03:22:23.861341Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8h46-5m9h-7553'} | null | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} |
PyPI | PYSEC-2021-501 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` is vulnerable to a division by 0. The implementation(https://github.com/tensorflow/tensorflow/blob/279bab6efa22752a2827621b7edb56a730233bd8/tensorflow/core/kernels/maxpooling_op.cc#L1033-L1034) fails to validate that the batch dimension of the tensor is non-zero, before dividing by this quantity. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-9vpm-rcf4-9wqw', 'CVE-2021-29573'} | 2021-12-09T06:34:54.700321Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vpm-rcf4-9wqw', 'https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d'} | null | {'https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d'} | {'https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d'} |
PyPI | GHSA-cjvr-mfj7-j4j8 | Incorrect Authorization and Exposure of Sensitive Information to an Unauthorized Actor in scrapy | ### Impact
If you manually define cookies on a [`Request`](https://docs.scrapy.org/en/latest/topics/request-response.html#scrapy.http.Request) object, and that `Request` object gets a redirect response, the new `Request` object scheduled to follow the redirect keeps those user-defined cookies, regardless of the target domain.
### Patches
Upgrade to Scrapy 2.6.0, which resets cookies when creating `Request` objects to follow redirects¹, and drops the ``Cookie`` header if manually-defined if the redirect target URL domain name does not match the source URL domain name².
If you are using Scrapy 1.8 or a lower version, and upgrading to Scrapy 2.6.0 is not an option, you may upgrade to Scrapy 1.8.2 instead.
¹ At that point the original, user-set cookies have been processed by the cookie middleware into the global or request-specific cookiejar, with their domain restricted to the domain of the original URL, so when the cookie middleware processes the new (redirect) request it will incorporate those cookies into the new request as long as the domain of the new request matches the domain of the original request.
² This prevents cookie leaks to unintended domains even if the cookies middleware is not used.
### Workarounds
If you cannot upgrade, set your cookies using a list of dictionaries instead of a single dictionary, as described in the [`Request` documentation](https://docs.scrapy.org/en/latest/topics/request-response.html#scrapy.http.Request), and set the right domain for each cookie.
Alternatively, you can [disable cookies altogether](https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#std-setting-COOKIES_ENABLED), or [limit target domains](https://docs.scrapy.org/en/latest/topics/spiders.html#scrapy.spiders.Spider.allowed_domains) to domains that you trust with all your user-set cookies.
### References
* Originally reported at [huntr.dev](https://huntr.dev/bounties/3da527b1-2348-4f69-9e88-2e11a96ac585/)
### For more information
If you have any questions or comments about this advisory:
* [Open an issue](https://github.com/scrapy/scrapy/issues)
* [Email us](mailto:opensource@zyte.com) | {'CVE-2022-0577'} | 2022-04-05T19:00:24.954658Z | 2022-03-01T22:12:47Z | MODERATE | null | {'CWE-863', 'CWE-200'} | {'https://huntr.dev/bounties/3da527b1-2348-4f69-9e88-2e11a96ac585', 'https://github.com/scrapy/scrapy', 'https://lists.debian.org/debian-lts-announce/2022/03/msg00021.html', 'https://github.com/scrapy/scrapy/security/advisories/GHSA-cjvr-mfj7-j4j8', 'https://nvd.nist.gov/vuln/detail/CVE-2022-0577', 'https://github.com/pypa/advisory-database/tree/main/vulns/scrapy/PYSEC-2022-159.yaml', 'https://github.com/scrapy/scrapy/commit/8ce01b3b76d4634f55067d6cfdf632ec70ba304a'} | null | {'https://github.com/scrapy/scrapy/commit/8ce01b3b76d4634f55067d6cfdf632ec70ba304a'} | {'https://github.com/scrapy/scrapy/commit/8ce01b3b76d4634f55067d6cfdf632ec70ba304a'} |
PyPI | GHSA-92vm-mxjf-jqf3 | Improper Verification of Cryptographic Signature in starkbank-ecdsa | The `verify` function in the Stark Bank Python ECDSA library (starkbank-ecdsa) 2.0.0 fails to check that the signature is non-zero, which allows attackers to forge signatures on arbitrary messages. | {'CVE-2021-43572'} | 2022-03-29T22:16:59.824727Z | 2021-11-10T20:41:39Z | CRITICAL | null | {'CWE-347'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-43572', 'https://github.com/starkbank/ecdsa-python/commit/d136170666e9510eb63c2572551805807bd4c17f', 'https://research.nccgroup.com/2021/11/08/technical-advisory-arbitrary-signature-forgery-in-stark-bank-ecdsa-libraries/', 'https://github.com/starkbank/ecdsa-python', 'https://github.com/starkbank/ecdsa-python/releases/tag/v2.0.1'} | null | {'https://github.com/starkbank/ecdsa-python/commit/d136170666e9510eb63c2572551805807bd4c17f'} | {'https://github.com/starkbank/ecdsa-python/commit/d136170666e9510eb63c2572551805807bd4c17f'} |
PyPI | GHSA-pwqf-9h7j-7mv8 | Incorrect threshold signature computation in TUF | ### Impact
Metadadata signature verification, as used in `tuf.client.updater`, counted each of multiple signatures with identical authorized keyids separately towards the threshold. Therefore, an attacker with access to a valid signing key could create multiple valid signatures in order to meet the minimum threshold of keys before the metadata was considered valid.
The tuf maintainers would like to thank Erik MacLean of Analog Devices, Inc. for reporting this issue.
### Patches
A [fix](https://github.com/theupdateframework/tuf/pull/974) is available in version [0.12.2](https://github.com/theupdateframework/tuf/releases/tag/v0.12.2) or newer.
### Workarounds
No workarounds are known for this issue.
### References
* [CVE-2020-6174](https://nvd.nist.gov/vuln/detail/CVE-2020-6174)
* Pull request resolving the issue [PR 974](https://github.com/theupdateframework/tuf/pull/974) | {'CVE-2020-6174'} | 2022-03-23T23:15:05.623162Z | 2020-08-21T16:25:26Z | CRITICAL | null | {'CWE-347'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-6174', 'https://github.com/theupdateframework/tuf/security/advisories/GHSA-pwqf-9h7j-7mv8', 'https://github.com/theupdateframework/tuf', 'https://github.com/theupdateframework/tuf/pull/974/commits/a0397c7c820ec1c30ebc793cc9469b61c8d3f50e', 'https://github.com/theupdateframework/tuf/pull/974'} | null | {'https://github.com/theupdateframework/tuf/pull/974/commits/a0397c7c820ec1c30ebc793cc9469b61c8d3f50e'} | {'https://github.com/theupdateframework/tuf/pull/974/commits/a0397c7c820ec1c30ebc793cc9469b61c8d3f50e'} |
PyPI | PYSEC-2021-385 | null | EnroCrypt is a Python module for encryption and hashing. Prior to version 1.1.4, EnroCrypt used the MD5 hashing algorithm in the hashing file. Beginners who are unfamiliar with hashes can face problems as MD5 is considered an insecure hashing algorithm. The vulnerability is patched in v1.1.4 of the product. As a workaround, users can remove the `MD5` hashing function from the file `hashing.py`. | {'CVE-2021-39182', 'GHSA-35m5-8cvj-8783'} | 2021-11-09T21:27:02.006228Z | 2021-11-08T15:15:00Z | null | null | null | {'https://github.com/Morgan-Phoenix/EnroCrypt/security/advisories/GHSA-35m5-8cvj-8783', 'https://github.com/Morgan-Phoenix/EnroCrypt/commit/e652d56ac60eadfc26489ab83927af13a9b9d8ce'} | null | {'https://github.com/Morgan-Phoenix/EnroCrypt/commit/e652d56ac60eadfc26489ab83927af13a9b9d8ce'} | {'https://github.com/Morgan-Phoenix/EnroCrypt/commit/e652d56ac60eadfc26489ab83927af13a9b9d8ce'} |
PyPI | GHSA-gwcx-jrx4-92w2 | Segfault in `simplifyBroadcast` in Tensorflow | ### Impact
The [`simplifyBroadcast` function in the MLIR-TFRT infrastructure in TensorFlow](https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/mlir/tfrt/jit/transforms/tf_cpurt_symbolic_shape_optimization.cc#L149-L205) is vulnerable to a segfault (hence, denial of service), if called with scalar shapes.
```cc
size_t maxRank = 0;
for (auto shape : llvm::enumerate(shapes)) {
auto found_shape = analysis.dimensionsForShapeTensor(shape.value());
if (!found_shape) return {};
shapes_found.push_back(*found_shape);
maxRank = std::max(maxRank, found_shape->size());
}
SmallVector<const ShapeComponentAnalysis::SymbolicDimension*>
joined_dimensions(maxRank);
```
If all shapes are scalar, then `maxRank` is 0, so we build an empty `SmallVector`.
### Patches
We have patched the issue in GitHub commit [35f0fabb4c178253a964d7aabdbb15c6a398b69a](https://github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a).
The fix will be included in TensorFlow 2.8.0. This is the only affected version.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2022-23593'} | 2022-03-28T19:45:16.566211Z | 2022-02-09T23:32:08Z | HIGH | null | {'CWE-754'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-23593', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gwcx-jrx4-92w2', 'https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/compiler/mlir/tfrt/jit/transforms/tf_cpurt_symbolic_shape_optimization.cc#L149-L205'} | null | {'https://github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a'} | {'https://github.com/tensorflow/tensorflow/commit/35f0fabb4c178253a964d7aabdbb15c6a398b69a'} |
PyPI | PYSEC-2017-47 | null | Cross-site request forgery in the REST API in IPython 2 and 3. | {'CVE-2015-5607'} | 2021-07-15T02:22:14.864070Z | 2017-09-20T16:29:00Z | null | null | null | {'https://github.com/ipython/ipython/commit/1415a9710407e7c14900531813c15ba6165f0816', 'https://github.com/ipython/ipython/commit/a05fe052a18810e92d9be8c1185952c13fe4e5b0', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-July/162936.html', 'http://www.openwall.com/lists/oss-security/2015/07/21/3', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-July/162671.html', 'https://bugzilla.redhat.com/show_bug.cgi?id=1243842'} | null | {'https://github.com/ipython/ipython/commit/1415a9710407e7c14900531813c15ba6165f0816', 'https://github.com/ipython/ipython/commit/a05fe052a18810e92d9be8c1185952c13fe4e5b0'} | {'https://github.com/ipython/ipython/commit/a05fe052a18810e92d9be8c1185952c13fe4e5b0', 'https://github.com/ipython/ipython/commit/1415a9710407e7c14900531813c15ba6165f0816'} |
PyPI | GHSA-jcxv-2j3h-mg59 | Improper Restriction of Operations within the Bounds of a Memory Buffer in OpenCV | OpenCV 3.3.1 (corresponding with opencv-python and opencv-contrib-python 3.3.1.11) has a Buffer Overflow in the cv::PxMDecoder::readData function in grfmt_pxm.cpp, because an incorrect size value is used. | {'CVE-2017-17760'} | 2022-03-03T05:13:10.385850Z | 2021-10-12T22:03:09Z | MODERATE | null | {'CWE-119'} | {'https://lists.debian.org/debian-lts-announce/2018/01/msg00008.html', 'https://github.com/opencv/opencv/pull/10369/commits/7bbe1a53cfc097b82b1589f7915a2120de39274c', 'http://www.securityfocus.com/bid/102974', 'https://lists.debian.org/debian-lts-announce/2021/10/msg00028.html', 'https://nvd.nist.gov/vuln/detail/CVE-2017-17760', 'https://github.com/opencv/opencv/issues/10351', 'https://github.com/opencv/opencv-python', 'https://lists.debian.org/debian-lts-announce/2018/07/msg00030.html'} | null | {'https://github.com/opencv/opencv/pull/10369/commits/7bbe1a53cfc097b82b1589f7915a2120de39274c'} | {'https://github.com/opencv/opencv/pull/10369/commits/7bbe1a53cfc097b82b1589f7915a2120de39274c'} |
PyPI | GHSA-6w9p-88qg-p3g3 | Cross-site Scripting in CKAN | In CKAN, versions 2.9.0 to 2.9.3 are affected by a stored XSS vulnerability via SVG file upload of users’ profile picture. This allows low privileged application users to store malicious scripts in their profile picture. These scripts are executed in a victim’s browser when they open the malicious profile picture | {'CVE-2021-25967'} | 2022-03-03T05:13:24.940426Z | 2021-12-03T20:44:48Z | MODERATE | null | {'CWE-79'} | {'https://github.com/ckan/ckan/commit/5a46989c0a4f2c2873ca182c196da83b82babd25', 'https://github.com/ckan/ckan/pull/6477', 'https://nvd.nist.gov/vuln/detail/CVE-2021-25967', 'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25967', 'https://github.com/ckan/ckan'} | null | {'https://github.com/ckan/ckan/commit/5a46989c0a4f2c2873ca182c196da83b82babd25'} | {'https://github.com/ckan/ckan/commit/5a46989c0a4f2c2873ca182c196da83b82babd25'} |
PyPI | PYSEC-2021-690 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference in the implementation of `tf.raw_ops.EditDistance`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/79865b542f9ffdc9caeb255631f7c56f1d4b6517/tensorflow/core/kernels/edit_distance_op.cc#L103-L159) has incomplete validation of the input parameters. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29564', 'GHSA-75f6-78jr-4656'} | 2021-12-09T06:35:25.817127Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-75f6-78jr-4656', 'https://github.com/tensorflow/tensorflow/commit/f4c364a5d6880557f6f5b6eb5cee2c407f0186b3'} | null | {'https://github.com/tensorflow/tensorflow/commit/f4c364a5d6880557f6f5b6eb5cee2c407f0186b3'} | {'https://github.com/tensorflow/tensorflow/commit/f4c364a5d6880557f6f5b6eb5cee2c407f0186b3'} |
PyPI | PYSEC-2016-5 | null | Buffer overflow in the ImagingLibTiffDecode function in libImaging/TiffDecode.c in Pillow before 3.1.1 allows remote attackers to overwrite memory via a crafted TIFF file. | {'CVE-2016-0740', 'GHSA-hggx-3h72-49ww'} | 2021-07-05T00:01:23.915090Z | 2016-04-13T16:59:00Z | null | null | null | {'https://security.gentoo.org/glsa/201612-52', 'https://github.com/advisories/GHSA-hggx-3h72-49ww', 'https://github.com/python-pillow/Pillow/commit/6dcbf5bd96b717c58d7b642949da8d323099928e', 'https://github.com/python-pillow/Pillow/blob/c3cb690fed5d4bf0c45576759de55d054916c165/CHANGES.rst', 'http://www.debian.org/security/2016/dsa-3499'} | null | {'https://github.com/python-pillow/Pillow/commit/6dcbf5bd96b717c58d7b642949da8d323099928e'} | {'https://github.com/python-pillow/Pillow/commit/6dcbf5bd96b717c58d7b642949da8d323099928e'} |
PyPI | PYSEC-2019-140 | null | Pallets Werkzeug before 0.15.3, when used with Docker, has insufficient debugger PIN randomness because Docker containers share the same machine id. | {'CVE-2019-14806', 'GHSA-gq9m-qvpx-68hc'} | 2019-09-11T00:15:00Z | 2019-08-09T15:15:00Z | null | null | null | {'https://palletsprojects.com/blog/werkzeug-0-15-3-released/', 'https://github.com/advisories/GHSA-gq9m-qvpx-68hc', 'https://github.com/pallets/werkzeug/commit/00bc43b1672e662e5e3b8cecd79e67fc968fa246', 'https://github.com/pallets/werkzeug/blob/7fef41b120327d3912fbe12fb64f1951496fcf3e/src/werkzeug/debug/__init__.py#L168', 'http://lists.opensuse.org/opensuse-security-announce/2019-09/msg00047.html', 'http://lists.opensuse.org/opensuse-security-announce/2019-09/msg00034.html'} | null | {'https://github.com/pallets/werkzeug/commit/00bc43b1672e662e5e3b8cecd79e67fc968fa246'} | {'https://github.com/pallets/werkzeug/commit/00bc43b1672e662e5e3b8cecd79e67fc968fa246'} |
PyPI | GHSA-qc53-44cj-vfvx | Denial of Service in Tensorflow | ### Impact
The `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L185
However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor.
### Patches
We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.
We recommend users to upgrade to TensorFlow 2.3.1.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability is a variant of [GHSA-p5f8-gfw5-33w4](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4) | {'CVE-2020-15197'} | 2021-08-26T15:12:09Z | 2020-09-25T18:28:30Z | MODERATE | null | {'CWE-20', 'CWE-617'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qc53-44cj-vfvx', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15197', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | PYSEC-2021-413 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for the `Cudnn*` operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow. This occurs because the ranks of the `input`, `input_h` and `input_c` parameters are not validated, but code assumes they have certain values. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41221', 'GHSA-cqv6-3phm-hcwx'} | 2021-11-13T06:52:45.325083Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx', 'https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6'} | null | {'https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6'} | {'https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6'} |
PyPI | PYSEC-2021-556 | null | TensorFlow is an end-to-end open source platform for machine learning. If a user does not provide a valid padding value to `tf.raw_ops.MatrixDiagPartOp`, then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from. We have patched the issue in GitHub commit 482da92095c4d48f8784b1f00dda4f81c28d2988. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37643', 'GHSA-fcwc-p4fc-c5cc'} | 2021-12-09T06:35:02.665889Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/482da92095c4d48f8784b1f00dda4f81c28d2988', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fcwc-p4fc-c5cc'} | null | {'https://github.com/tensorflow/tensorflow/commit/482da92095c4d48f8784b1f00dda4f81c28d2988'} | {'https://github.com/tensorflow/tensorflow/commit/482da92095c4d48f8784b1f00dda4f81c28d2988'} |
PyPI | PYSEC-2021-805 | null | TensorFlow is an open source platform for machine learning. In affected versions TensorFlow allows tensor to have a large number of dimensions and each dimension can be as large as desired. However, the total number of elements in a tensor must fit within an `int64_t`. If an overflow occurs, `MultiplyWithoutOverflow` would return a negative result. In the majority of TensorFlow codebase this then results in a `CHECK`-failure. Newer constructs exist which return a `Status` instead of crashing the binary. This is similar to CVE-2021-29584. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-prcg-wp5q-rv7p', 'CVE-2021-41197'} | 2021-12-09T06:35:40.728775Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a871989d7b6c18cdebf2fb4f0e5c5b62fbc19edf', 'https://github.com/tensorflow/tensorflow/issues/46890', 'https://github.com/tensorflow/tensorflow/commit/d81b1351da3e8c884ff836b64458d94e4a157c15', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-prcg-wp5q-rv7p', 'https://github.com/tensorflow/tensorflow/commit/7c1692bd417eb4f9b33ead749a41166d6080af85', 'https://github.com/tensorflow/tensorflow/issues/51908'} | null | {'https://github.com/tensorflow/tensorflow/commit/d81b1351da3e8c884ff836b64458d94e4a157c15', 'https://github.com/tensorflow/tensorflow/commit/7c1692bd417eb4f9b33ead749a41166d6080af85', 'https://github.com/tensorflow/tensorflow/commit/a871989d7b6c18cdebf2fb4f0e5c5b62fbc19edf'} | {'https://github.com/tensorflow/tensorflow/commit/7c1692bd417eb4f9b33ead749a41166d6080af85', 'https://github.com/tensorflow/tensorflow/commit/a871989d7b6c18cdebf2fb4f0e5c5b62fbc19edf', 'https://github.com/tensorflow/tensorflow/commit/d81b1351da3e8c884ff836b64458d94e4a157c15'} |
PyPI | PYSEC-2016-4 | null | The _Rsa15 class in the RSA 1.5 algorithm implementation in jwa.py in jwcrypto before 0.3.2 lacks the Random Filling protection mechanism, which makes it easier for remote attackers to obtain cleartext data via a Million Message Attack (MMA). | {'CVE-2016-6298'} | 2021-07-05T00:01:22.078687Z | 2016-09-01T23:59:00Z | null | null | null | {'http://www.securityfocus.com/bid/92729', 'https://github.com/latchset/jwcrypto/issues/65', 'https://github.com/latchset/jwcrypto/pull/66', 'https://github.com/latchset/jwcrypto/commit/eb5be5bd94c8cae1d7f3ba9801377084d8e5a7ba', 'https://github.com/latchset/jwcrypto/releases/tag/v0.3.2'} | null | {'https://github.com/latchset/jwcrypto/commit/eb5be5bd94c8cae1d7f3ba9801377084d8e5a7ba'} | {'https://github.com/latchset/jwcrypto/commit/eb5be5bd94c8cae1d7f3ba9801377084d8e5a7ba'} |
PyPI | PYSEC-2022-21 | null | Products.ATContentTypes are the core content types for Plone 2.1 - 4.3. Versions of Plone that are dependent on Products.ATContentTypes prior to version 3.0.6 are vulnerable to reflected cross site scripting and open redirect when an attacker can get a compromised version of the image_view_fullscreen page in a cache, for example in Varnish. The technique is known as cache poisoning. Any later visitor can get redirected when clicking on a link on this page. Usually only anonymous users are affected, but this depends on the user's cache settings. Version 3.0.6 of Products.ATContentTypes has been released with a fix. This version works on Plone 5.2, Python 2 only. As a workaround, make sure the image_view_fullscreen page is not stored in the cache. More information about the vulnerability and cvmitigation measures is available in the GitHub Security Advisory. | {'CVE-2022-23599', 'GHSA-g4c2-ghfg-g5rh'} | 2022-02-04T17:23:45.077200Z | 2022-01-28T22:15:00Z | null | null | null | {'https://github.com/plone/Products.ATContentTypes/security/advisories/GHSA-g4c2-ghfg-g5rh', 'https://github.com/plone/Products.ATContentTypes/commit/fc793f88f35a15a68b52e4abed77af0da5fdbab8'} | null | {'https://github.com/plone/Products.ATContentTypes/commit/fc793f88f35a15a68b52e4abed77af0da5fdbab8'} | {'https://github.com/plone/Products.ATContentTypes/commit/fc793f88f35a15a68b52e4abed77af0da5fdbab8'} |
PyPI | PYSEC-2021-412 | null | TensorFlow is an open source platform for machine learning. In affected versions the async implementation of `CollectiveReduceV2` suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected. | {'GHSA-gpfh-jvf9-7wg5', 'CVE-2021-41220'} | 2021-11-13T06:52:45.180075Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5'} | null | {'https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75'} | {'https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75'} |
PyPI | GHSA-pqrv-8r2f-7278 | Crash due to erroneous `StatusOr` in TensorFlow | ### Impact
A `GraphDef` from a TensorFlow `SavedModel` can be maliciously altered to cause a TensorFlow process to crash due to encountering [a `StatusOr` value that is an error and forcibly extracting the value from it](https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/core/graph/graph.cc#L560-L567):
```cc
if (op_reg_data->type_ctor != nullptr) {
VLOG(3) << "AddNode: found type constructor for " << node_def.name();
const auto ctor_type =
full_type::SpecializeType(AttrSlice(node_def), op_reg_data->op_def);
const FullTypeDef ctor_typedef = ctor_type.ValueOrDie();
if (ctor_typedef.type_id() != TFT_UNSET) {
*(node_def.mutable_experimental_type()) = ctor_typedef;
}
}
```
If `ctor_type` is an error status, `ValueOrDie` results in a crash.
### Patches
We have patched the issue in GitHub commit [955059813cc325dc1db5e2daa6221271406d4439](https://github.com/tensorflow/tensorflow/commit/955059813cc325dc1db5e2daa6221271406d4439).
We have patched the issue in multiple GitHub commits and these will be included in TensorFlow 2.8.0 and TensorFlow 2.7.1, as both are affected.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2022-23590'} | 2022-02-11T20:00:42Z | 2022-02-09T23:29:38Z | MODERATE | null | {'CWE-754'} | {'https://github.com/tensorflow/tensorflow/commit/955059813cc325dc1db5e2daa6221271406d4439', 'https://github.com/tensorflow/tensorflow/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23590', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pqrv-8r2f-7278', 'https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/core/graph/graph.cc#L560-L567'} | null | {'https://github.com/tensorflow/tensorflow/commit/955059813cc325dc1db5e2daa6221271406d4439'} | {'https://github.com/tensorflow/tensorflow/commit/955059813cc325dc1db5e2daa6221271406d4439'} |
PyPI | PYSEC-2021-557 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions providing a negative element to `num_elements` list argument of `tf.raw_ops.TensorListReserve` causes the runtime to abort the process due to reallocating a `std::vector` to have a negative number of elements. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/list_kernels.cc#L312) calls `std::vector.resize()` with the new size controlled by input given by the user, without checking that this input is valid. We have patched the issue in GitHub commit 8a6e874437670045e6c7dc6154c7412b4a2135e2. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-27j5-4p9v-pp67', 'CVE-2021-37644'} | 2021-12-09T06:35:02.745951Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27j5-4p9v-pp67'} | null | {'https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2'} | {'https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2'} |
PyPI | GHSA-4365-fhm5-qcrx | Maliciously Crafted Model Archive Can Lead To Arbitrary File Write | ### Impact
An Archive Extraction (Zip Slip) vulnerability in the functionality that allows a user to load a trained model archive in Rasa 2.8.9 and older allows an attacker arbitrary write capability within specific directories using a malicious crafted archive file.
### Patches
The vulnerability is fixed in Rasa 2.8.10
### Workarounds
Mitigating steps for vulnerable end users are to ensure that they do not upload untrusted model files, and restrict CLI or API endpoint access where a malicious actor could target a deployed Rasa instance.
### For more information
If you have any questions or comments about this advisory:
* Email [the Rasa Security Team](mailto:security@rasa.com)
| {'CVE-2021-41127'} | 2022-03-03T05:13:21.545751Z | 2021-10-22T16:19:13Z | HIGH | null | {'CWE-23', 'CWE-22'} | {'https://github.com/RasaHQ/rasa/commit/1b6b502f52d73b4f8cd1959ce724b8ad0eb33989', 'https://github.com/RasaHQ/rasa/security/advisories/GHSA-4365-fhm5-qcrx', 'https://github.com/RasaHQ/rasa'} | null | {'https://github.com/RasaHQ/rasa/commit/1b6b502f52d73b4f8cd1959ce724b8ad0eb33989'} | {'https://github.com/RasaHQ/rasa/commit/1b6b502f52d73b4f8cd1959ce724b8ad0eb33989'} |
PyPI | GHSA-624f-cqvr-3qw4 | URL Redirection to Untrusted Site ('Open Redirect') in Flask-AppBuilder | ### Impact
If using Flask-AppBuilder OAuth, an attacker can share a carefully crafted URL with a trusted domain for an application built with Flask-AppBuilder, this URL can redirect a user to a malicious site. This is an open redirect vulnerability
### Patches
Install Flask-AppBuilder 3.2.2 or above
### Workarounds
Filter HTTP traffic containing `?next={next-site}` where the `next-site` domain is different from the application you are protecting
| {'CVE-2021-32805'} | 2022-03-03T05:14:09.479675Z | 2021-09-08T21:11:14Z | HIGH | null | {'CWE-601'} | {'https://github.com/dpgaspar/Flask-AppBuilder', 'https://nvd.nist.gov/vuln/detail/CVE-2021-32805', 'https://github.com/dpgaspar/Flask-AppBuilder/commit/6af28521589599b1dbafd6313256229ee9a4fa74', 'https://github.com/dpgaspar/Flask-AppBuilder/security/advisories/GHSA-624f-cqvr-3qw4', 'https://github.com/dpgaspar/Flask-AppBuilder/releases/tag/v3.3.2', 'https://pypi.org/project/Flask-AppBuilder/'} | null | {'https://github.com/dpgaspar/Flask-AppBuilder/commit/6af28521589599b1dbafd6313256229ee9a4fa74'} | {'https://github.com/dpgaspar/Flask-AppBuilder/commit/6af28521589599b1dbafd6313256229ee9a4fa74'} |
PyPI | PYSEC-2021-804 | null | TensorFlow is an open source platform for machine learning. In affected versions the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-m539-j985-hcr8', 'CVE-2021-41196'} | 2021-12-09T06:35:40.561915Z | 2021-11-05T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m539-j985-hcr8', 'https://github.com/tensorflow/tensorflow/issues/51936', 'https://github.com/tensorflow/tensorflow/commit/12b1ff82b3f26ff8de17e58703231d5a02ef1b8b'} | null | {'https://github.com/tensorflow/tensorflow/commit/12b1ff82b3f26ff8de17e58703231d5a02ef1b8b'} | {'https://github.com/tensorflow/tensorflow/commit/12b1ff82b3f26ff8de17e58703231d5a02ef1b8b'} |
PyPI | PYSEC-2021-31 | null | OMERO.web is open source Django-based software for managing microscopy imaging. OMERO.web before version 5.9.0 loads various information about the current user such as their id, name and the groups they are in, and these are available on the main webclient pages. This represents an information exposure vulnerability. Some additional information being loaded is not used by the webclient and is being removed in this release. This is fixed in version 5.9.0. | {'CVE-2021-21376', 'GHSA-gfp2-w5jm-955q'} | 2021-03-27T01:59:00Z | 2021-03-23T16:15:00Z | null | null | null | {'https://github.com/ome/omero-web/commit/952f8e5d28532fbb14fb665982211329d137908c', 'https://www.openmicroscopy.org/security/advisories/2021-SV1/', 'https://pypi.org/project/omero-web/', 'https://github.com/ome/omero-web/security/advisories/GHSA-gfp2-w5jm-955q', 'https://github.com/ome/omero-web/blob/master/CHANGELOG.md#590-march-2021'} | null | {'https://github.com/ome/omero-web/commit/952f8e5d28532fbb14fb665982211329d137908c'} | {'https://github.com/ome/omero-web/commit/952f8e5d28532fbb14fb665982211329d137908c'} |
PyPI | PYSEC-2017-46 | null | Cross-site scripting (XSS) vulnerability in IPython before 3.2 allows remote attackers to inject arbitrary web script or HTML via vectors involving JSON error messages and the /api/notebooks path. | {'CVE-2015-4707'} | 2021-07-15T02:22:14.812507Z | 2017-09-20T18:29:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=1235688', 'http://www.securityfocus.com/bid/75328', 'https://github.com/ipython/ipython/commit/7222bd53ad089a65fd610fab4626f9d0ab47dfce', 'https://ipython.org/ipython-doc/3/whatsnew/version3.html', 'http://www.openwall.com/lists/oss-security/2015/06/22/7', 'https://github.com/ipython/ipython/commit/c2078a53543ed502efd968649fee1125e0eb549c'} | null | {'https://github.com/ipython/ipython/commit/7222bd53ad089a65fd610fab4626f9d0ab47dfce', 'https://github.com/ipython/ipython/commit/c2078a53543ed502efd968649fee1125e0eb549c'} | {'https://github.com/ipython/ipython/commit/7222bd53ad089a65fd610fab4626f9d0ab47dfce', 'https://github.com/ipython/ipython/commit/c2078a53543ed502efd968649fee1125e0eb549c'} |
PyPI | GHSA-5vq5-pg3r-9ph3 | Path Traversal in Zope | Zope is an open-source web application server. This advisory extends the previous advisory at https://github.com/zopefoundation/Zope/security/advisories/GHSA-5pr9-v234-jw36 with additional cases of TAL expression traversal vulnerabilities. Most Python modules are not available for using in TAL expressions that you can add through-the-web, for example in Zope Page Templates. This restriction avoids file system access, for example via the 'os' module. But some of the untrusted modules are available indirectly through Python modules that are available for direct use. By default, you need to have the Manager role to add or edit Zope Page Templates through the web. Only sites that allow untrusted users to add/edit Zope Page Templates through the web are at risk. The problem has been fixed in Zope 5.2.1 and 4.6.1. The workaround is the same as for https://github.com/zopefoundation/Zope/security/advisories/GHSA-5pr9-v234-jw36: A site administrator can restrict adding/editing Zope Page Templates through the web using the standard Zope user/role permission mechanisms. Untrusted users should not be assigned the Zope Manager role and adding/editing Zope Page Templates through the web should be restricted to trusted users only. | {'CVE-2021-32674'} | 2022-03-03T05:13:51.482119Z | 2021-06-10T17:22:08Z | HIGH | null | {'CWE-12', 'CWE-22'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-32674', 'https://github.com/zopefoundation/Zope/commit/1d897910139e2c0b11984fc9b78c1da1365bec21', 'https://github.com/zopefoundation/Zope', 'https://pypi.org/project/Zope/', 'https://github.com/zopefoundation/Zope/security/advisories/GHSA-5pr9-v234-jw36', 'https://github.com/zopefoundation/Zope/security/advisories/GHSA-rpcg-f9q6-2mq6'} | null | {'https://github.com/zopefoundation/Zope/commit/1d897910139e2c0b11984fc9b78c1da1365bec21'} | {'https://github.com/zopefoundation/Zope/commit/1d897910139e2c0b11984fc9b78c1da1365bec21'} |
PyPI | GHSA-7wgr-7666-7pwj | Path Traversal in openapi-python-client | ### Impact
Path traversal vulnerability. If a user generated a client using a maliciously crafted OpenAPI document, it is possible for generated files to be placed in arbitrary locations on disk.
Giving this a CVSS score of 3.0 (Low) with CVSS:3.0/AV:N/AC:H/PR:L/UI:R/S:C/C:N/I:L/A:N/E:P/RL:U/RC:C
### Patches
A fix is being worked on for version 0.5.3
### Workarounds
Inspect OpenAPI documents before generating clients for them.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [openapi-python-client](https://github.com/triaxtec/openapi-python-client/issues)
* Email us at [danthony@triaxtec.com](mailto:danthony@triaxtec.com) | {'CVE-2020-15141'} | 2022-03-03T05:13:07.819928Z | 2020-08-20T14:38:13Z | LOW | null | {'CWE-22'} | {'https://github.com/triaxtec/openapi-python-client/blob/main/CHANGELOG.md#053---2020-08-13', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15141', 'https://pypi.org/project/openapi-python-client', 'https://github.com/triaxtec/openapi-python-client/commit/3e7dfae5d0b3685abf1ede1bc6c086a116ac4746', 'https://github.com/triaxtec/openapi-python-client/security/advisories/GHSA-7wgr-7666-7pwj', 'https://pypi.org/project/openapi-python-client/'} | null | {'https://github.com/triaxtec/openapi-python-client/commit/3e7dfae5d0b3685abf1ede1bc6c086a116ac4746'} | {'https://github.com/triaxtec/openapi-python-client/commit/3e7dfae5d0b3685abf1ede1bc6c086a116ac4746'} |
PyPI | PYSEC-2021-691 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference in the implementation of `tf.raw_ops.SparseFillEmptyRows`. This is because of missing validation(https://github.com/tensorflow/tensorflow/blob/fdc82089d206e281c628a93771336bf87863d5e8/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L230-L231) that was covered under a `TODO`. If the `dense_shape` tensor is empty, then `dense_shape_t.vec<>()` would cause a null pointer dereference in the implementation of the op. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-r6pg-pjwc-j585', 'CVE-2021-29565'} | 2021-12-09T06:35:26.009362Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/faa76f39014ed3b5e2c158593b1335522e573c7f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r6pg-pjwc-j585'} | null | {'https://github.com/tensorflow/tensorflow/commit/faa76f39014ed3b5e2c158593b1335522e573c7f'} | {'https://github.com/tensorflow/tensorflow/commit/faa76f39014ed3b5e2c158593b1335522e573c7f'} |
PyPI | PYSEC-2022-159 | null | Exposure of Sensitive Information to an Unauthorized Actor in GitHub repository scrapy/scrapy prior to 2.6.1. | {'CVE-2022-0577', 'GHSA-cjvr-mfj7-j4j8'} | 2022-03-09T19:24:19.981012Z | 2022-03-02T04:15:00Z | null | null | null | {'https://huntr.dev/bounties/3da527b1-2348-4f69-9e88-2e11a96ac585', 'https://github.com/advisories/GHSA-cjvr-mfj7-j4j8', 'https://github.com/scrapy/scrapy/commit/8ce01b3b76d4634f55067d6cfdf632ec70ba304a'} | null | {'https://github.com/scrapy/scrapy/commit/8ce01b3b76d4634f55067d6cfdf632ec70ba304a'} | {'https://github.com/scrapy/scrapy/commit/8ce01b3b76d4634f55067d6cfdf632ec70ba304a'} |
PyPI | PYSEC-2021-445 | null | TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-772p-x54p-hjrv', 'CVE-2021-29517'} | 2021-12-09T06:34:46.030158Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv', 'https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f'} | null | {'https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f'} | {'https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f'} |
PyPI | PYSEC-2022-76 | null | Tensorflow is an Open Source Machine Learning Framework. The implementations of `Sparse*Cwise*` ops 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). 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. | {'GHSA-rrx2-r989-2c43', 'CVE-2022-23567'} | 2022-03-09T00:17:33.310692Z | 2022-02-03T12:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1b54cadd19391b60b6fcccd8d076426f7221d5e8', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc', '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-2021-853 | null | vault-cli is a configurable command-line interface tool (and python library) to interact with Hashicorp Vault. In versions before 3.0.0 vault-cli features the ability for rendering templated values. When a secret starts with the prefix `!template!`, vault-cli interprets the rest of the contents of the secret as a Jinja2 template. Jinja2 is a powerful templating engine and is not designed to safely render arbitrary templates. An attacker controlling a jinja2 template rendered on a machine can trigger arbitrary code, making this a Remote Code Execution (RCE) risk. If the content of the vault can be completely trusted, then this is not a problem. Otherwise, if your threat model includes cases where an attacker can manipulate a secret value read from the vault using vault-cli, then this vulnerability may impact you. In 3.0.0, the code related to interpreting vault templated secrets has been removed entirely. Users are advised to upgrade as soon as possible. For users unable to upgrade a workaround does exist. Using the environment variable `VAULT_CLI_RENDER=false` or the flag `--no-render` (placed between `vault-cli` and the subcommand, e.g. `vault-cli --no-render get-all`) or adding `render: false` to the vault-cli configuration yaml file disables rendering and removes the vulnerability. Using the python library, you can use: `vault_cli.get_client(render=False)` when creating your client to get a client that will not render templated secrets and thus operates securely. | {'GHSA-q34h-97wf-8r8j', 'CVE-2021-43837'} | 2021-12-16T21:30:16.499668Z | 2021-12-16T19:15:00Z | null | null | null | {'https://github.com/peopledoc/vault-cli/security/advisories/GHSA-q34h-97wf-8r8j', 'https://podalirius.net/en/publications/grehack-2021-optimizing-ssti-payloads-for-jinja2/', 'https://github.com/peopledoc/vault-cli/commit/3ba3955887fd6b7d4d646c8b260f21cebf5db852'} | null | {'https://github.com/peopledoc/vault-cli/commit/3ba3955887fd6b7d4d646c8b260f21cebf5db852'} | {'https://github.com/peopledoc/vault-cli/commit/3ba3955887fd6b7d4d646c8b260f21cebf5db852'} |
PyPI | PYSEC-2022-99 | null | Tensorflow is an Open Source Machine Learning Framework. A `GraphDef` from a TensorFlow `SavedModel` can be maliciously altered to cause a TensorFlow process to crash due to encountering a `StatusOr` value that is an error and forcibly extracting the value from it. We have patched the issue in multiple GitHub commits and these will be included in TensorFlow 2.8.0 and TensorFlow 2.7.1, as both are affected. | {'CVE-2022-23590', 'GHSA-pqrv-8r2f-7278'} | 2022-03-09T00:17:36.120513Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/955059813cc325dc1db5e2daa6221271406d4439', 'https://github.com/tensorflow/tensorflow/blob/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/core/graph/graph.cc#L560-L567', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pqrv-8r2f-7278'} | null | {'https://github.com/tensorflow/tensorflow/commit/955059813cc325dc1db5e2daa6221271406d4439'} | {'https://github.com/tensorflow/tensorflow/commit/955059813cc325dc1db5e2daa6221271406d4439'} |
PyPI | PYSEC-2021-150 | 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'} | 2021-08-27T03:22:23.682962Z | 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-2021-500 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.SdcaOptimizer` triggers undefined behavior due to dereferencing a null pointer. The implementation(https://github.com/tensorflow/tensorflow/blob/60a45c8b6192a4699f2e2709a2645a751d435cc3/tensorflow/core/kernels/sdca_internal.cc) does not validate that the user supplied arguments satisfy all constraints expected by the op(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SdcaOptimizer). 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-5gqf-456p-4836', 'CVE-2021-29572'} | 2021-12-09T06:34:54.530775Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5gqf-456p-4836', 'https://github.com/tensorflow/tensorflow/commit/f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb'} | null | {'https://github.com/tensorflow/tensorflow/commit/f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb'} | {'https://github.com/tensorflow/tensorflow/commit/f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb'} |
PyPI | PYSEC-2020-279 | null | In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the `splits` tensor has the minimum required number of elements. Code uses this quantity to initialize a different data structure. Since `BatchedMap` is equivalent to a vector, it needs to have at least one element to not be `nullptr`. If user passes a `splits` tensor that is empty or has exactly one element, we get a `SIGABRT` signal raised by the operating system. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'GHSA-x5cp-9pcf-pp3h', 'CVE-2020-15199'} | 2021-12-09T06:34:41.569566Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x5cp-9pcf-pp3h', 'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | PYSEC-2021-629 | null | TensorFlow is an open source platform for machine learning. In affected versions the async implementation of `CollectiveReduceV2` suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected. | {'GHSA-gpfh-jvf9-7wg5', 'CVE-2021-41220'} | 2021-12-09T06:35:10.358368Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5'} | null | {'https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75'} | {'https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75'} |
PyPI | PYSEC-2021-279 | 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 all operations of type `tf.raw_ops.MatrixDiagV*`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of `k` is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. We have patched the issue in GitHub commit f2a673bd34f0d64b8e40a551ac78989d16daad09. 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-2021-37657', 'GHSA-5xwc-mrhx-5g3m'} | 2021-08-27T03:22:44.622008Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f2a673bd34f0d64b8e40a551ac78989d16daad09', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5xwc-mrhx-5g3m'} | null | {'https://github.com/tensorflow/tensorflow/commit/f2a673bd34f0d64b8e40a551ac78989d16daad09'} | {'https://github.com/tensorflow/tensorflow/commit/f2a673bd34f0d64b8e40a551ac78989d16daad09'} |
PyPI | PYSEC-2021-783 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.SdcaOptimizerV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of `example_labels` is the same as the number of examples. We have patched the issue in GitHub commit a4e138660270e7599793fa438cd7b2fc2ce215a6. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-5hj3-vjjf-f5m7', 'CVE-2021-37672'} | 2021-12-09T06:35:38.609873Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hj3-vjjf-f5m7', 'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} | null | {'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} | {'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} |
PyPI | PYSEC-2021-296 | 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'} | 2021-08-27T03:22:46.211223Z | 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 | GHSA-c45w-2wxr-pp53 | Heap OOB read in `tf.raw_ops.Dequantize` | ### Impact
Due to lack of validation in `tf.raw_ops.Dequantize`, an attacker can trigger a read from outside of bounds of heap allocated data:
```python
import tensorflow as tf
input_tensor=tf.constant(
[75, 75, 75, 75, -6, -9, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\
-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,\
-10, -10, -10, -10], shape=[5, 10], dtype=tf.int32)
input_tensor=tf.cast(input_tensor, dtype=tf.quint8)
min_range = tf.constant([-10], shape=[1], dtype=tf.float32)
max_range = tf.constant([24, 758, 758, 758, 758], shape=[5], dtype=tf.float32)
tf.raw_ops.Dequantize(
input=input_tensor, min_range=min_range, max_range=max_range, mode='SCALED',
narrow_range=True, axis=0, dtype=tf.dtypes.float32)
```
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:
```cc
if (num_slices == 1) {
const float min_range = input_min_tensor.flat<float>()(0);
const float max_range = input_max_tensor.flat<float>()(0);
DequantizeTensor(ctx, input, min_range, max_range, &float_output);
} else {
...
auto min_ranges = input_min_tensor.vec<float>();
auto max_ranges = input_max_tensor.vec<float>();
for (int i = 0; i < num_slices; ++i) {
DequantizeSlice(ctx->eigen_device<Device>(), ctx,
input_tensor.template chip<1>(i), min_ranges(i),
max_ranges(i), output_tensor.template chip<1>(i));
...
}
}
```
### Patches
We have patched the issue in GitHub commit [5899741d0421391ca878da47907b1452f06aaf1b](https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1b).
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-2021-29582'} | 2022-03-03T05:12:52.909210Z | 2021-05-21T14:26:32Z | LOW | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1b', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29582', '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 | GHSA-w4vg-rf63-f3j3 | Arbitrary code using "crafted image file" approach affecting Pillow | Pillow before 3.3.2 allows context-dependent attackers to execute arbitrary code by using the "crafted image file" approach, related to an "Insecure Sign Extension" issue affecting the ImagingNew in Storage.c component. | {'CVE-2016-9190'} | 2022-04-26T18:17:06.543150Z | 2018-07-12T14:45:42Z | HIGH | null | {'CWE-284'} | {'http://pillow.readthedocs.io/en/3.4.x/releasenotes/3.3.2.html', 'https://github.com/python-pillow/Pillow/issues/2105', 'http://www.debian.org/security/2016/dsa-3710', 'https://github.com/advisories/GHSA-w4vg-rf63-f3j3', 'https://github.com/python-pillow/Pillow/pull/2146/commits/5d8a0be45aad78c5a22c8d099118ee26ef8144af', 'http://www.securityfocus.com/bid/94234', 'https://security.gentoo.org/glsa/201612-52', 'https://nvd.nist.gov/vuln/detail/CVE-2016-9190', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/pull/2146/commits/5d8a0be45aad78c5a22c8d099118ee26ef8144af'} | {'https://github.com/python-pillow/Pillow/pull/2146/commits/5d8a0be45aad78c5a22c8d099118ee26ef8144af'} |
PyPI | GHSA-vgmw-9cww-qq99 | Incorrect Authorization in calibreweb | calibreweb prior to version 0.6.16 contains an Incorrect Authorization vulnerability. | {'CVE-2022-0273'} | 2022-03-03T05:13:46.990224Z | 2022-01-31T00:00:29Z | MODERATE | null | {'CWE-863', 'CWE-284'} | {'https://huntr.dev/bounties/8f27686f-d698-4ab6-8ef0-899125792f13', 'https://github.com/janeczku/calibre-web/commit/0c0313f375bed7b035c8c0482bbb09599e16bfcf', 'https://github.com/janeczku/calibre-web', 'https://nvd.nist.gov/vuln/detail/CVE-2022-0273'} | null | {'https://github.com/janeczku/calibre-web/commit/0c0313f375bed7b035c8c0482bbb09599e16bfcf'} | {'https://github.com/janeczku/calibre-web/commit/0c0313f375bed7b035c8c0482bbb09599e16bfcf'} |
PyPI | PYSEC-2020-296 | null | In Tensorflow before version 2.4.0, when the `boxes` argument of `tf.image.crop_and_resize` has a very large value, the CPU kernel implementation receives it as a C++ `nan` floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. | {'GHSA-xwhf-g6j5-j5gc', 'CVE-2020-15266'} | 2021-12-09T06:34:44.028853Z | 2020-10-21T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/issues/42129', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc', 'https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845'} | null | {'https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845'} | {'https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845'} |
PyPI | PYSEC-2017-11 | null | Cross-site request forgery (CSRF) vulnerability in django CMS before 3.0.14, 3.1.x before 3.1.1 allows remote attackers to manipulate privileged users into performing unknown actions via unspecified vectors. | {'CVE-2015-5081'} | 2021-07-05T00:01:17.665929Z | 2017-08-18T18:29:00Z | null | null | null | {'https://www.django-cms.org/en/blog/2015/06/27/311-3014-release/', 'https://github.com/divio/django-cms/commit/f77cbc607d6e2a62e63287d37ad320109a2cc78a', 'http://www.openwall.com/lists/oss-security/2015/06/28/1'} | null | {'https://github.com/divio/django-cms/commit/f77cbc607d6e2a62e63287d37ad320109a2cc78a'} | {'https://github.com/divio/django-cms/commit/f77cbc607d6e2a62e63287d37ad320109a2cc78a'} |
PyPI | GHSA-3qgw-p4fm-x7gf | Division by zero in TFLite's convolution code | ### Impact
TFLite's [convolution code](https://github.com/tensorflow/tensorflow/blob/09c73bca7d648e961dd05898292d91a8322a9d45/tensorflow/lite/kernels/conv.cc) has multiple division where the divisor is controlled by the user and not checked to be non-zero. For example:
```cc
const int input_size = NumElements(input) / SizeOfDimension(input, 0);
```
### Patches
We have patched the issue in GitHub commit [ff489d95a9006be080ad14feb378f2b4dac35552](https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552).
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-2021-29594'} | 2022-03-03T05:13:11.303735Z | 2021-05-21T14:27:45Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3qgw-p4fm-x7gf', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29594', 'https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552'} | null | {'https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552'} | {'https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552'} |
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