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PyPI | GHSA-98gj-wwxm-cj3h | Moderate severity vulnerability that affects mistune | Cross-site scripting (XSS) vulnerability in the _keyify function in mistune.py in Mistune before 0.8.1 allows remote attackers to inject arbitrary web script or HTML by leveraging failure to escape the "key" argument. | {'CVE-2017-16876'} | 2022-03-03T05:13:01.434198Z | 2019-01-04T17:47:50Z | MODERATE | null | {'CWE-79'} | {'https://github.com/advisories/GHSA-98gj-wwxm-cj3h', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/NUR3GMHQBMA3UC4PFMCK6GCLOQC4LQQC/', 'https://github.com/lepture/mistune', 'https://bugzilla.redhat.com/show_bug.cgi?id=1524596', 'https://github.com/lepture/mistune/blob/master/CHANGES.rst', 'https://github.com/lepture/mistune/commit/5f06d724bc05580e7f203db2d4a4905fc1127f98', 'https://nvd.nist.gov/vuln/detail/CVE-2017-16876'} | null | {'https://github.com/lepture/mistune/commit/5f06d724bc05580e7f203db2d4a4905fc1127f98'} | {'https://github.com/lepture/mistune/commit/5f06d724bc05580e7f203db2d4a4905fc1127f98'} |
PyPI | GHSA-2v5j-q74q-r53f | django-helpdesk is vulnerable to Cross-site Scripting | django-helpdesk is vulnerable to Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting'). | {'CVE-2021-3994'} | 2022-03-03T05:13:24.793404Z | 2021-12-03T20:42:26Z | HIGH | null | {'CWE-79'} | {'https://github.com/django-helpdesk/django-helpdesk/commit/a22eb0673fe0b7784f99c6b5fd343b64a6700f06', 'https://huntr.dev/bounties/be7f211d-4bfd-44fd-91e8-682329906fbd', 'https://nvd.nist.gov/vuln/detail/CVE-2021-3994', 'https://github.com/django-helpdesk/django-helpdesk', 'https://github.com/django-helpdesk/django-helpdesk/releases/tag/0.3.2'} | null | {'https://github.com/django-helpdesk/django-helpdesk/commit/a22eb0673fe0b7784f99c6b5fd343b64a6700f06'} | {'https://github.com/django-helpdesk/django-helpdesk/commit/a22eb0673fe0b7784f99c6b5fd343b64a6700f06'} |
PyPI | PYSEC-2021-196 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can access data outside of bounds of heap allocated array in `tf.raw_ops.UnicodeEncode`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/472c1f12ad9063405737679d4f6bd43094e1d36d/tensorflow/core/kernels/unicode_ops.cc) assumes that the `input_value`/`input_splits` pair specify a valid sparse tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29559', 'GHSA-59q2-x2qc-4c97'} | 2021-08-27T03:22:31.940947Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-59q2-x2qc-4c97', 'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298'} | null | {'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298'} | {'https://github.com/tensorflow/tensorflow/commit/51300ba1cc2f487aefec6e6631fef03b0e08b298'} |
PyPI | PYSEC-2022-170 | null | mitmproxy is an interactive, SSL/TLS-capable intercepting proxy. In mitmproxy 7.0.4 and below, a malicious client or server is able to perform HTTP request smuggling attacks through mitmproxy. This means that a malicious client/server could smuggle a request/response through mitmproxy as part of another request/response's HTTP message body. While mitmproxy would only see one request, the target server would see multiple requests. A smuggled request is still captured as part of another request's body, but it does not appear in the request list and does not go through the usual mitmproxy event hooks, where users may have implemented custom access control checks or input sanitization. Unless mitmproxy is used to protect an HTTP/1 service, no action is required. The vulnerability has been fixed in mitmproxy 8.0.0 and above. There are currently no known workarounds. | {'GHSA-gcx2-gvj7-pxv3', 'CVE-2022-24766'} | 2022-03-29T18:37:43.309818Z | 2022-03-21T19:15:00Z | null | null | null | {'https://github.com/mitmproxy/mitmproxy/commit/b06fb6d157087d526bd02e7aadbe37c56865c71b', 'https://mitmproxy.org/posts/releases/mitmproxy8/', 'https://github.com/mitmproxy/mitmproxy/security/advisories/GHSA-gcx2-gvj7-pxv3'} | null | {'https://github.com/mitmproxy/mitmproxy/commit/b06fb6d157087d526bd02e7aadbe37c56865c71b'} | {'https://github.com/mitmproxy/mitmproxy/commit/b06fb6d157087d526bd02e7aadbe37c56865c71b'} |
PyPI | PYSEC-2021-529 | null | TensorFlow is an end-to-end open source platform for machine learning. The TFLite implementation of concatenation is vulnerable to an integer overflow issue(https://github.com/tensorflow/tensorflow/blob/7b7352a724b690b11bfaae2cd54bc3907daf6285/tensorflow/lite/kernels/concatenation.cc#L70-L76). An attacker can craft a model such that the dimensions of one of the concatenation input overflow the values of `int`. TFLite uses `int` to represent tensor dimensions, whereas TF uses `int64`. Hence, valid TF models can trigger an integer overflow when converted to TFLite format. 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-2021-29601', 'GHSA-9c84-4hx6-xmm4'} | 2021-12-09T06:34:59.076380Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c84-4hx6-xmm4', 'https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73'} | null | {'https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73'} | {'https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73'} |
PyPI | PYSEC-2016-15 | null | The utils.http.is_safe_url function in Django before 1.8.10 and 1.9.x before 1.9.3 allows remote attackers to redirect users to arbitrary web sites and conduct phishing attacks or possibly conduct cross-site scripting (XSS) attacks via a URL containing basic authentication, as demonstrated by http://mysite.example.com\@attacker.com. | {'CVE-2016-2512'} | 2021-07-15T02:22:10.137209Z | 2016-04-08T15:59:00Z | null | null | null | {'http://www.ubuntu.com/usn/USN-2915-2', 'http://rhn.redhat.com/errata/RHSA-2016-0504.html', 'http://www.debian.org/security/2016/dsa-3544', 'http://www.oracle.com/technetwork/topics/security/bulletinapr2016-2952098.html', 'https://github.com/django/django/commit/c5544d289233f501917e25970c03ed444abbd4f0', 'https://www.djangoproject.com/weblog/2016/mar/01/security-releases/', 'http://rhn.redhat.com/errata/RHSA-2016-0506.html', 'http://www.ubuntu.com/usn/USN-2915-3', 'http://rhn.redhat.com/errata/RHSA-2016-0502.html', 'http://rhn.redhat.com/errata/RHSA-2016-0505.html', 'http://www.ubuntu.com/usn/USN-2915-1', 'http://www.securityfocus.com/bid/83879', 'http://www.securitytracker.com/id/1035152'} | null | {'https://github.com/django/django/commit/c5544d289233f501917e25970c03ed444abbd4f0'} | {'https://github.com/django/django/commit/c5544d289233f501917e25970c03ed444abbd4f0'} |
PyPI | PYSEC-2021-179 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements. If input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`. 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-2021-29542', 'GHSA-4hrh-9vmp-2jgg'} | 2021-08-27T03:22:28.937409Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hrh-9vmp-2jgg'} | null | {'https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b'} | {'https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b'} |
PyPI | PYSEC-2021-483 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.FusedBatchNorm`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/828f346274841fa7505f7020e88ca36c22e557ab/tensorflow/core/kernels/fused_batch_norm_op.cc#L295-L297) performs a division based on the last dimension of the `x` tensor. Since this is controlled by the user, an attacker can trigger a denial of service. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29555', 'GHSA-r35g-4525-29fq'} | 2021-12-09T06:34:51.920437Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r35g-4525-29fq'} | null | {'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d'} | {'https://github.com/tensorflow/tensorflow/commit/1a2a87229d1d61e23a39373777c056161eb4084d'} |
PyPI | GHSA-663j-rjcr-789f | CSV injection in shuup | “Shuup” application in versions 0.4.2 to 2.10.8 is affected by the “Formula Injection” vulnerability. A customer can inject payloads in the name input field in the billing address while buying a product. When a store administrator accesses the reports page to export the data as an Excel file and opens it, the payload gets executed. | {'CVE-2021-25962'} | 2022-03-03T05:13:34.774281Z | 2021-09-30T20:50:07Z | HIGH | null | {'CWE-1236'} | {'https://github.com/shuup/shuup/commit/0a2db392e8518410c282412561461cd8797eea51', 'https://nvd.nist.gov/vuln/detail/CVE-2021-25962', 'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25962', 'https://github.com/shuup/shuup'} | null | {'https://github.com/shuup/shuup/commit/0a2db392e8518410c282412561461cd8797eea51'} | {'https://github.com/shuup/shuup/commit/0a2db392e8518410c282412561461cd8797eea51'} |
PyPI | PYSEC-2021-250 | null | TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-vvg4-vgrv-xfr7', 'CVE-2021-29613'} | 2021-08-27T03:22:41.522961Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vvg4-vgrv-xfr7', 'https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c'} | null | {'https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c', 'https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b'} | {'https://github.com/tensorflow/tensorflow/commit/4504a081af71514bb1828048363e6540f797005b', 'https://github.com/tensorflow/tensorflow/commit/14607c0707040d775e06b6817325640cb4b5864c'} |
PyPI | PYSEC-2021-600 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's [`GatherNd` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather_nd.cc#L124) does not support negative indices but there are no checks for this situation. Hence, an attacker can read arbitrary data from the heap by carefully crafting a model with negative values in `indices`. Similar issue exists in [`Gather` implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/gather.cc). We have patched the issue in GitHub commits bb6a0383ed553c286f87ca88c207f6774d5c4a8f and eb921122119a6b6e470ee98b89e65d721663179d. 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-jwf9-w5xm-f437', 'CVE-2021-37687'} | 2021-12-09T06:35:06.437026Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jwf9-w5xm-f437', 'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d'} | null | {'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d'} | {'https://github.com/tensorflow/tensorflow/commit/bb6a0383ed553c286f87ca88c207f6774d5c4a8f', 'https://github.com/tensorflow/tensorflow/commit/eb921122119a6b6e470ee98b89e65d721663179d'} |
PyPI | PYSEC-2020-315 | 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. Thus, the code sets up conditions to cause a heap buffer overflow. A `BatchedMap` is equivalent to a vector where each element is a hashmap. However, if the first element of `splits_values` is not 0, `batch_idx` will never be 1, hence there will be no hashmap at index 0 in `per_batch_counts`. Trying to access that in the user code results in a segmentation fault. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'CVE-2020-15200', 'GHSA-x7rp-74x2-mjf3'} | 2021-12-09T06:35:13.201827Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x7rp-74x2-mjf3', '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-2021-315 | null | nbgitpuller is a Jupyter server extension to sync a git repository one-way to a local path. Due to unsanitized input, visiting maliciously crafted links could result in arbitrary code execution in the user environment. This has been resolved in version 0.10.2 and all users are advised to upgrade. No work around exist for users who can not upgrade. | {'CVE-2021-39160', 'GHSA-mq5p-2mcr-m52j'} | 2021-08-30T18:40:30.558311Z | 2021-08-25T18:15:00Z | null | null | null | {'https://github.com/jupyterhub/nbgitpuller/commit/07690644f29a566011dd0d7ba14cae3eb0490481', 'https://github.com/jupyterhub/nbgitpuller/blob/main/CHANGELOG.md#0102---2021-08-25', 'https://github.com/jupyterhub/nbgitpuller/security/advisories/GHSA-mq5p-2mcr-m52j'} | null | {'https://github.com/jupyterhub/nbgitpuller/commit/07690644f29a566011dd0d7ba14cae3eb0490481'} | {'https://github.com/jupyterhub/nbgitpuller/commit/07690644f29a566011dd0d7ba14cae3eb0490481'} |
PyPI | PYSEC-2021-745 | null | TensorFlow is an end-to-end open source platform for machine learning. Passing invalid arguments (e.g., discovered via fuzzing) to `tf.raw_ops.SparseCountSparseOutput` results in segfault. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29619', 'GHSA-wvjw-p9f5-vq28'} | 2021-12-09T06:35:35.220537Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/82e6203221865de4008445b13c69b6826d2b28d9', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wvjw-p9f5-vq28'} | null | {'https://github.com/tensorflow/tensorflow/commit/82e6203221865de4008445b13c69b6826d2b28d9'} | {'https://github.com/tensorflow/tensorflow/commit/82e6203221865de4008445b13c69b6826d2b28d9'} |
PyPI | PYSEC-2021-732 | null | TensorFlow is an end-to-end open source platform for machine learning. A specially crafted TFLite model could trigger an OOB read on heap in the TFLite implementation of `Split_V`(https://github.com/tensorflow/tensorflow/blob/c59c37e7b2d563967da813fa50fe20b21f4da683/tensorflow/lite/kernels/split_v.cc#L99). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the `SizeOfDimension` function(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/kernel_util.h#L148-L150) will access data outside the bounds of the tensor shape array. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29606', 'GHSA-h4pc-gx2w-f2xv'} | 2021-12-09T06:35:33.046688Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h4pc-gx2w-f2xv', 'https://github.com/tensorflow/tensorflow/commit/ae2daeb45abfe2c6dda539cf8d0d6f653d3ef412'} | null | {'https://github.com/tensorflow/tensorflow/commit/ae2daeb45abfe2c6dda539cf8d0d6f653d3ef412'} | {'https://github.com/tensorflow/tensorflow/commit/ae2daeb45abfe2c6dda539cf8d0d6f653d3ef412'} |
PyPI | PYSEC-2021-698 | 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'} | 2021-12-09T06:35:27.168452Z | 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 | GHSA-743r-5g92-5vgf | Improper certificate management in AWS IoT Device SDK v2 | Connections initialized by the AWS IoT Device SDK v2 for Java (versions prior to 1.4.2), Python (versions prior to 1.6.1), C++ (versions prior to 1.12.7) and Node.js (versions prior to 1.5.3) did not verify server certificate hostname during TLS handshake when overriding Certificate Authorities (CA) in their trust stores on MacOS. This issue has been addressed in aws-c-io submodule versions 0.10.5 onward. This issue affects: Amazon Web Services AWS IoT Device SDK v2 for Java versions prior to 1.4.2 on macOS. Amazon Web Services AWS IoT Device SDK v2 for Python versions prior to 1.6.1 on macOS. Amazon Web Services AWS IoT Device SDK v2 for C++ versions prior to 1.12.7 on macOS. Amazon Web Services AWS IoT Device SDK v2 for Node.js versions prior to 1.5.3 on macOS. Amazon Web Services AWS-C-IO 0.10.4 on macOS. | {'CVE-2021-40829'} | 2022-03-03T05:13:12.773300Z | 2021-11-24T21:11:16Z | MODERATE | null | {'CWE-295'} | {'https://github.com/aws/aws-iot-device-sdk-cpp-v2', 'https://nvd.nist.gov/vuln/detail/CVE-2021-40829', 'https://github.com/aws/aws-iot-device-sdk-js-v2', 'https://github.com/aws/aws-iot-device-sdk-java-v2/commits/v1.4.2', 'https://github.com/aws/aws-iot-device-sdk-java-v2', 'https://github.com/aws/aws-iot-device-sdk-python-v2', 'https://github.com/awslabs/aws-c-io/'} | null | {'https://github.com/aws/aws-iot-device-sdk-java-v2/commits/v1.4.2'} | {'https://github.com/aws/aws-iot-device-sdk-java-v2/commits/v1.4.2'} |
PyPI | PYSEC-2021-677 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixTriangularSolve`(https://github.com/tensorflow/tensorflow/blob/8cae746d8449c7dda5298327353d68613f16e798/tensorflow/core/kernels/linalg/matrix_triangular_solve_op_impl.h#L160-L240) fails to terminate kernel execution if one validation condition fails. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29551', 'GHSA-vqw6-72r7-fgw7'} | 2021-12-09T06:35:23.626707Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/480641e3599775a8895254ffbc0fc45621334f68', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vqw6-72r7-fgw7'} | null | {'https://github.com/tensorflow/tensorflow/commit/480641e3599775a8895254ffbc0fc45621334f68'} | {'https://github.com/tensorflow/tensorflow/commit/480641e3599775a8895254ffbc0fc45621334f68'} |
PyPI | GHSA-52q8-877j-gghq | remote code execution via cache action in MoinMoin | ### Impact
The cache action in action/cache.py allows directory traversal through a crafted HTTP request. An attacker who can upload attachments to
the wiki can use this to achieve remote code execution.
### Patches
Users are strongly advised to upgrade to a patched version.
MoinMoin Wiki 1.9.11 has the necessary fixes and also contains other important fixes.
### Workarounds
It is not advised to work around this, but to upgrade MoinMoin to a patched version.
That said, a work around via disabling the `cache` or the `AttachFile` action might be possible.
Also, it is of course helpful if you give `write` permissions (which include uploading attachments) only to trusted users.
### Credits
This vulnerability was discovered by Michael Chapman.
### For more information
If you have any questions or comments about this advisory, email me at [twaldmann@thinkmo.de](mailto:twaldmann@thinkmo.de). | {'CVE-2020-25074'} | 2022-03-22T21:01:58.312411Z | 2020-11-11T15:54:51Z | CRITICAL | null | {'CWE-22'} | {'https://www.debian.org/security/2020/dsa-4787', 'https://lists.debian.org/debian-lts-announce/2020/11/msg00020.html', 'http://moinmo.in/SecurityFixes', 'https://github.com/moinwiki/moin-1.9/commit/6b96a9060069302996b5af47fd4a388fc80172b7', 'https://nvd.nist.gov/vuln/detail/CVE-2020-25074', 'https://pypi.org/project/moin/', 'https://github.com/moinwiki/moin-1.9/security/advisories/GHSA-52q8-877j-gghq'} | null | {'https://github.com/moinwiki/moin-1.9/commit/6b96a9060069302996b5af47fd4a388fc80172b7'} | {'https://github.com/moinwiki/moin-1.9/commit/6b96a9060069302996b5af47fd4a388fc80172b7'} |
PyPI | PYSEC-2021-227 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementations of the `Minimum` and `Maximum` TFLite operators can be used to read data outside of bounds of heap allocated objects, if any of the two input tensor arguments are empty. This is because the broadcasting implementation(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/maximum_minimum.h#L52-L56) indexes in both tensors with the same index but does not validate that the index is within 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-24x6-8c7m-hv3f', 'CVE-2021-29590'} | 2021-08-27T03:22:37.400702Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/953f28dca13c92839ba389c055587cfe6c723578', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-24x6-8c7m-hv3f'} | null | {'https://github.com/tensorflow/tensorflow/commit/953f28dca13c92839ba389c055587cfe6c723578'} | {'https://github.com/tensorflow/tensorflow/commit/953f28dca13c92839ba389c055587cfe6c723578'} |
PyPI | GHSA-cjc7-49v2-jp64 | Incomplete validation in `SparseAdd` | ### Impact
Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data:
```python
import tensorflow as tf
a_indices = tf.zeros([10, 97], dtype=tf.int64)
a_values = tf.zeros([10], dtype=tf.int64)
a_shape = tf.zeros([0], dtype=tf.int64)
b_indices = tf.zeros([0, 0], dtype=tf.int64)
b_values = tf.zeros([0], dtype=tf.int64)
b_shape = tf.zeros([0], dtype=tf.int64)
thresh = 0
tf.raw_ops.SparseAdd(a_indices=a_indices,
a_values=a_values,
a_shape=a_shape,
b_indices=b_indices,
b_values=b_values,
b_shape=b_shape,
thresh=thresh)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_add_op.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation.
### Patches
We have patched the issue in GitHub commit [6fd02f44810754ae7481838b6a67c5df7f909ca3](https://github.com/tensorflow/tensorflow/commit/6fd02f44810754ae7481838b6a67c5df7f909ca3) followed by GitHub commit [41727ff06111117bdf86b37db198217fd7a143cc](https://github.com/tensorflow/tensorflow/commit/41727ff06111117bdf86b37db198217fd7a143cc).
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-2021-29609'} | 2022-03-03T05:14:06.533877Z | 2021-05-21T14:28:29Z | MODERATE | null | {'CWE-787', 'CWE-665'} | {'https://github.com/tensorflow/tensorflow/commit/41727ff06111117bdf86b37db198217fd7a143cc', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29609', 'https://github.com/tensorflow/tensorflow/commit/6fd02f44810754ae7481838b6a67c5df7f909ca3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cjc7-49v2-jp64'} | null | {'https://github.com/tensorflow/tensorflow/commit/41727ff06111117bdf86b37db198217fd7a143cc', 'https://github.com/tensorflow/tensorflow/commit/6fd02f44810754ae7481838b6a67c5df7f909ca3'} | {'https://github.com/tensorflow/tensorflow/commit/41727ff06111117bdf86b37db198217fd7a143cc', 'https://github.com/tensorflow/tensorflow/commit/6fd02f44810754ae7481838b6a67c5df7f909ca3'} |
PyPI | PYSEC-2022-107 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `ReverseSequence` does not fully validate the value of `batch_dim` and can result in a heap OOB read. There is a check to make sure the value of `batch_dim` does not go over the rank of the input, but there is no check for negative values. Negative dimensions are allowed in some cases to mimic Python's negative indexing (i.e., indexing from the end of the array), however if the value is too negative then the implementation of `Dim` would access elements before the start of an array. 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-6gmv-pjp9-p8w8', 'CVE-2022-21728'} | 2022-03-09T00:18:23.406972Z | 2022-02-03T11:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/37c01fb5e25c3d80213060460196406c43d31995', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L1636-L1671', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gmv-pjp9-p8w8', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/shape_inference.h#L415-L428'} | null | {'https://github.com/tensorflow/tensorflow/commit/37c01fb5e25c3d80213060460196406c43d31995'} | {'https://github.com/tensorflow/tensorflow/commit/37c01fb5e25c3d80213060460196406c43d31995'} |
PyPI | GHSA-63xm-rx5p-xvqr | Heap buffer overflow in Tensorflow | ### Impact
The implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L263-L269
It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in a heap buffer overflow.
### Patches
We have patched the issue in 390611e0d45c5793c7066110af37c8514e6a6c54 and will release a patch release for all affected versions.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2020-15195'} | 2022-03-03T05:13:19.626280Z | 2020-09-25T18:28:29Z | HIGH | null | {'CWE-787', 'CWE-119', 'CWE-122'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15195', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr', 'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54'} | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54'} |
PyPI | GHSA-hpv4-7p9c-mvfr | Heap buffer overflow in `FractionalAvgPoolGrad` | ### Impact
The implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers:
```python
import tensorflow as tf
tf.raw_ops.FractionalAvgPoolGrad(
orig_input_tensor_shape=[0,1,2,3],
out_backprop = np.array([[[[541],[541]],[[541],[541]]]]),
row_pooling_sequence=[0, 0, 0, 0, 0],
col_pooling_sequence=[-2, 0, 0, 2, 0],
overlapping=True)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area.
### Patches
We have patched the issue in GitHub commit [0f931751fb20f565c4e94aa6df58d54a003cdb30](https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30).
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-2021-37651'} | 2022-03-03T05:13:17.766274Z | 2021-08-25T14:43:21Z | HIGH | null | {'CWE-787', 'CWE-125'} | {'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37651'} | null | {'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30'} | {'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30'} |
PyPI | PYSEC-2022-28 | null | Insecure Temporary File in GitHub repository mlflow/mlflow prior to 1.23.1. | {'CVE-2022-0736', 'GHSA-vqj2-4v8m-8vrq'} | 2022-03-02T06:39:30.836439Z | 2022-02-23T09:15:00Z | null | null | null | {'https://github.com/mlflow/mlflow/commit/61984e6843d2e59235d82a580c529920cd8f3711', 'https://github.com/advisories/GHSA-vqj2-4v8m-8vrq', 'https://huntr.dev/bounties/e5384764-c583-4dec-a1d8-4697f4e12f75'} | null | {'https://github.com/mlflow/mlflow/commit/61984e6843d2e59235d82a580c529920cd8f3711'} | {'https://github.com/mlflow/mlflow/commit/61984e6843d2e59235d82a580c529920cd8f3711'} |
PyPI | PYSEC-2020-335 | null | In TensorFlow release candidate versions 2.4.0rc*, the general implementation for matching filesystem paths to globbing pattern is vulnerable to an access out of bounds of the array holding the directories. There are multiple invariants and preconditions that are assumed by the parallel implementation of GetMatchingPaths but are not verified by the PRs introducing it (#40861 and #44310). Thus, we are completely rewriting the implementation to fully specify and validate these. This is patched in version 2.4.0. This issue only impacts master branch and the release candidates for TF version 2.4. The final release of the 2.4 release will be patched. | {'GHSA-9jjw-hf72-3mxw', 'CVE-2020-26269'} | 2020-12-14T17:42:00Z | 2020-12-10T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8b5b9dc96666a3a5d27fad7179ff215e3b74b67c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9jjw-hf72-3mxw'} | null | {'https://github.com/tensorflow/tensorflow/commit/8b5b9dc96666a3a5d27fad7179ff215e3b74b67c'} | {'https://github.com/tensorflow/tensorflow/commit/8b5b9dc96666a3a5d27fad7179ff215e3b74b67c'} |
PyPI | PYSEC-2015-25 | null | The editor in IPython Notebook before 3.2.2 and Jupyter Notebook 4.0.x before 4.0.5 allows remote attackers to execute arbitrary JavaScript code via a crafted file, which triggers a redirect to files/, related to MIME types. | {'CVE-2015-7337'} | 2021-07-15T02:22:14.948088Z | 2015-09-29T19:59:00Z | null | null | null | {'http://seclists.org/oss-sec/2015/q3/558', 'http://seclists.org/oss-sec/2015/q3/634', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-September/167670.html', 'https://github.com/ipython/ipython/commit/0a8096adf165e2465550bd5893d7e352544e5967', 'https://bugzilla.redhat.com/show_bug.cgi?id=1264067', 'https://github.com/jupyter/notebook/commit/9e63dd89b603dfbe3a7e774d8a962ee0fa30c0b5', 'https://security.gentoo.org/glsa/201512-02'} | null | {'https://github.com/jupyter/notebook/commit/9e63dd89b603dfbe3a7e774d8a962ee0fa30c0b5', 'https://github.com/ipython/ipython/commit/0a8096adf165e2465550bd5893d7e352544e5967'} | {'https://github.com/ipython/ipython/commit/0a8096adf165e2465550bd5893d7e352544e5967', 'https://github.com/jupyter/notebook/commit/9e63dd89b603dfbe3a7e774d8a962ee0fa30c0b5'} |
PyPI | PYSEC-2017-18 | null | Cross-site scripting (XSS) vulnerability in the _keyify function in mistune.py in Mistune before 0.8.1 allows remote attackers to inject arbitrary web script or HTML by leveraging failure to escape the "key" argument. | {'CVE-2017-16876', 'GHSA-98gj-wwxm-cj3h'} | 2021-07-05T00:01:22.732782Z | 2017-12-29T15:29:00Z | null | null | null | {'https://github.com/advisories/GHSA-98gj-wwxm-cj3h', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/NUR3GMHQBMA3UC4PFMCK6GCLOQC4LQQC/', 'https://bugzilla.redhat.com/show_bug.cgi?id=1524596', 'https://github.com/lepture/mistune/blob/master/CHANGES.rst', 'https://github.com/lepture/mistune/commit/5f06d724bc05580e7f203db2d4a4905fc1127f98'} | null | {'https://github.com/lepture/mistune/commit/5f06d724bc05580e7f203db2d4a4905fc1127f98'} | {'https://github.com/lepture/mistune/commit/5f06d724bc05580e7f203db2d4a4905fc1127f98'} |
PyPI | PYSEC-2021-335 | null | The module `AccessControl` defines security policies for Python code used in restricted code within Zope applications. Restricted code is any code that resides in Zope's object database, such as the contents of `Script (Python)` objects. The policies defined in `AccessControl` severely restrict access to Python modules and only exempt a few that are deemed safe, such as Python's `string` module. However, full access to the `string` module also allows access to the class `Formatter`, which can be overridden and extended within `Script (Python)` in a way that provides access to other unsafe Python libraries. Those unsafe Python libraries can be used for remote code execution. By default, you need to have the admin-level Zope "Manager" role to add or edit `Script (Python)` objects through the web. Only sites that allow untrusted users to add/edit these scripts through the web - which would be a very unusual configuration to begin with - are at risk. The problem has been fixed in AccessControl 4.3 and 5.2. Only AccessControl versions 4 and 5 are vulnerable, and only on Python 3, not Python 2.7. As a workaround, a site administrator can restrict adding/editing `Script (Python)` objects through the web using the standard Zope user/role permission mechanisms. Untrusted users should not be assigned the Zope Manager role and adding/editing these scripts through the web should be restricted to trusted users only. This is the default configuration in Zope. | {'CVE-2021-32807', 'GHSA-qcx9-j53g-ccgf'} | 2021-09-26T23:32:08.989778Z | 2021-07-30T22:15:00Z | null | null | null | {'https://github.com/zopefoundation/AccessControl/blob/master/CHANGES.rst#51-2021-07-30', 'https://github.com/zopefoundation/AccessControl/commit/b42dd4badf803bb9fb71ac34cd9cb0c249262f2c', 'https://github.com/zopefoundation/AccessControl/security/advisories/GHSA-qcx9-j53g-ccgf'} | null | {'https://github.com/zopefoundation/AccessControl/commit/b42dd4badf803bb9fb71ac34cd9cb0c249262f2c'} | {'https://github.com/zopefoundation/AccessControl/commit/b42dd4badf803bb9fb71ac34cd9cb0c249262f2c'} |
PyPI | PYSEC-2021-765 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a crash via a `CHECK`-fail in debug builds of TensorFlow using `tf.raw_ops.ResourceGather` or a read from outside the bounds of heap allocated data in the same API in a release build. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L660-L668) does not check that the `batch_dims` value that the user supplies is less than the rank of the input tensor. Since the implementation uses several for loops over the dimensions of `tensor`, this results in reading data from outside the bounds of heap allocated buffer backing the tensor. We have patched the issue in GitHub commit bc9c546ce7015c57c2f15c168b3d9201de679a1d. 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-2021-37654', 'GHSA-2r8p-fg3c-wcj4'} | 2021-12-09T06:35:36.998638Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2r8p-fg3c-wcj4', 'https://github.com/tensorflow/tensorflow/commit/bc9c546ce7015c57c2f15c168b3d9201de679a1d'} | null | {'https://github.com/tensorflow/tensorflow/commit/bc9c546ce7015c57c2f15c168b3d9201de679a1d'} | {'https://github.com/tensorflow/tensorflow/commit/bc9c546ce7015c57c2f15c168b3d9201de679a1d'} |
PyPI | PYSEC-2021-270 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the code for `tf.raw_ops.SaveV2` does not properly validate the inputs and an attacker can trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/save_restore_v2_ops.cc) uses `ValidateInputs` to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above. However, the validation uses `OP_REQUIRES` which translates to setting the `Status` object of the current `OpKernelContext` to an error status, followed by an empty `return` statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the next line in `Compute` that follows the call to `ValidateInputs`. This is equivalent to lacking the validation. We have patched the issue in GitHub commit 9728c60e136912a12d99ca56e106b7cce7af5986. 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-2021-37648', 'GHSA-wp77-4gmm-7cq8'} | 2021-08-27T03:22:43.792593Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wp77-4gmm-7cq8', 'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} | null | {'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} | {'https://github.com/tensorflow/tensorflow/commit/9728c60e136912a12d99ca56e106b7cce7af5986'} |
PyPI | PYSEC-2021-620 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `QuantizeV2` can trigger a read outside of bounds of heap allocated array. This occurs whenever `axis` is a negative value less than `-1`. In this case, we are accessing data before the start of a heap buffer. The code allows `axis` to be an optional argument (`s` would contain an `error::NOT_FOUND` error code). Otherwise, it assumes that `axis` is a valid index into the dimensions of the `input` tensor. If `axis` is less than `-1` then this results in a heap OOB read. 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. | {'CVE-2021-41211', 'GHSA-cvgx-3v3q-m36c'} | 2021-12-09T06:35:09.057312Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvgx-3v3q-m36c'} | null | {'https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244'} | {'https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244'} |
PyPI | PYSEC-2020-270 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the `tf.raw_ops.Switch` operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output. Since only one of the tensors is defined, the other one is `nullptr`, hence we are binding a reference to `nullptr`. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. In this case, this results in a segmentation fault The issue is patched in commit da8558533d925694483d2c136a9220d6d49d843c, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15190', 'GHSA-4g9f-63rx-5cw4'} | 2021-12-09T06:34:40.732914Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/da8558533d925694483d2c136a9220d6d49d843c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4g9f-63rx-5cw4', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/da8558533d925694483d2c136a9220d6d49d843c'} | {'https://github.com/tensorflow/tensorflow/commit/da8558533d925694483d2c136a9220d6d49d843c'} |
PyPI | PYSEC-2021-509 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.CTCBeamSearchDecoder`, an attacker can trigger denial of service via segmentation faults. The implementation(https://github.com/tensorflow/tensorflow/blob/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7/tensorflow/core/kernels/ctc_decoder_ops.cc#L68-L79) fails to detect cases when the input tensor is empty and proceeds to read data from a null buffer. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29581', 'GHSA-vq2r-5xvm-3hc3'} | 2021-12-09T06:34:55.926686Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vq2r-5xvm-3hc3', 'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} | null | {'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} | {'https://github.com/tensorflow/tensorflow/commit/b1b323042264740c398140da32e93fb9c2c9f33e'} |
PyPI | PYSEC-2021-159 | null | TensorFlow is an end-to-end open source platform for machine learning. The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450) does not check that the divisor used in computing the shard size is not zero. Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29522', 'GHSA-c968-pq7h-7fxv'} | 2021-08-27T03:22:25.206676Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c968-pq7h-7fxv'} | null | {'https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa'} | {'https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa'} |
PyPI | GHSA-wf5p-c75w-w3wh | Null pointer dereference in TFLite MLIR optimizations | ### Impact
An attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service:
This is caused by the MLIR optimization of `L2NormalizeReduceAxis` operator. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements:
```cc
bool L2NormalizeReduceAxis(Value sq_op, DenseElementsAttr axis) {
if (sq_op.getType().cast<ShapedType>().getRank() - 1 ==
*axis.getValues<int>().begin() ||
*axis.getValues<int>().begin() == -1) {
// ...
}
// ...
}
```
### Patches
We have patched the issue in GitHub commit [d6b57f461b39fd1aa8c1b870f1b974aac3554955](https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang of Baidu Security. | {'CVE-2021-37689'} | 2021-08-24T17:58:35Z | 2021-08-25T14:39:36Z | HIGH | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37689', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wf5p-c75w-w3wh', 'https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955'} | null | {'https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955'} | {'https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955'} |
PyPI | GHSA-c2vx-49jm-h3f6 | Pysaml2 does not sanitize XML responses | XML External Entity (XXE) vulnerability in PySAML2 4.4.0 and earlier allows remote attackers to read arbitrary files via a crafted SAML XML request or response. | {'CVE-2016-10149'} | 2022-04-26T18:32:58.537426Z | 2018-07-16T16:50:12Z | HIGH | null | {'CWE-611'} | {'https://access.redhat.com/errata/RHSA-2017:0937', 'https://github.com/rohe/pysaml2/pull/379', 'https://github.com/advisories/GHSA-c2vx-49jm-h3f6', 'https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=850716', 'https://access.redhat.com/errata/RHSA-2017:0936', 'http://www.openwall.com/lists/oss-security/2017/01/19/5', 'http://www.securityfocus.com/bid/97692', 'https://github.com/rohe/pysaml2/issues/366', 'https://github.com/rohe/pysaml2', 'https://github.com/rohe/pysaml2/commit/6e09a25d9b4b7aa7a506853210a9a14100b8bc9b', 'https://nvd.nist.gov/vuln/detail/CVE-2016-10149', 'https://access.redhat.com/errata/RHSA-2017:0938', 'http://www.debian.org/security/2017/dsa-3759'} | null | {'https://github.com/rohe/pysaml2/commit/6e09a25d9b4b7aa7a506853210a9a14100b8bc9b'} | {'https://github.com/rohe/pysaml2/commit/6e09a25d9b4b7aa7a506853210a9a14100b8bc9b'} |
PyPI | PYSEC-2022-90 | null | Tensorflow is an Open Source Machine Learning Framework. The Grappler optimizer in TensorFlow can be used to cause a denial of service by altering a `SavedModel` such that `IsSimplifiableReshape` would trigger `CHECK` failures. 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-2022-23581', 'GHSA-fq86-3f29-px2c'} | 2022-03-09T00:17:35.012769Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1fb27733f943295d874417630edd3b38b34ce082', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq86-3f29-px2c', 'https://github.com/tensorflow/tensorflow/commit/240655511cd3e701155f944a972db71b6c0b1bb6', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/constant_folding.cc#L1687-L1742', 'https://github.com/tensorflow/tensorflow/commit/ebc1a2ffe5a7573d905e99bd0ee3568ee07c12c1'} | null | {'https://github.com/tensorflow/tensorflow/commit/1fb27733f943295d874417630edd3b38b34ce082', 'https://github.com/tensorflow/tensorflow/commit/240655511cd3e701155f944a972db71b6c0b1bb6', 'https://github.com/tensorflow/tensorflow/commit/ebc1a2ffe5a7573d905e99bd0ee3568ee07c12c1'} | {'https://github.com/tensorflow/tensorflow/commit/240655511cd3e701155f944a972db71b6c0b1bb6', 'https://github.com/tensorflow/tensorflow/commit/ebc1a2ffe5a7573d905e99bd0ee3568ee07c12c1', 'https://github.com/tensorflow/tensorflow/commit/1fb27733f943295d874417630edd3b38b34ce082'} |
PyPI | GHSA-p49h-hjvm-jg3h | PCX P mode buffer overflow in Pillow | libImaging/PcxDecode.c in Pillow before 6.2.2 has a PCX P mode buffer overflow. | {'CVE-2020-5312'} | 2022-03-03T05:13:31.313661Z | 2021-11-03T18:05:04Z | HIGH | null | {'CWE-120'} | {'https://github.com/pypa/advisory-db/blob/7872b0a91b4d980f749e6d75a81f8cc1af32829f/vulns/pillow/PYSEC-2020-83.yaml', 'https://github.com/python-pillow/Pillow', 'https://www.debian.org/security/2020/dsa-4631', 'https://access.redhat.com/errata/RHSA-2020:0578', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2MMU3WT2X64GS5WHDPKKC2WZA7UIIQ3A/', 'https://github.com/python-pillow/Pillow/commit/93b22b846e0269ee9594ff71a72bec02d2bea8fd', 'https://access.redhat.com/errata/RHSA-2020:0580', 'https://access.redhat.com/errata/RHSA-2020:0566', 'https://nvd.nist.gov/vuln/detail/CVE-2020-5312', 'https://access.redhat.com/errata/RHSA-2020:0683', 'https://access.redhat.com/errata/RHSA-2020:0694', 'https://usn.ubuntu.com/4272-1/', 'https://pillow.readthedocs.io/en/stable/releasenotes/6.2.2.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/3DUMIBUYGJRAVJCTFUWBRLVQKOUTVX5P/', 'https://access.redhat.com/errata/RHSA-2020:0681'} | null | {'https://github.com/python-pillow/Pillow/commit/93b22b846e0269ee9594ff71a72bec02d2bea8fd'} | {'https://github.com/python-pillow/Pillow/commit/93b22b846e0269ee9594ff71a72bec02d2bea8fd'} |
PyPI | PYSEC-2020-159 | null | In Alerta before version 8.1.0, users may be able to bypass LDAP authentication if they provide an empty password when Alerta server is configure to use LDAP as the authorization provider. Only deployments where LDAP servers are configured to allow unauthenticated authentication mechanism for anonymous authorization are affected. A fix has been implemented in version 8.1.0 that returns HTTP 401 Unauthorized response for any authentication attempts where the password field is empty. As a workaround LDAP administrators can disallow unauthenticated bind requests by clients. | {'CVE-2020-26214', 'GHSA-5hmm-x8q8-w5jh'} | 2020-11-17T21:08:00Z | 2020-11-06T18:15:00Z | null | null | null | {'https://github.com/alerta/alerta/security/advisories/GHSA-5hmm-x8q8-w5jh', 'https://github.com/alerta/alerta/issues/1277', 'https://github.com/alerta/alerta/commit/2bfa31779a4c9df2fa68fa4d0c5c909698c5ef65', 'https://github.com/alerta/alerta/pull/1345', 'https://pypi.org/project/alerta-server/8.1.0/', 'https://tools.ietf.org/html/rfc4513#section-5.1.2'} | null | {'https://github.com/alerta/alerta/commit/2bfa31779a4c9df2fa68fa4d0c5c909698c5ef65'} | {'https://github.com/alerta/alerta/commit/2bfa31779a4c9df2fa68fa4d0c5c909698c5ef65'} |
PyPI | PYSEC-2020-42 | null | An issue was discovered in fastecdsa before 2.1.2. When using the NIST P-256 curve in the ECDSA implementation, the point at infinity is mishandled. This means that for an extreme value in k and s^-1, the signature verification fails even if the signature is correct. This behavior is not solely a usability problem. There are some threat models where an attacker can benefit by successfully guessing users for whom signature verification will fail. | {'CVE-2020-12607', 'GHSA-56wv-2wr9-3h9r'} | 2020-06-03T13:47:00Z | 2020-06-02T21:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-56wv-2wr9-3h9r', 'https://github.com/AntonKueltz/fastecdsa/commit/e592f106edd5acf6dacedfab2ad16fe6c735c9d1', 'https://github.com/AntonKueltz/fastecdsa/issues/52', 'https://github.com/AntonKueltz/fastecdsa/commit/4a16daeaf139be20654ef58a9fe4c79dc030458c', 'https://github.com/AntonKueltz/fastecdsa/commit/7b64e3efaa806b4daaf73bb5172af3581812f8de'} | null | {'https://github.com/AntonKueltz/fastecdsa/commit/7b64e3efaa806b4daaf73bb5172af3581812f8de', 'https://github.com/AntonKueltz/fastecdsa/commit/4a16daeaf139be20654ef58a9fe4c79dc030458c', 'https://github.com/AntonKueltz/fastecdsa/commit/e592f106edd5acf6dacedfab2ad16fe6c735c9d1'} | {'https://github.com/AntonKueltz/fastecdsa/commit/e592f106edd5acf6dacedfab2ad16fe6c735c9d1', 'https://github.com/AntonKueltz/fastecdsa/commit/7b64e3efaa806b4daaf73bb5172af3581812f8de', 'https://github.com/AntonKueltz/fastecdsa/commit/4a16daeaf139be20654ef58a9fe4c79dc030458c'} |
PyPI | PYSEC-2022-150 | null | Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that assertions in `function.cc` would be falsified and crash the Python interpreter. 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-2022-23586', 'GHSA-43jf-985q-588j'} | 2022-03-09T00:18:29.301352Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/dcc21c7bc972b10b6fb95c2fb0f4ab5a59680ec2', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/function.cc', 'https://github.com/tensorflow/tensorflow/commit/3d89911481ba6ebe8c88c1c0b595412121e6c645', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-43jf-985q-588j'} | null | {'https://github.com/tensorflow/tensorflow/commit/3d89911481ba6ebe8c88c1c0b595412121e6c645', 'https://github.com/tensorflow/tensorflow/commit/dcc21c7bc972b10b6fb95c2fb0f4ab5a59680ec2'} | {'https://github.com/tensorflow/tensorflow/commit/3d89911481ba6ebe8c88c1c0b595412121e6c645', 'https://github.com/tensorflow/tensorflow/commit/dcc21c7bc972b10b6fb95c2fb0f4ab5a59680ec2'} |
PyPI | GHSA-2828-9vh6-9m6j | Client Denial of Service on TUF | ### Impact
An attacker who can gain file access to the repository and modify metadata files may cause a denial of service to clients by creating many invalid signatures on a metadata file. Having a large number of signatures to verify will delay the moment when the client will determine the signature is not valid. This delay may be for at least a few minutes, but possibly could be longer especially if multiple files are impacted.
The tuf maintainers would like to thank Erik MacLean of Analog Devices, Inc. for reporting this issue.
### Patches
No fix exists for this issue.
### Workarounds
No workarounds are known for this issue.
### References
* [CVE-2020-6173](https://nvd.nist.gov/vuln/detail/CVE-2020-6173)
* [Issue #973](https://github.com/theupdateframework/tuf/issues/973) | {'CVE-2020-6173'} | 2022-03-03T05:12:38.507755Z | 2020-08-21T16:25:48Z | MODERATE | null | {'CWE-400'} | {'https://github.com/theupdateframework/tuf/security/advisories/GHSA-2828-9vh6-9m6j', 'https://nvd.nist.gov/vuln/detail/CVE-2020-6173', 'https://github.com/theupdateframework/tuf', 'https://github.com/theupdateframework/tuf/issues/973', 'https://github.com/theupdateframework/tuf/commits/develop'} | null | {'https://github.com/theupdateframework/tuf/commits/develop'} | {'https://github.com/theupdateframework/tuf/commits/develop'} |
PyPI | PYSEC-2021-773 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can generate undefined behavior via a reference binding to nullptr in `BoostedTreesCalculateBestGainsPerFeature` and similar attack can occur in `BoostedTreesCalculateBestFeatureSplitV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) does not validate the input values. We have patched the issue in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and in commit 429f009d2b2c09028647dd4bb7b3f6f414bbaad7. 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-2021-37662', 'GHSA-f5cx-5wr3-5qrc'} | 2021-12-09T06:35:37.708091Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad', 'https://github.com/tensorflow/tensorflow/commit/429f009d2b2c09028647dd4bb7b3f6f414bbaad7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f5cx-5wr3-5qrc'} | null | {'https://github.com/tensorflow/tensorflow/commit/429f009d2b2c09028647dd4bb7b3f6f414bbaad7', 'https://github.com/tensorflow/tensorflow/commit/9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad'} | {'https://github.com/tensorflow/tensorflow/commit/429f009d2b2c09028647dd4bb7b3f6f414bbaad7', 'https://github.com/tensorflow/tensorflow/commit/9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad'} |
PyPI | GHSA-g57j-q48p-9vm2 | Command injection in Gerapy | This affects the package Gerapy from 0 and before 0.9.3. The input being passed to Popen, via the project_configure endpoint, isn’t being sanitized. | {'CVE-2020-7698'} | 2022-03-21T23:01:55.945709Z | 2021-05-06T18:52:13Z | CRITICAL | null | {'CWE-74'} | {'https://github.com/Gerapy/Gerapy/commit/e8446605eb2424717418eae199ec7aad573da2d2', 'https://github.com/Gerapy/Gerapy', 'https://snyk.io/vuln/SNYK-PYTHON-GERAPY-572470', 'https://nvd.nist.gov/vuln/detail/CVE-2020-7698'} | null | {'https://github.com/Gerapy/Gerapy/commit/e8446605eb2424717418eae199ec7aad573da2d2'} | {'https://github.com/Gerapy/Gerapy/commit/e8446605eb2424717418eae199ec7aad573da2d2'} |
PyPI | PYSEC-2021-289 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.UnicodeEncode`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/unicode_ops.cc#L533-L539) reads the first dimension of the `input_splits` tensor before validating that this tensor is not empty. We have patched the issue in GitHub commit 2e0ee46f1a47675152d3d865797a18358881d7a6. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37667', 'GHSA-w74j-v8xh-3w5h'} | 2021-08-27T03:22:45.582995Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/2e0ee46f1a47675152d3d865797a18358881d7a6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w74j-v8xh-3w5h'} | null | {'https://github.com/tensorflow/tensorflow/commit/2e0ee46f1a47675152d3d865797a18358881d7a6'} | {'https://github.com/tensorflow/tensorflow/commit/2e0ee46f1a47675152d3d865797a18358881d7a6'} |
PyPI | PYSEC-2021-323 | null | Products.isurlinportal is a replacement for isURLInPortal method in Plone. Versions of Products.isurlinportal prior to 1.2.0 have an Open Redirect vulnerability. Various parts of Plone use the 'is url in portal' check for security, mostly to see if it is safe to redirect to a url. A url like `https://example.org` is not in the portal. The url `https:example.org` without slashes is considered to be in the portal. When redirecting, some browsers go to `https://example.org`, others give an error. Attackers may use this to redirect victims to their site, especially as part of a phishing attack. The problem has been patched in Products.isurlinportal 1.2.0. | {'GHSA-q3m9-9fj2-mfwr', 'CVE-2021-32806'} | 2021-09-20T14:26:43.785985Z | 2021-08-02T19:15:00Z | null | null | null | {'https://github.com/plone/Products.isurlinportal/security/advisories/GHSA-q3m9-9fj2-mfwr', 'http://jvn.jp/en/jp/JVN50804280/index.html', 'https://github.com/plone/Products.isurlinportal/commit/d4fd34990d18adf05a10dc5e2bb4b066798280ba'} | null | {'https://github.com/plone/Products.isurlinportal/commit/d4fd34990d18adf05a10dc5e2bb4b066798280ba'} | {'https://github.com/plone/Products.isurlinportal/commit/d4fd34990d18adf05a10dc5e2bb4b066798280ba'} |
PyPI | PYSEC-2021-96 | null | This affects the package Flask-Unchained before 0.9.0. When using the the _validate_redirect_url function, it is possible to bypass URL validation and redirect a user to an arbitrary URL by providing multiple back slashes such as \\\evil.com/path. This vulnerability is only exploitable if an alternative WSGI server other than Werkzeug is used, or the default behaviour of Werkzeug is modified using 'autocorrect_location_header=False. | {'GHSA-pjc4-3w99-j7v4', 'SNYK-PYTHON-FLASKUNCHAINED-1293189', 'CVE-2021-23393'} | 2021-06-15T05:47:49.199835Z | 2021-06-11T00:15:00Z | null | null | null | {'https://github.com/briancappello/flask-unchained/commit/71e36b28166f9ffbe0a991f51127f0984f7e6a40', 'https://snyk.io/vuln/SNYK-PYTHON-FLASKUNCHAINED-1293189', 'https://github.com/advisories/GHSA-pjc4-3w99-j7v4'} | null | {'https://github.com/briancappello/flask-unchained/commit/71e36b28166f9ffbe0a991f51127f0984f7e6a40'} | {'https://github.com/briancappello/flask-unchained/commit/71e36b28166f9ffbe0a991f51127f0984f7e6a40'} |
PyPI | PYSEC-2020-289 | null | In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one. The runtime assumes that these buffers are written to before a possible read, hence they are initialized with `nullptr`. However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference. The issue is patched in commit 0b5662bc, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-qh32-6jjc-qprm', 'CVE-2020-15209'} | 2021-12-09T06:34:43.227280Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/0b5662bc2be13a8c8f044d925d87fb6e56247cd8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qh32-6jjc-qprm', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/0b5662bc2be13a8c8f044d925d87fb6e56247cd8'} | {'https://github.com/tensorflow/tensorflow/commit/0b5662bc2be13a8c8f044d925d87fb6e56247cd8'} |
PyPI | PYSEC-2019-220 | null | In Pallets Jinja before 2.8.1, str.format allows a sandbox escape. | {'CVE-2016-10745', 'GHSA-hj2j-77xm-mc5v'} | 2021-11-22T04:57:52.929678Z | 2019-04-08T13:29:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2019-05/msg00030.html', 'http://lists.opensuse.org/opensuse-security-announce/2019-06/msg00064.html', 'https://access.redhat.com/errata/RHSA-2019:4062', 'https://access.redhat.com/errata/RHSA-2019:3964', 'https://access.redhat.com/errata/RHSA-2019:1022', 'https://palletsprojects.com/blog/jinja-281-released/', 'https://github.com/advisories/GHSA-hj2j-77xm-mc5v', 'https://access.redhat.com/errata/RHSA-2019:1237', 'https://usn.ubuntu.com/4011-1/', 'https://usn.ubuntu.com/4011-2/', 'https://github.com/pallets/jinja/commit/9b53045c34e61013dc8f09b7e52a555fa16bed16', 'https://access.redhat.com/errata/RHSA-2019:1260'} | null | {'https://github.com/pallets/jinja/commit/9b53045c34e61013dc8f09b7e52a555fa16bed16'} | {'https://github.com/pallets/jinja/commit/9b53045c34e61013dc8f09b7e52a555fa16bed16'} |
PyPI | PYSEC-2020-323 | null | In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can craft cases where this is larger than that of the second tensor. In turn, this would result in reads/writes outside of bounds since the interpreter will wrongly assume that there is enough data in both tensors. The issue is patched in commit 8ee24e7949a203d234489f9da2c5bf45a7d5157d, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-mxjj-953w-2c2v', 'CVE-2020-15208'} | 2021-12-09T06:35:14.801373Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mxjj-953w-2c2v', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157d', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157d'} | {'https://github.com/tensorflow/tensorflow/commit/8ee24e7949a203d234489f9da2c5bf45a7d5157d'} |
PyPI | PYSEC-2021-636 | null | TensorFlow is an open source platform for machine learning. In affected versions the `ImmutableConst` operation in TensorFlow can be tricked into reading arbitrary memory contents. This is because the `tstring` TensorFlow string class has a special case for memory mapped strings but the operation itself does not offer any support for this datatype. 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-2021-41227', 'GHSA-j8c8-67vp-6mx7'} | 2021-12-09T06:35:11.413655Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585', 'https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8c8-67vp-6mx7'} | null | {'https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b', 'https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585'} | {'https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585', 'https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b'} |
PyPI | GHSA-rhq2-3vr9-6mcr | Files on the host computer can be accessed from the Gradio interface | ### Impact
This is a vulnerability that affects anyone who creates and publicly shares Gradio interfaces using `gradio<2.4.8`. Because of the way that static files were being served, someone who generated a public Gradio link and shared it with others would potentially be exposing the files on the computer that generated the link, while the link was active. An attacker would be able to view the contents of a file on the computer if they knew the exact relative filepath. We do not have any evidence that this was ever exploited, but we treated the issue seriously and immediately took steps to mitigate it (see below)
### Response
1. We worked with @haby0 to immediately patch the issue and released a new version, `gradio 2.5.0`, within 24 hours of the issue being brought to our attention
2. We enabled a notification that is printed to anyone using an older version of gradio telling them to upgrade (see screenshot below)
3. We expanded our test suite to test for this vulnerability ensuring that our patch does not get reverted in future releases of `gradio`
### Patches
The problem has been patched in `gradio>=2.5.0`.
| {'CVE-2021-43831'} | 2022-03-03T05:13:32.858956Z | 2022-01-21T23:43:33Z | HIGH | null | {'CWE-22'} | {'https://github.com/gradio-app/gradio/security/advisories/GHSA-rhq2-3vr9-6mcr', 'https://github.com/gradio-app/gradio', 'https://github.com/gradio-app/gradio/commit/41bd3645bdb616e1248b2167ca83636a2653f781', 'https://nvd.nist.gov/vuln/detail/CVE-2021-43831'} | null | {'https://github.com/gradio-app/gradio/commit/41bd3645bdb616e1248b2167ca83636a2653f781'} | {'https://github.com/gradio-app/gradio/commit/41bd3645bdb616e1248b2167ca83636a2653f781'} |
PyPI | PYSEC-2021-266 | 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'} | 2021-08-27T03:22:43.455188Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27j5-4p9v-pp67'} | null | {'https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2'} | {'https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2'} |
PyPI | PYSEC-2022-86 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `GetInitOp` is vulnerable to a crash caused by dereferencing a null pointer. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-8cxv-76p7-jxwr', 'CVE-2022-23577'} | 2022-03-09T00:17:34.536542Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/cc/saved_model/loader_util.cc#L31-L61', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8cxv-76p7-jxwr', 'https://github.com/tensorflow/tensorflow/commit/4f38b1ac8e42727e18a2f0bde06d3bee8e77b250'} | null | {'https://github.com/tensorflow/tensorflow/commit/4f38b1ac8e42727e18a2f0bde06d3bee8e77b250'} | {'https://github.com/tensorflow/tensorflow/commit/4f38b1ac8e42727e18a2f0bde06d3bee8e77b250'} |
PyPI | PYSEC-2022-146 | null | Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that `TensorByteSize` would trigger `CHECK` failures. `TensorShape` constructor throws a `CHECK`-fail if shape is partial or has a number of elements that would overflow the size of an `int`. The `PartialTensorShape` constructor instead does not cause a `CHECK`-abort if the shape is partial, which is exactly what this function needs to be able to return `-1`. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'CVE-2022-23582', 'GHSA-4j82-5ccr-4r8v'} | 2022-03-09T00:18:28.710235Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4j82-5ccr-4r8v', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/attr_value_util.cc#L46-L50', 'https://github.com/tensorflow/tensorflow/commit/c2426bba00a01de6913738df8fa78e0215fcce02'} | null | {'https://github.com/tensorflow/tensorflow/commit/c2426bba00a01de6913738df8fa78e0215fcce02'} | {'https://github.com/tensorflow/tensorflow/commit/c2426bba00a01de6913738df8fa78e0215fcce02'} |
PyPI | GHSA-m242-wc86-8768 | Moderate severity vulnerability that affects python-fedora | python-fedora 0.8.0 and lower is vulnerable to an open redirect resulting in loss of CSRF protection | {'CVE-2017-1002150'} | 2022-03-03T05:14:00.412847Z | 2018-07-13T15:17:05Z | MODERATE | null | {'CWE-601'} | {'https://github.com/fedora-infra/python-fedora', 'https://github.com/fedora-infra/python-fedora/commit/b27f38a67573f4c989710c9bfb726dd4c1eeb929.patch', 'https://nvd.nist.gov/vuln/detail/CVE-2017-1002150', 'https://github.com/fedora-infra/python-fedora/commit/b27f38a67573f4c989710c9bfb726dd4c1eeb929', 'https://github.com/advisories/GHSA-m242-wc86-8768'} | null | {'https://github.com/fedora-infra/python-fedora/commit/b27f38a67573f4c989710c9bfb726dd4c1eeb929'} | {'https://github.com/fedora-infra/python-fedora/commit/b27f38a67573f4c989710c9bfb726dd4c1eeb929'} |
PyPI | GHSA-vfq6-hq5r-27r6 | Potential account hijack via password reset form in Django | Django before 1.11.27, 2.x before 2.2.9, and 3.x before 3.0.1 allows account takeover. A suitably crafted email address (that is equal to an existing user's email address after case transformation of Unicode characters) would allow an attacker to be sent a password reset token for the matched user account. (One mitigation in the new releases is to send password reset tokens only to the registered user email address.) | {'CVE-2019-19844'} | 2022-03-21T21:00:59.294957Z | 2020-01-16T22:35:12Z | CRITICAL | null | {'CWE-640'} | {'https://nvd.nist.gov/vuln/detail/CVE-2019-19844', 'https://github.com/django/django/commit/5b1fbcef7a8bec991ebe7b2a18b5d5a95d72cb70', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/HCM2DPUI7TOZWN4A6JFQFUVQ2XGE7GUD/', 'https://usn.ubuntu.com/4224-1/', 'https://groups.google.com/forum/#!topic/django-announce/3oaB2rVH3a0', 'https://www.djangoproject.com/weblog/2019/dec/18/security-releases/', 'https://www.debian.org/security/2020/dsa-4598', 'https://security.gentoo.org/glsa/202004-17', 'https://security.netapp.com/advisory/ntap-20200110-0003/', 'https://seclists.org/bugtraq/2020/Jan/9', 'http://packetstormsecurity.com/files/155872/Django-Account-Hijack.html', 'https://github.com/django/django/commit/f4cff43bf921fcea6a29b726eb66767f67753fa2'} | null | {'https://github.com/django/django/commit/f4cff43bf921fcea6a29b726eb66767f67753fa2', 'https://github.com/django/django/commit/5b1fbcef7a8bec991ebe7b2a18b5d5a95d72cb70'} | {'https://github.com/django/django/commit/5b1fbcef7a8bec991ebe7b2a18b5d5a95d72cb70', 'https://github.com/django/django/commit/f4cff43bf921fcea6a29b726eb66767f67753fa2'} |
PyPI | PYSEC-2022-69 | null | Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would allow limited reads and writes outside of arrays in TFLite. This exploits missing validation in the conversion from sparse tensors to dense tensors. The fix is included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. Users are advised to upgrade as soon as possible. | {'GHSA-4hvf-hxvg-f67v', 'CVE-2022-23560'} | 2022-03-09T00:17:32.438434Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v', 'https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038', 'https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/internal/utils/sparsity_format_converter.cc#L252-L293'} | null | {'https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038'} | {'https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038'} |
PyPI | GHSA-4mrx-6fxm-8jpg | Buffer Overflow in vyper | ### Impact
Importing a function from a JSON interface which returns `bytes` generates bytecode which does not clamp bytes length, potentially resulting in a buffer overrun.
### Patches
0.3.2 (as of https://github.com/vyperlang/vyper/commit/049dbdc647b2ce838fae7c188e6bb09cf16e470b)
### Workarounds
Use .vy interfaces. | {'CVE-2022-24788'} | 2022-04-22T20:00:10.011649Z | 2022-04-20T20:31:44Z | HIGH | null | {'CWE-120', 'CWE-119'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-24788', 'https://github.com/vyperlang/vyper/commit/049dbdc647b2ce838fae7c188e6bb09cf16e470b', 'https://github.com/vyperlang/vyper', 'https://github.com/vyperlang/vyper/security/advisories/GHSA-4mrx-6fxm-8jpg'} | null | {'https://github.com/vyperlang/vyper/commit/049dbdc647b2ce838fae7c188e6bb09cf16e470b'} | {'https://github.com/vyperlang/vyper/commit/049dbdc647b2ce838fae7c188e6bb09cf16e470b'} |
PyPI | PYSEC-2021-374 | null | Cobbler before 3.3.0 allows arbitrary file write operations via upload_log_data. | {'GHSA-4cfr-gjfx-fj3x', 'CVE-2021-40324'} | 2021-10-19T21:47:31.730339Z | 2021-10-04T06:15:00Z | null | null | null | {'https://github.com/cobbler/cobbler/commit/d8f60bbf14a838c8c8a1dba98086b223e35fe70a', 'https://github.com/advisories/GHSA-4cfr-gjfx-fj3x', 'https://github.com/cobbler/cobbler/releases/tag/v3.3.0'} | null | {'https://github.com/cobbler/cobbler/commit/d8f60bbf14a838c8c8a1dba98086b223e35fe70a'} | {'https://github.com/cobbler/cobbler/commit/d8f60bbf14a838c8c8a1dba98086b223e35fe70a'} |
PyPI | PYSEC-2021-724 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `SVDF` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/7f283ff806b2031f407db64c4d3edcda8fb9f9f5/tensorflow/lite/kernels/svdf.cc#L99-L102). An attacker can craft a model such that `params->rank` 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. | {'CVE-2021-29598', 'GHSA-pmpr-55fj-r229'} | 2021-12-09T06:35:31.724950Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pmpr-55fj-r229', 'https://github.com/tensorflow/tensorflow/commit/6841e522a3e7d48706a02e8819836e809f738682'} | null | {'https://github.com/tensorflow/tensorflow/commit/6841e522a3e7d48706a02e8819836e809f738682'} | {'https://github.com/tensorflow/tensorflow/commit/6841e522a3e7d48706a02e8819836e809f738682'} |
PyPI | PYSEC-2021-231 | null | TensorFlow is an end-to-end open source platform for machine learning. 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. 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-2021-29594', 'GHSA-3qgw-p4fm-x7gf'} | 2021-08-27T03:22:38.125295Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3qgw-p4fm-x7gf', 'https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552'} | null | {'https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552'} | {'https://github.com/tensorflow/tensorflow/commit/ff489d95a9006be080ad14feb378f2b4dac35552'} |
PyPI | GHSA-98vv-pw6r-q6q4 | Uncontrolled Resource Consumption in pillow | The package pillow from 0 and before 8.3.2 are vulnerable to Regular Expression Denial of Service (ReDoS) via the getrgb function. | {'CVE-2021-23437'} | 2022-03-03T05:14:09.055722Z | 2021-09-07T23:08:10Z | HIGH | null | {'CWE-125', 'CWE-400'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-23437', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/VKRCL7KKAKOXCVD7M6WC5OKFGL4L3SJT/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/RNSG6VFXTAROGF7ACYLMAZNQV4EJ6I2C/', 'https://pillow.readthedocs.io/en/stable/releasenotes/8.3.2.html', 'https://snyk.io/vuln/SNYK-PYTHON-PILLOW-1319443', 'https://github.com/python-pillow/Pillow/commit/9e08eb8f78fdfd2f476e1b20b7cf38683754866b', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/9e08eb8f78fdfd2f476e1b20b7cf38683754866b'} | {'https://github.com/python-pillow/Pillow/commit/9e08eb8f78fdfd2f476e1b20b7cf38683754866b'} |
PyPI | PYSEC-2021-661 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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-2021-29535', 'GHSA-m3f9-w3p3-p669'} | 2021-12-09T06:35:20.792Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m3f9-w3p3-p669', 'https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87'} | null | {'https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87'} | {'https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87'} |
PyPI | PYSEC-2020-231 | null | Feedgen (python feedgen) before 0.9.0 is susceptible to XML Denial of Service attacks. 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 (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. This problem has been fixed in feedgen 0.9.0 which disallows XML entity expansion and external resources. | {'GHSA-g8q7-xv52-hf9f', 'CVE-2020-5227'} | 2021-08-27T03:22:03.690780Z | 2020-01-28T23:15:00Z | null | null | null | {'https://github.com/lkiesow/python-feedgen/security/advisories/GHSA-g8q7-xv52-hf9f', 'https://github.com/lkiesow/python-feedgen/commit/f57a01b20fa4aaaeccfa417f28e66b4084b9d0cf', 'https://docs.microsoft.com/en-us/archive/msdn-magazine/2009/november/xml-denial-of-service-attacks-and-defenses', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/T6I5ENUYGFNMIH6ZQ62FZ6VU2WD3SIOI/'} | null | {'https://github.com/lkiesow/python-feedgen/commit/f57a01b20fa4aaaeccfa417f28e66b4084b9d0cf'} | {'https://github.com/lkiesow/python-feedgen/commit/f57a01b20fa4aaaeccfa417f28e66b4084b9d0cf'} |
PyPI | PYSEC-2021-548 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-cgfm-62j4-v4rf', 'CVE-2021-37635'} | 2021-12-09T06:35:01.967587Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cgfm-62j4-v4rf'} | null | {'https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750'} | {'https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750'} |
PyPI | GHSA-jg4f-jqm5-4mgq | Ansible fails to properly sanitize fact variables sent from the Ansible controller | Ansible before version 2.2.0 fails to properly sanitize fact variables sent from the Ansible controller. An attacker with the ability to create special variables on the controller could execute arbitrary commands on Ansible clients as the user Ansible runs as. | {'CVE-2016-8628'} | 2022-04-26T18:48:01.177597Z | 2018-10-10T17:23:14Z | CRITICAL | null | {'CWE-77'} | {'https://github.com/ansible/ansible', 'https://github.com/ansible/ansible/commit/35938b907dfcd1106ca40b794f0db446bdb8cf09', 'https://access.redhat.com/errata/RHSA-2016:2778', 'https://nvd.nist.gov/vuln/detail/CVE-2016-8628', 'http://www.securityfocus.com/bid/94109', 'https://github.com/advisories/GHSA-jg4f-jqm5-4mgq', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2016-8628'} | null | {'https://github.com/ansible/ansible/commit/35938b907dfcd1106ca40b794f0db446bdb8cf09'} | {'https://github.com/ansible/ansible/commit/35938b907dfcd1106ca40b794f0db446bdb8cf09'} |
PyPI | GHSA-f2vv-v9cg-qhh7 | Assertion failure based denial of service in Tensorflow | ### Impact
The [implementation of `*Bincount` operations](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/bincount_op.cc) allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail:
```python
import tensorflow as tf
tf.raw_ops.DenseBincount(
input=[[0], [1], [2]],
size=[1],
weights=[3,2,1],
binary_output=False)
```
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.
### Patches
We have patched the issue in GitHub commit [7019ce4f68925fd01cdafde26f8d8c938f47e6f9](https://github.com/tensorflow/tensorflow/commit/7019ce4f68925fd01cdafde26f8d8c938f47e6f9).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Faysal Hossain Shezan from University of Virginia. | {'CVE-2022-21737'} | 2022-03-03T05:13:47.124429Z | 2022-02-09T23:43:48Z | MODERATE | null | {'CWE-617', 'CWE-754'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-21737', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2vv-v9cg-qhh7', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/bincount_op.cc', 'https://github.com/tensorflow/tensorflow/commit/7019ce4f68925fd01cdafde26f8d8c938f47e6f9'} | null | {'https://github.com/tensorflow/tensorflow/commit/7019ce4f68925fd01cdafde26f8d8c938f47e6f9'} | {'https://github.com/tensorflow/tensorflow/commit/7019ce4f68925fd01cdafde26f8d8c938f47e6f9'} |
PyPI | PYSEC-2021-118 | null | The Jupyter notebook is a web-based notebook environment for interactive computing. In affected versions untrusted notebook can execute code on load. Jupyter Notebook uses a deprecated version of Google Caja to sanitize user inputs. A public Caja bypass can be used to trigger an XSS when a victim opens a malicious ipynb document in Jupyter Notebook. The XSS allows an attacker to execute arbitrary code on the victim computer using Jupyter APIs. | {'CVE-2021-32798', 'GHSA-hwvq-6gjx-j797'} | 2021-08-17T18:40:19.674164Z | 2021-08-09T21:15:00Z | null | null | null | {'https://github.com/jupyter/notebook/security/advisories/GHSA-hwvq-6gjx-j797', 'https://github.com/jupyter/notebook/commit/79fc76e890a8ec42f73a3d009e44ef84c14ef0d5'} | null | {'https://github.com/jupyter/notebook/commit/79fc76e890a8ec42f73a3d009e44ef84c14ef0d5'} | {'https://github.com/jupyter/notebook/commit/79fc76e890a8ec42f73a3d009e44ef84c14ef0d5'} |
PyPI | GHSA-9c8h-2mv3-49ww | Division by 0 in most convolution operators | ### Impact
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:
```python
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
tf.raw_ops.Conv2D(
input = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32),
filter = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32),
strides = [1, 1, 1, 1],
padding = "SAME")
```
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.
### Patches
We have patched the issue in GitHub commit [8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4](https://github.com/tensorflow/tensorflow/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.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang of Baidu Security. | {'CVE-2021-37675'} | 2022-03-03T05:12:49.576138Z | 2021-08-25T14:41:29Z | MODERATE | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c8h-2mv3-49ww', 'https://github.com/tensorflow/tensorflow/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37675'} | null | {'https://github.com/tensorflow/tensorflow/commit/8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4'} | {'https://github.com/tensorflow/tensorflow/commit/8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4'} |
PyPI | PYSEC-2020-118 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, the implementation of `SparseFillEmptyRowsGrad` uses a double indexing pattern. It is possible for `reverse_index_map(i)` to be an index outside of bounds of `grad_values`, thus resulting in a heap buffer overflow. The issue is patched in commit 390611e0d45c5793c7066110af37c8514e6a6c54, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-63xm-rx5p-xvqr', 'CVE-2020-15195'} | 2020-10-29T16:15:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-63xm-rx5p-xvqr', 'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54'} | {'https://github.com/tensorflow/tensorflow/commit/390611e0d45c5793c7066110af37c8514e6a6c54'} |
PyPI | PYSEC-2018-85 | null | python-kdcproxy before 0.3.2 allows remote attackers to cause a denial of service via a large POST request. | {'CVE-2015-5159', 'GHSA-j7c4-2xj8-wm7r'} | 2021-08-27T03:22:05.569463Z | 2018-10-30T18:29:00Z | null | null | null | {'https://github.com/latchset/kdcproxy/commit/f274aa6787cb8b3ec1cc12c440a56665b7231882', 'https://github.com/advisories/GHSA-j7c4-2xj8-wm7r', 'https://bugzilla.redhat.com/show_bug.cgi?id=1245200'} | null | {'https://github.com/latchset/kdcproxy/commit/f274aa6787cb8b3ec1cc12c440a56665b7231882'} | {'https://github.com/latchset/kdcproxy/commit/f274aa6787cb8b3ec1cc12c440a56665b7231882'} |
PyPI | GHSA-m3jw-62m7-jjcm | Out-of-bounds read in typed-ast and cpython may allow an attacker to crash the interpreter process (handle_keywordonly_args case). | typed_ast 1.3.0 and 1.3.1 has a handle_keywordonly_args out-of-bounds read. An attacker with the ability to cause a Python interpreter to parse Python source (but not necessarily execute it) may be able to crash the interpreter process. This could be a concern, for example, in a web-based service that parses (but does not execute) Python code. (This issue also affected certain Python 3.8.0-alpha prereleases.) | {'CVE-2019-19274'} | 2022-03-03T05:13:58.719975Z | 2019-12-02T18:02:02Z | HIGH | null | {'CWE-125'} | {'https://github.com/python/cpython/commit/a4d78362397fc3bced6ea80fbc7b5f4827aec55e', 'https://nvd.nist.gov/vuln/detail/CVE-2019-19274', 'https://bugs.python.org/issue36495', 'https://github.com/python/cpython/commit/dcfcd146f8e6fc5c2fc16a4c192a0c5f5ca8c53c', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/LG5H4Q6LFVRX7SFXLBEJMNQFI4T5SCEA/', 'https://github.com/python/typed_ast/commit/156afcb26c198e162504a57caddfe0acd9ed7dce', 'https://github.com/python/typed_ast/commit/dc317ac9cff859aa84eeabe03fb5004982545b3b'} | null | {'https://github.com/python/cpython/commit/dcfcd146f8e6fc5c2fc16a4c192a0c5f5ca8c53c', 'https://github.com/python/cpython/commit/a4d78362397fc3bced6ea80fbc7b5f4827aec55e', 'https://github.com/python/typed_ast/commit/dc317ac9cff859aa84eeabe03fb5004982545b3b', 'https://github.com/python/typed_ast/commit/156afcb26c198e162504a57caddfe0acd9ed7dce'} | {'https://github.com/python/cpython/commit/a4d78362397fc3bced6ea80fbc7b5f4827aec55e', 'https://github.com/python/typed_ast/commit/dc317ac9cff859aa84eeabe03fb5004982545b3b', 'https://github.com/python/cpython/commit/dcfcd146f8e6fc5c2fc16a4c192a0c5f5ca8c53c', 'https://github.com/python/typed_ast/commit/156afcb26c198e162504a57caddfe0acd9ed7dce'} |
PyPI | GHSA-q3g3-h9r4-prrc | Reference binding to nullptr and heap OOB in binary cwise ops | ### Impact
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):
```python
import tensorflow as tf
tf.raw_ops.SqrtGrad(y=[4, 16],dy=[])
```
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.
### Patches
We have patched the issue in GitHub commit [93f428fd1768df147171ed674fee1fc5ab8309ec](https://github.com/tensorflow/tensorflow/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.
### 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-2021-37659'} | 2022-03-03T05:13:43.553860Z | 2021-08-25T14:42:47Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q3g3-h9r4-prrc', 'https://github.com/tensorflow/tensorflow/commit/93f428fd1768df147171ed674fee1fc5ab8309ec', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37659'} | null | {'https://github.com/tensorflow/tensorflow/commit/93f428fd1768df147171ed674fee1fc5ab8309ec'} | {'https://github.com/tensorflow/tensorflow/commit/93f428fd1768df147171ed674fee1fc5ab8309ec'} |
PyPI | PYSEC-2022-111 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `ThreadPoolHandle` can be used to trigger a denial of service attack by allocating too much memory. This is because the `num_threads` argument is only checked to not be negative, but there is no upper bound on its value. 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-c582-c96p-r5cq', 'CVE-2022-21732'} | 2022-03-09T00:18:23.954976Z | 2022-02-03T12:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c582-c96p-r5cq', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/data/experimental/threadpool_dataset_op.cc#L79-L135'} | null | {'https://github.com/tensorflow/tensorflow/commit/e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e'} | {'https://github.com/tensorflow/tensorflow/commit/e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e'} |
PyPI | GHSA-h65g-jfqg-2w6m | Server-Side Request Forgery in calibreweb | calibreweb prior to version 0.6.17 is vulnerable to server-side request forgery (SSRF). This is a result of incomplete SSRF protection that can be bypassed via an HTTP redirect. An HTTP server set up to respond with a 302 redirect may redirect a request to `localhost`. | {'CVE-2022-0767'} | 2022-03-15T22:00:10.899967Z | 2022-03-08T00:00:31Z | CRITICAL | null | {'CWE-918'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-0767', 'https://github.com/janeczku/calibre-web/commit/965352c8d96c9eae7a6867ff76b0db137d04b0b8', 'https://huntr.dev/bounties/b26fc127-9b6a-4be7-a455-58aefbb62d9e', 'https://github.com/janeczku/calibre-web'} | null | {'https://github.com/janeczku/calibre-web/commit/965352c8d96c9eae7a6867ff76b0db137d04b0b8'} | {'https://github.com/janeczku/calibre-web/commit/965352c8d96c9eae7a6867ff76b0db137d04b0b8'} |
PyPI | GHSA-g6rj-rv7j-xwp4 | Denial of service | An issue was discovered in Pillow before 8.2.0. PSDImagePlugin.PsdImageFile lacked a sanity check on the number of input layers relative to the size of the data block. This could lead to a DoS on Image.open prior to Image.load. | {'CVE-2021-28675'} | 2022-03-03T05:13:18.742311Z | 2021-06-08T18:49:11Z | MODERATE | null | {'CWE-252', 'CWE-233'} | {'https://pillow.readthedocs.io/en/stable/releasenotes/8.2.0.html#cve-2021-28675-fix-dos-in-psdimageplugin', 'https://github.com/python-pillow/Pillow/pull/5377/commits/22e9bee4ef225c0edbb9323f94c26cee0c623497', 'https://nvd.nist.gov/vuln/detail/CVE-2021-28675', 'https://security.gentoo.org/glsa/202107-33', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MQHA5HAIBOYI3R6HDWCLAGFTIQP767FL/'} | null | {'https://github.com/python-pillow/Pillow/pull/5377/commits/22e9bee4ef225c0edbb9323f94c26cee0c623497'} | {'https://github.com/python-pillow/Pillow/pull/5377/commits/22e9bee4ef225c0edbb9323f94c26cee0c623497'} |
PyPI | GHSA-r854-96gq-rfg3 | Python Image Library (PIL) allows symlink attacks | The (1) JpegImagePlugin.py and (2) EpsImagePlugin.py scripts in Python Image Library (PIL) 1.1.7 and earlier and Pillow before 2.3.1 uses the names of temporary files on the command line, which makes it easier for local users to conduct symlink attacks by listing the processes. | {'CVE-2014-1933'} | 2022-03-03T05:13:05.290482Z | 2020-05-18T17:41:19Z | MODERATE | null | null | {'https://github.com/python-imaging/Pillow', 'http://www.openwall.com/lists/oss-security/2014/02/11/1', 'http://www.openwall.com/lists/oss-security/2014/02/10/15', 'https://nvd.nist.gov/vuln/detail/CVE-2014-1933', 'https://github.com/python-imaging/Pillow/commit/4e9f367dfd3f04c8f5d23f7f759ec12782e10ee7', 'https://security.gentoo.org/glsa/201612-52', 'http://www.ubuntu.com/usn/USN-2168-1', 'http://www.securityfocus.com/bid/65513', 'http://lists.opensuse.org/opensuse-updates/2014-05/msg00002.html'} | null | {'https://github.com/python-imaging/Pillow/commit/4e9f367dfd3f04c8f5d23f7f759ec12782e10ee7'} | {'https://github.com/python-imaging/Pillow/commit/4e9f367dfd3f04c8f5d23f7f759ec12782e10ee7'} |
PyPI | PYSEC-2017-22 | null | An exploitable vulnerability exists in the YAML loading functionality of util.py in OwlMixin before 2.0.0a12. A "Load YAML" string or file (aka load_yaml or load_yamlf) 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-2017-16618', 'GHSA-ccmq-qvcp-5mrm'} | 2021-07-05T00:01:23.384346Z | 2017-11-08T03:29:00Z | null | null | null | {'https://joel-malwarebenchmark.github.io/blog/2017/11/08/cve-2017-16618-convert-through-owlmixin/', 'https://github.com/tadashi-aikawa/owlmixin/issues/12', 'https://github.com/advisories/GHSA-ccmq-qvcp-5mrm', 'https://github.com/tadashi-aikawa/owlmixin/commit/5d0575303f6df869a515ced4285f24ba721e0d4e'} | null | {'https://github.com/tadashi-aikawa/owlmixin/commit/5d0575303f6df869a515ced4285f24ba721e0d4e'} | {'https://github.com/tadashi-aikawa/owlmixin/commit/5d0575303f6df869a515ced4285f24ba721e0d4e'} |
PyPI | GHSA-49rx-x2rw-pc6f | Heap OOB read in all `tf.raw_ops.QuantizeAndDequantizeV*` ops | ### Impact
The [shape inference functions for the `QuantizeAndDequantizeV*` operations](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/ops/array_ops.cc) can trigger a read outside of bounds of heap allocated array as illustrated in the following sets of PoCs:
```python
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV4Grad(
gradients=[1.0,1.0],
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
axis=-100)
return data
test()
```
```python
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV4(
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
signed_input=False,
num_bits=10,
range_given=False,
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-100)
return data
test()
```
```python
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV3(
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
signed_input=False,
num_bits=10,
range_given=False,
narrow_range=False,
axis=-100)
return data
test()
```
```python
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV2(
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
signed_input=False,
num_bits=10,
range_given=False,
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-100)
return data
test()
```
In all of these cases, `axis` is a negative value different than the special value used for optional/unknown dimensions (i.e., -1). However, the code ignores the occurences of these values:
```cc
...
if (axis != -1) {
...
c->Dim(input, axis);
...
}
```
### Patches
We have patched the issue in GitHub commit [7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d](https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
| {'CVE-2021-41205'} | 2022-03-03T05:14:08.515949Z | 2021-11-10T19:04:25Z | HIGH | null | {'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-41205', 'https://github.com/tensorflow/tensorflow', '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-5xwc-mrhx-5g3m | Reference binding to nullptr in `MatrixDiagV*` ops | ### Impact
An attacker can cause undefined behavior via binding a reference to null pointer in all operations of type `tf.raw_ops.MatrixDiagV*`:
```python
import tensorflow as tf
tf.raw_ops.MatrixDiagV3(
diagonal=[1,0],
k=[],
num_rows=[1,2,3],
num_cols=[4,5],
padding_value=[],
align='RIGHT_RIGHT')
```
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:
```cc
auto& diag_index = context->input(1);
...
lower_diag_index = diag_index.flat<int32>()(0);
```
### Patches
We have patched the issue in GitHub commit [f2a673bd34f0d64b8e40a551ac78989d16daad09](https://github.com/tensorflow/tensorflow/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.
### 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-2021-37657'} | 2022-03-03T05:12:52.150970Z | 2021-08-25T14:42:52Z | HIGH | null | {'CWE-824'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37657', 'https://github.com/tensorflow/tensorflow', '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-2020-78 | null | In Pillow before 7.1.0, there are two Buffer Overflows in libImaging/TiffDecode.c. | null | 2020-07-27T19:15:00Z | 2020-06-25T19:15:00Z | null | null | null | {'https://github.com/python-pillow/Pillow/commits/master/src/libImaging', 'https://github.com/python-pillow/Pillow#diff-9478f2787e3ae9668a15123b165c23ac/commit/46f4a349b88915787fea3fb91348bb1665831bbb', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/HOKHNWV2VS5GESY7IBD237E7C6T3I427/', 'https://github.com/python-pillow/Pillow/commit/46f4a349b88915787fea3fb91348bb1665831bbb#diff-9478f2787e3ae9668a15123b165c23ac', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/BEBCPE4F2VHTIT6EZA2YZQZLPVDEBJGD/', 'https://usn.ubuntu.com/4430-2/', 'https://pillow.readthedocs.io/en/stable/releasenotes/7.1.0.html', 'https://github.com/python-pillow/Pillow/pull/4538'} | null | {'https://github.com/python-pillow/Pillow/commit/46f4a349b88915787fea3fb91348bb1665831bbb#diff-9478f2787e3ae9668a15123b165c23ac', 'https://github.com/python-pillow/Pillow#diff-9478f2787e3ae9668a15123b165c23ac/commit/46f4a349b88915787fea3fb91348bb1665831bbb'} | {'https://github.com/python-pillow/Pillow/commit/46f4a349b88915787fea3fb91348bb1665831bbb#diff-9478f2787e3ae9668a15123b165c23ac', 'https://github.com/python-pillow/Pillow#diff-9478f2787e3ae9668a15123b165c23ac/commit/46f4a349b88915787fea3fb91348bb1665831bbb'} |
PyPI | PYSEC-2021-476 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc) does not validate all constraints specified in the op's contract(https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedBatchNormWithGlobalNormalization). 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-2021-29548', 'GHSA-p45v-v4pw-77jr'} | 2021-12-09T06:34:50.800401Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p45v-v4pw-77jr'} | null | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} |
PyPI | PYSEC-2021-533 | 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'} | 2021-12-09T06:34:59.713113Z | 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 | GHSA-gh8h-7j2j-qv4f | Incomplete validation in `tf.summary.create_file_writer` | ### Impact
If `tf.summary.create_file_writer` is called with non-scalar arguments code crashes due to a `CHECK`-fail.
```python
import tensorflow as tf
import numpy as np
tf.summary.create_file_writer(logdir='', flush_millis=np.ones((1,2)))
```
### Patches
We have patched the issue in GitHub commit [874bda09e6702cd50bac90b453b50bcc65b2769e](https://github.com/tensorflow/tensorflow/commit/874bda09e6702cd50bac90b453b50bcc65b2769e) (merging [#51715](https://github.com/tensorflow/tensorflow/pull/51715)).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/46909). | {'CVE-2021-41200'} | 2022-03-03T05:12:28.810039Z | 2021-11-10T19:31:16Z | MODERATE | null | {'CWE-617'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-41200', 'https://github.com/tensorflow/tensorflow/commit/874bda09e6702cd50bac90b453b50bcc65b2769e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gh8h-7j2j-qv4f', 'https://github.com/tensorflow/tensorflow/issues/46909', 'https://github.com/tensorflow/tensorflow'} | null | {'https://github.com/tensorflow/tensorflow/commit/874bda09e6702cd50bac90b453b50bcc65b2769e'} | {'https://github.com/tensorflow/tensorflow/commit/874bda09e6702cd50bac90b453b50bcc65b2769e'} |
PyPI | PYSEC-2021-163 | 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.Conv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/988087bd83f144af14087fe4fecee2d250d93737/tensorflow/core/kernels/conv_ops.cc#L261-L263) does a division by a quantity that is controlled by the caller. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-4vf2-4xcg-65cx', 'CVE-2021-29526'} | 2021-08-27T03:22:25.990763Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4vf2-4xcg-65cx'} | null | {'https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b'} | {'https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b'} |
PyPI | PYSEC-2021-499 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29571', 'GHSA-whr9-vfh2-7hm6'} | 2021-12-09T06:34:54.370426Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/79865b542f9ffdc9caeb255631f7c56f1d4b6517', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-whr9-vfh2-7hm6'} | null | {'https://github.com/tensorflow/tensorflow/commit/79865b542f9ffdc9caeb255631f7c56f1d4b6517'} | {'https://github.com/tensorflow/tensorflow/commit/79865b542f9ffdc9caeb255631f7c56f1d4b6517'} |
PyPI | PYSEC-2020-97 | null | In qutebrowser versions less than 1.11.1, reloading a page with certificate errors shows a green URL. After a certificate error was overridden by the user, qutebrowser displays the URL as yellow (colors.statusbar.url.warn.fg). However, when the affected website was subsequently loaded again, the URL was mistakenly displayed as green (colors.statusbar.url.success_https). While the user already has seen a certificate error prompt at this point (or set content.ssl_strict to false, which is not recommended), this could still provide a false sense of security. This has been fixed in 1.11.1 and 1.12.0. All versions of qutebrowser are believed to be affected, though versions before v0.11.x couldn't be tested. Backported patches for older versions (greater than or equal to 1.4.0 and less than or equal to 1.10.2) are available, but no further releases are planned. | {'GHSA-4rcq-jv2f-898j', 'CVE-2020-11054'} | 2020-09-21T02:15:00Z | 2020-05-07T21:15:00Z | null | null | null | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/7YWJ5QNHXKTGG5NLV7EGEOKPBVZBA5GS/', 'https://github.com/qutebrowser/qutebrowser/commit/19f01bb42d02da539446a52a25bb0c1232b86327', 'https://github.com/qutebrowser/qutebrowser/security/advisories/GHSA-4rcq-jv2f-898j', 'https://github.com/qutebrowser/qutebrowser/commit/4020210b193f77cf1785b21717f6ef7c5de5f0f8', 'https://github.com/qutebrowser/qutebrowser/commit/2281a205c3e70ec20f35ec8fafecee0d5c4f3478', 'https://github.com/qutebrowser/qutebrowser/issues/5403', 'https://github.com/qutebrowser/qutebrowser/commit/a45ca9c788f648d10cccce2af41405bf25ee2948', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MKAZOOTJ2MBHTYVYQQ52NL53F5CB2XAP/', 'https://bugs.kde.org/show_bug.cgi?id=420902', 'https://github.com/qutebrowser/qutebrowser/commit/d28ed758d077a5bf19ddac4da468f7224114df23', 'https://github.com/qutebrowser/qutebrowser/commit/9bd1cf585fccdfe8318fff7af793730e74a04db3', 'https://tracker.die-offenbachs.homelinux.org/eric/issue328', 'https://github.com/qutebrowser/qutebrowser/commit/f5d801251aa5436aff44660c87d7013e29ac5864', 'https://github.com/qutebrowser/qutebrowser/commit/021ab572a319ca3db5907a33a59774f502b3b975', 'https://github.com/qutebrowser/qutebrowser/commit/1b7946ed14b386a24db050f2d6dba81ba6518755', 'https://github.com/qutebrowser/qutebrowser/commit/6821c236f9ae23adf21d46ce0d56768ac8d0c467'} | null | {'https://github.com/qutebrowser/qutebrowser/commit/4020210b193f77cf1785b21717f6ef7c5de5f0f8', 'https://github.com/qutebrowser/qutebrowser/commit/f5d801251aa5436aff44660c87d7013e29ac5864', 'https://github.com/qutebrowser/qutebrowser/commit/021ab572a319ca3db5907a33a59774f502b3b975', 'https://github.com/qutebrowser/qutebrowser/commit/9bd1cf585fccdfe8318fff7af793730e74a04db3', 'https://github.com/qutebrowser/qutebrowser/commit/d28ed758d077a5bf19ddac4da468f7224114df23', 'https://github.com/qutebrowser/qutebrowser/commit/6821c236f9ae23adf21d46ce0d56768ac8d0c467', 'https://github.com/qutebrowser/qutebrowser/commit/a45ca9c788f648d10cccce2af41405bf25ee2948', 'https://github.com/qutebrowser/qutebrowser/commit/2281a205c3e70ec20f35ec8fafecee0d5c4f3478', 'https://github.com/qutebrowser/qutebrowser/commit/1b7946ed14b386a24db050f2d6dba81ba6518755', 'https://github.com/qutebrowser/qutebrowser/commit/19f01bb42d02da539446a52a25bb0c1232b86327'} | {'https://github.com/qutebrowser/qutebrowser/commit/6821c236f9ae23adf21d46ce0d56768ac8d0c467', 'https://github.com/qutebrowser/qutebrowser/commit/2281a205c3e70ec20f35ec8fafecee0d5c4f3478', 'https://github.com/qutebrowser/qutebrowser/commit/d28ed758d077a5bf19ddac4da468f7224114df23', 'https://github.com/qutebrowser/qutebrowser/commit/19f01bb42d02da539446a52a25bb0c1232b86327', 'https://github.com/qutebrowser/qutebrowser/commit/1b7946ed14b386a24db050f2d6dba81ba6518755', 'https://github.com/qutebrowser/qutebrowser/commit/4020210b193f77cf1785b21717f6ef7c5de5f0f8', 'https://github.com/qutebrowser/qutebrowser/commit/a45ca9c788f648d10cccce2af41405bf25ee2948', 'https://github.com/qutebrowser/qutebrowser/commit/f5d801251aa5436aff44660c87d7013e29ac5864', 'https://github.com/qutebrowser/qutebrowser/commit/9bd1cf585fccdfe8318fff7af793730e74a04db3', 'https://github.com/qutebrowser/qutebrowser/commit/021ab572a319ca3db5907a33a59774f502b3b975'} |
PyPI | PYSEC-2021-860 | null | Croatia Control Asterix 2.8.1 (python_v0.7.2) has a heap-based buffer over-read, with additional details to be disclosed at a later date. | {'CVE-2021-44144'} | 2022-01-05T02:16:11.881162Z | 2021-11-22T21:15:00Z | null | null | null | {'https://github.com/CroatiaControlLtd/asterix/issues/183', 'https://github.com/croatiacontrolltd/asterix/commit/3f765d387d239ccc44e278a2ffa600fb6a6587f9'} | null | {'https://github.com/croatiacontrolltd/asterix/commit/3f765d387d239ccc44e278a2ffa600fb6a6587f9'} | {'https://github.com/croatiacontrolltd/asterix/commit/3f765d387d239ccc44e278a2ffa600fb6a6587f9'} |
PyPI | GHSA-vfrc-ggmc-5jwv | Cross-site Scripting in django-helpdesk | django-helpdesk is vulnerable to Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') | {'CVE-2021-3950'} | 2022-03-03T05:13:21.706511Z | 2021-11-23T17:55:46Z | HIGH | null | {'CWE-79'} | {'https://huntr.dev/bounties/4d7a5fdd-b2de-467a-ade0-3f2fb386638e', 'https://nvd.nist.gov/vuln/detail/CVE-2021-3950', 'https://github.com/django-helpdesk/django-helpdesk/commit/04483bdac3b5196737516398b5ce0383875a5c60', 'https://github.com/django-helpdesk/django-helpdesk', 'https://github.com/django-helpdesk/django-helpdesk/releases/tag/0.3.2'} | null | {'https://github.com/django-helpdesk/django-helpdesk/commit/04483bdac3b5196737516398b5ce0383875a5c60'} | {'https://github.com/django-helpdesk/django-helpdesk/commit/04483bdac3b5196737516398b5ce0383875a5c60'} |
PyPI | GHSA-xwhf-g6j5-j5gc | Float cast overflow undefined behavior | ### Impact
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.
### Patches
We have patched the issue in c0319231333f0f16e1cc75ec83660b01fedd4182 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported in #42129 | {'CVE-2020-15266'} | 2022-03-03T05:13:49.090611Z | 2020-11-13T17:18:29Z | LOW | null | {'CWE-119'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15266', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845', 'https://github.com/tensorflow/tensorflow/issues/42129', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc'} | null | {'https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845'} | {'https://github.com/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845'} |
PyPI | GHSA-cwv3-863g-39vx | Stack overflow due to looping TFLite subgraph | ### Impact
TFlite graphs must not have loops between nodes. However, this condition was not checked and an attacker could craft models that would result in infinite loop during evaluation. In certain cases, the infinite loop would be replaced by stack overflow due to too many recursive calls.
For example, the [`While` implementation](https://github.com/tensorflow/tensorflow/blob/106d8f4fb89335a2c52d7c895b7a7485465ca8d9/tensorflow/lite/kernels/while.cc) could be tricked into a scneario where both the body and the loop subgraphs are the same. Evaluating one of the subgraphs means calling the `Eval` function for the other and this quickly exhaust all stack space.
### Patches
We have patched the issue in GitHub commit [9c1dc920d8ffb4893d6c9d27d1f039607b326743](https://github.com/tensorflow/tensorflow/commit/9c1dc920d8ffb4893d6c9d27d1f039607b326743) (for the `While` operator) and in GitHub commit [c6173f5fe66cdbab74f4f869311fe6aae2ba35f4](https://github.com/tensorflow/tensorflow/commit/c6173f5fe66cdbab74f4f869311fe6aae2ba35f4) (in general).
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-2021-29591'} | 2022-04-26T18:17:12.504780Z | 2021-05-21T14:26:56Z | HIGH | null | {'CWE-835', 'CWE-674'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29591', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cwv3-863g-39vx', 'https://github.com/tensorflow/tensorflow/commit/c6173f5fe66cdbab74f4f869311fe6aae2ba35f4', 'https://github.com/tensorflow/tensorflow/commit/9c1dc920d8ffb4893d6c9d27d1f039607b326743'} | null | {'https://github.com/tensorflow/tensorflow/commit/c6173f5fe66cdbab74f4f869311fe6aae2ba35f4', 'https://github.com/tensorflow/tensorflow/commit/9c1dc920d8ffb4893d6c9d27d1f039607b326743'} | {'https://github.com/tensorflow/tensorflow/commit/c6173f5fe66cdbab74f4f869311fe6aae2ba35f4', 'https://github.com/tensorflow/tensorflow/commit/9c1dc920d8ffb4893d6c9d27d1f039607b326743'} |
PyPI | GHSA-4hvv-7x94-7vq8 | Null dereference in Grappler's `TrySimplify` | ### Impact
The implementation of [`TrySimplify`](https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that
result in optimizing a node with no inputs.
### Patches
We have patched the issue in GitHub commit [e6340f0665d53716ef3197ada88936c2a5f7a2d3](https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3).
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. | {'CVE-2021-29616'} | 2022-03-03T05:14:00.916803Z | 2021-05-21T14:28:47Z | LOW | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvv-7x94-7vq8', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29616'} | null | {'https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3'} | {'https://github.com/tensorflow/tensorflow/commit/e6340f0665d53716ef3197ada88936c2a5f7a2d3'} |
PyPI | PYSEC-2021-708 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.Dequantize`, an attacker can trigger a read from outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/26003593aa94b1742f34dc22ce88a1e17776a67d/tensorflow/core/kernels/dequantize_op.cc#L106-L131) accesses the `min_range` and `max_range` tensors in parallel but fails to check that they have the same shape. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-c45w-2wxr-pp53', 'CVE-2021-29582'} | 2021-12-09T06:35:28.875018Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c45w-2wxr-pp53'} | null | {'https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1b'} | {'https://github.com/tensorflow/tensorflow/commit/5899741d0421391ca878da47907b1452f06aaf1b'} |
PyPI | GHSA-35m5-8cvj-8783 | Improper hashing in enrocrypt | ### Impact
The vulnerability is we used MD5 hashing Algorithm In our hashing file. If anyone who is a beginner(and doesn't know about hashes) can face problems as MD5 is considered a Insecure Hashing Algorithm.
### Patches
The vulnerability is patched in v1.1.4 of the product, the users can upgrade to version 1.1.4.
### Workarounds
If u specifically want a version and don't want to upgrade, you can remove the `MD5` hashing function from the file `hashing.py` and this vulnerability will be gone
### References
https://www.cybersecurity-help.cz/vdb/cwe/916/
https://www.cybersecurity-help.cz/vdb/cwe/327/
https://www.cybersecurity-help.cz/vdb/cwe/328/
https://www.section.io/engineering-education/what-is-md5/
https://www.johndcook.com/blog/2019/01/24/reversing-an-md5-hash/
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [**Enrocrypt's Official Repo**](http://www.github.com/Morgan-Phoenix/EnroCrypt)
* Create a Discussion in [**Enrocrypt's Official Repo**](http://www.github.com/Morgan-Phoenix/EnroCrypt)
| {'CVE-2021-39182'} | 2022-03-03T05:11:36.826995Z | 2021-11-10T16:28:46Z | HIGH | null | {'CWE-916', 'CWE-328', 'CWE-327'} | {'https://github.com/Morgan-Phoenix/EnroCrypt/security/advisories/GHSA-35m5-8cvj-8783', 'https://github.com/Morgan-Phoenix/EnroCrypt/commit/e652d56ac60eadfc26489ab83927af13a9b9d8ce', 'https://github.com/Morgan-Phoenix/EnroCrypt', 'https://nvd.nist.gov/vuln/detail/CVE-2021-39182'} | null | {'https://github.com/Morgan-Phoenix/EnroCrypt/commit/e652d56ac60eadfc26489ab83927af13a9b9d8ce'} | {'https://github.com/Morgan-Phoenix/EnroCrypt/commit/e652d56ac60eadfc26489ab83927af13a9b9d8ce'} |
PyPI | GHSA-fxqh-cfjm-fp93 | Division by 0 in `Reverse` | ### Impact
An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.Reverse`:
```python
import tensorflow as tf
tensor_input = tf.constant([], shape=[0, 1, 1], dtype=tf.int32)
dims = tf.constant([False, True, False], shape=[3], dtype=tf.bool)
tf.raw_ops.Reverse(tensor=tensor_input, dims=dims)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/36229ea9e9451dac14a8b1f4711c435a1d84a594/tensorflow/core/kernels/reverse_op.cc#L75-L76) performs a division based on the first dimension of the tensor argument:
```cc
const int64 N = input.dim_size(0);
const int64 cost_per_unit = input.NumElements() / N;
```
Since this is controlled by the user, an attacker can trigger a denial of service.
### Patches
We have patched the issue in GitHub commit [4071d8e2f6c45c1955a811fee757ca2adbe462c1](https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29556'} | 2022-03-03T05:13:10.573532Z | 2021-05-21T14:24:39Z | LOW | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29556', 'https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fxqh-cfjm-fp93'} | null | {'https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1'} | {'https://github.com/tensorflow/tensorflow/commit/4071d8e2f6c45c1955a811fee757ca2adbe462c1'} |
PyPI | GHSA-xc3p-ff3m-f46v | Insecure input handling in Flask-Cors | An issue was discovered in Flask-CORS (aka CORS Middleware for Flask) before 3.0.9. It allows ../ directory traversal to access private resources because resource matching does not ensure that pathnames are in a canonical format. | {'CVE-2020-25032'} | 2022-04-29T20:33:26.063099Z | 2021-05-06T18:51:48Z | HIGH | null | {'CWE-22'} | {'https://github.com/corydolphin/flask-cors/commit/67c4b2cc98ae87cf1fa7df4f97fd81b40c79b895', 'http://lists.opensuse.org/opensuse-security-announce/2020-09/msg00048.html', 'http://lists.opensuse.org/opensuse-security-announce/2020-09/msg00032.html', 'https://www.debian.org/security/2020/dsa-4775', 'https://github.com/corydolphin/flask-cors', 'https://nvd.nist.gov/vuln/detail/CVE-2020-25032', 'http://lists.opensuse.org/opensuse-security-announce/2020-09/msg00039.html', 'https://github.com/corydolphin/flask-cors/releases/tag/3.0.9', 'http://lists.opensuse.org/opensuse-security-announce/2020-09/msg00028.html'} | null | {'https://github.com/corydolphin/flask-cors/commit/67c4b2cc98ae87cf1fa7df4f97fd81b40c79b895'} | {'https://github.com/corydolphin/flask-cors/commit/67c4b2cc98ae87cf1fa7df4f97fd81b40c79b895'} |
PyPI | GHSA-wqvq-5m8c-6g24 | CRLF injection | urllib3 before 1.25.9 allows CRLF injection if the attacker controls the HTTP request method, as demonstrated by inserting CR and LF control characters in the first argument of putrequest(). NOTE: this is similar to CVE-2020-26116. | {'CVE-2020-26137'} | 2022-03-03T05:14:02.786960Z | 2021-06-18T18:46:43Z | MODERATE | null | {'CWE-74'} | {'https://bugs.python.org/issue39603', 'https://github.com/urllib3/urllib3/commit/1dd69c5c5982fae7c87a620d487c2ebf7a6b436b', 'https://github.com/urllib3/urllib3/pull/1800', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26137', 'https://www.oracle.com/security-alerts/cpuoct2021.html', 'https://lists.debian.org/debian-lts-announce/2021/06/msg00015.html', 'https://github.com/urllib3/urllib3', 'https://usn.ubuntu.com/4570-1/'} | null | {'https://github.com/urllib3/urllib3/commit/1dd69c5c5982fae7c87a620d487c2ebf7a6b436b'} | {'https://github.com/urllib3/urllib3/commit/1dd69c5c5982fae7c87a620d487c2ebf7a6b436b'} |
PyPI | GHSA-f865-m6cq-j9vx | ReDOS in Mpmath | A Regular Expression Denial of Service (ReDOS) vulnerability was discovered in Mpmath v1.0.0 when the mpmathify function is called. | {'CVE-2021-29063'} | 2022-03-03T05:12:52.232066Z | 2021-08-09T20:44:51Z | HIGH | null | {'CWE-770'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29063', 'https://github.com/fredrik-johansson/mpmath/issues/548', 'https://github.com/fredrik-johansson/mpmath/commit/c811b37c65a4372a7ce613111d2a508c204f9833', 'https://github.com/yetingli/PoCs/blob/main/CVE-2021-29063/Mpmath.md', 'https://www.npmjs.com/package/hosted-git-info', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MS2U6GLXQSRZJE2HVUAUMVFR2DWQLCZG/', 'https://github.com/npm/hosted-git-info/pull/76', 'https://github.com/yetingli/SaveResults/blob/main/js/hosted-git-info.js', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/3M5O55E7VUDMXCPQR6MQTOIFDKHP36AA/', 'https://github.com/fredrik-johansson/mpmath', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/EIUX3XWY2K3MSO7QXMZXQQYAURARSPC5/'} | null | {'https://github.com/fredrik-johansson/mpmath/commit/c811b37c65a4372a7ce613111d2a508c204f9833'} | {'https://github.com/fredrik-johansson/mpmath/commit/c811b37c65a4372a7ce613111d2a508c204f9833'} |
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