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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | PYSEC-2022-12 | null | IPython (Interactive Python) is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language. Affected versions are subject to an arbitrary code execution vulnerability achieved by not properly managing cross user temporary files. This vulnerability allows one user to run code as another on the same machine. All users are advised to upgrade. | {'GHSA-pq7m-3gw7-gq5x', 'CVE-2022-21699'} | 2022-01-26T19:22:31.675139Z | 2022-01-19T22:15:00Z | null | null | null | {'https://lists.debian.org/debian-lts-announce/2022/01/msg00021.html', 'https://github.com/ipython/ipython/security/advisories/GHSA-pq7m-3gw7-gq5x', 'https://ipython.readthedocs.io/en/stable/whatsnew/version8.html#ipython-8-0-1-cve-2022-21699', 'https://github.com/ipython/ipython/commit/46a51ed69cdf41b4333943d9ceeb945c4ede5668'} | null | {'https://github.com/ipython/ipython/commit/46a51ed69cdf41b4333943d9ceeb945c4ede5668'} | {'https://github.com/ipython/ipython/commit/46a51ed69cdf41b4333943d9ceeb945c4ede5668'} |
PyPI | PYSEC-2020-134 | null | In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code. | {'GHSA-cvpc-8phh-8f45', 'CVE-2020-15211'} | 2020-10-29T16:15:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9', 'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859'} | {'https://github.com/tensorflow/tensorflow/commit/00302787b788c5ff04cb6f62aed5a74d936e86c0', 'https://github.com/tensorflow/tensorflow/commit/1970c2158b1ffa416d159d03c3370b9a462aee35', 'https://github.com/tensorflow/tensorflow/commit/e11f55585f614645b360563072ffeb5c3eeff162', 'https://github.com/tensorflow/tensorflow/commit/fff2c8326280c07733828f990548979bdc893859', 'https://github.com/tensorflow/tensorflow/commit/cd31fd0ce0449a9e0f83dcad08d6ed7f1d6bef3f', 'https://github.com/tensorflow/tensorflow/commit/46d5b0852528ddfd614ded79bccc75589f801bd9'} |
PyPI | PYSEC-2021-837 | null | Invenio-Drafts-Resources is a submission/deposit module for Invenio, a software framework for research data management. Invenio-Drafts-Resources prior to versions 0.13.7 and 0.14.6 does not properly check permissions when a record is published. The vulnerability is exploitable in a default installation of InvenioRDM. An authenticated a user is able via REST API calls to publish draft records of other users if they know the record identifier and the draft validates (e.g. all require fields filled out). An attacker is not able to modify the data in the record, and thus e.g. *cannot* change a record from restricted to public. The problem is patched in Invenio-Drafts-Resources v0.13.7 and 0.14.6, which is part of InvenioRDM v6.0.1 and InvenioRDM v7.0 respectively. | {'CVE-2021-43781', 'GHSA-xr38-w74q-r8jv'} | 2021-12-10T06:37:24.185813Z | 2021-12-06T18:15:00Z | null | null | null | {'https://github.com/inveniosoftware/invenio-drafts-resources/security/advisories/GHSA-xr38-w74q-r8jv', 'https://github.com/inveniosoftware/invenio-drafts-resources/commit/039b0cff1ad4b952000f4d8c3a93f347108b6626'} | null | {'https://github.com/inveniosoftware/invenio-drafts-resources/commit/039b0cff1ad4b952000f4d8c3a93f347108b6626'} | {'https://github.com/inveniosoftware/invenio-drafts-resources/commit/039b0cff1ad4b952000f4d8c3a93f347108b6626'} |
PyPI | GHSA-2473-9hgq-j7xw | Cross-Site Scripting in Wagtail | ### Impact
When a form page type is made available to Wagtail editors through the `wagtail.contrib.forms` app, and the page template is built using Django's standard form rendering helpers such as `form.as_p` ([as directed in the documentation](https://docs.wagtail.io/en/stable/reference/contrib/forms/index.html#usage)), any HTML tags used within a form field's help text will be rendered unescaped in the page. Allowing HTML within help text is [an intentional design decision](https://docs.djangoproject.com/en/3.0/ref/models/fields/#django.db.models.Field.help_text) by Django; however, as a matter of policy Wagtail does not allow editors to insert arbitrary HTML by default, as this could potentially be used to carry out cross-site scripting attacks, including privilege escalation. This functionality should therefore not have been made available to editor-level users.
The vulnerability is not exploitable by an ordinary site visitor without access to the Wagtail admin.
### Patches
Patched versions have been released as Wagtail 2.7.4 (for the LTS 2.7 branch) and Wagtail 2.9.3 (for the current 2.9 branch). In these versions, help text will be escaped to prevent the inclusion of HTML tags. Site owners who wish to re-enable the use of HTML within help text (and are willing to accept the risk of this being exploited by editors) may set `WAGTAILFORMS_HELP_TEXT_ALLOW_HTML = True` in their configuration settings.
### Workarounds
Site owners who are unable to upgrade to the new versions can secure their form page templates by [rendering forms field-by-field as per Django's documentation](https://docs.djangoproject.com/en/3.0/topics/forms/#looping-over-the-form-s-fields), but omitting the `|safe` filter when outputting the help text.
### Acknowledgements
Many thanks to Timothy Bautista for reporting this issue.
### For more information
If you have any questions or comments about this advisory:
* Visit Wagtail's [support channels](https://docs.wagtail.io/en/stable/support.html)
* Email us at [security@wagtail.io](mailto:security@wagtail.io) (if you wish to send encrypted email, the public key ID is `0x6ba1e1a86e0f8ce8`) | {'CVE-2020-15118'} | 2022-03-03T05:13:59.610986Z | 2020-07-20T17:50:30Z | MODERATE | null | {'CWE-79'} | {'https://github.com/wagtail/wagtail/commit/d9a41e7f24d08c024acc9a3094940199df94db34', 'https://docs.djangoproject.com/en/3.0/ref/models/fields/#django.db.models.Field.help_text', 'https://github.com/wagtail/wagtail/security/advisories/GHSA-2473-9hgq-j7xw', 'https://docs.wagtail.io/en/stable/reference/contrib/forms/index.html#usage', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15118', 'https://github.com/wagtail/wagtail/blob/master/docs/releases/2.9.3.rst'} | null | {'https://github.com/wagtail/wagtail/commit/d9a41e7f24d08c024acc9a3094940199df94db34'} | {'https://github.com/wagtail/wagtail/commit/d9a41e7f24d08c024acc9a3094940199df94db34'} |
PyPI | GHSA-x9j7-x98r-r4w2 | Segmentation fault in tensorflow-lite | ### Impact
If a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption.
### Patches
We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
### Workarounds
A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that no operator reuses tensors as both inputs and outputs. Care should be taken to check all types of inputs (i.e., constant or variable tensors as well as optional tensors).
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been discovered from a variant analysis of [GHSA-cvpc-8phh-8f45](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvpc-8phh-8f45). | {'CVE-2020-15210'} | 2022-03-03T05:14:16.385167Z | 2020-09-25T18:28:51Z | MODERATE | null | {'CWE-20', 'CWE-787'} | {'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2020-15210', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453'} | {'https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453'} |
PyPI | PYSEC-2018-46 | null | Anymail django-anymail version version 0.2 through 1.3 contains a CWE-532, CWE-209 vulnerability in WEBHOOK_AUTHORIZATION setting value that can result in An attacker with access to error logs could fabricate email tracking events. This attack appear to be exploitable via If you have exposed your Django error reports, an attacker could discover your ANYMAIL_WEBHOOK setting and use this to post fabricated or malicious Anymail tracking/inbound events to your app. This vulnerability appears to have been fixed in v1.4. | {'CVE-2018-1000089'} | 2021-07-05T00:01:17.580240Z | 2018-03-13T15:29:00Z | null | null | null | {'https://github.com/anymail/django-anymail/releases/tag/v1.4', 'https://github.com/anymail/django-anymail/commit/1a6086f2b58478d71f89bf27eb034ed81aefe5ef'} | null | {'https://github.com/anymail/django-anymail/commit/1a6086f2b58478d71f89bf27eb034ed81aefe5ef'} | {'https://github.com/anymail/django-anymail/commit/1a6086f2b58478d71f89bf27eb034ed81aefe5ef'} |
PyPI | PYSEC-2021-134 | null | Synapse is a Matrix reference homeserver written in python (pypi package matrix-synapse). Matrix is an ecosystem for open federated Instant Messaging and VoIP. In Synapse before version 1.27.0, the notification emails sent for notifications for missed messages or for an expiring account are subject to HTML injection. In the case of the notification for missed messages, this could allow an attacker to insert forged content into the email. The account expiry feature is not enabled by default and the HTML injection is not controllable by an attacker. This is fixed in version 1.27.0. | {'GHSA-c5f8-35qr-q4fm', 'CVE-2021-21333'} | 2021-08-27T03:22:06.710356Z | 2021-03-26T20:15:00Z | null | null | null | {'https://github.com/matrix-org/synapse/commit/e54746bdf7d5c831eabe4dcea76a7626f1de73df', 'https://github.com/matrix-org/synapse/releases/tag/v1.27.0', 'https://github.com/matrix-org/synapse/pull/9200', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-c5f8-35qr-q4fm'} | null | {'https://github.com/matrix-org/synapse/commit/e54746bdf7d5c831eabe4dcea76a7626f1de73df'} | {'https://github.com/matrix-org/synapse/commit/e54746bdf7d5c831eabe4dcea76a7626f1de73df'} |
PyPI | PYSEC-2021-564 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. 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. We have patched the issue in GitHub 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. | {'GHSA-hpv4-7p9c-mvfr', 'CVE-2021-37651'} | 2021-12-09T06:35:03.344534Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr'} | null | {'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30'} | {'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30'} |
PyPI | GHSA-2r8p-fg3c-wcj4 | Heap OOB and CHECK fail in `ResourceGather` | ### Impact
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:
```python
import tensorflow as tf
tensor = tf.constant(value=[[1,2],[3,4],[5,6]],shape=(3,2),dtype=tf.uint32)
v = tf.Variable(tensor)
tf.raw_ops.ResourceGather(
resource=v.handle,
indices=[0],
dtype=tf.uint32,
batch_dims=10,
validate_indices=False)
```
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:
```cc
// batch_dims_ = > params.dims() (10 > 2)
for (int i = 0; i < batch_dims_; ++i) {
result_shape.AddDim(params.dim_size(i));
}
for (int i = batch_dims_; i < indices.dims(); ++i) {
result_shape.AddDim(indices.dim_size(i));
}
for (int i = batch_dims_ + 1; i < params.dims(); ++i) {
result_shape.AddDim(params.dim_size(i));
}
```
In debug mode, `.dim_size(i)` validates that the argument is less than `.dims()` using a `DCHECK`. But the `DCHECK` is a no-op in release builds.
### Patches
We have patched the issue in GitHub commit [bc9c546ce7015c57c2f15c168b3d9201de679a1d](https://github.com/tensorflow/tensorflow/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.
### 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-2021-37654'} | 2022-03-03T05:13:57.699257Z | 2021-08-25T14:43:01Z | HIGH | null | {'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37654', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2r8p-fg3c-wcj4', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/commit/bc9c546ce7015c57c2f15c168b3d9201de679a1d'} | null | {'https://github.com/tensorflow/tensorflow/commit/bc9c546ce7015c57c2f15c168b3d9201de679a1d'} | {'https://github.com/tensorflow/tensorflow/commit/bc9c546ce7015c57c2f15c168b3d9201de679a1d'} |
PyPI | PYSEC-2020-39 | null | Django Two-Factor Authentication before 1.12, stores the user's password in clear text in the user session (base64-encoded). The password is stored in the session when the user submits their username and password, and is removed once they complete authentication by entering a two-factor authentication code. This means that the password is stored in clear text in the session for an arbitrary amount of time, and potentially forever if the user begins the login process by entering their username and password and then leaves before entering their two-factor authentication code. The severity of this issue depends on which type of session storage you have configured: in the worst case, if you're using Django's default database session storage, then users' passwords are stored in clear text in your database. In the best case, if you're using Django's signed cookie session, then users' passwords are only stored in clear text within their browser's cookie store. In the common case of using Django's cache session store, the users' passwords are stored in clear text in whatever cache storage you have configured (typically Memcached or Redis). This has been fixed in 1.12. After upgrading, users should be sure to delete any clear text passwords that have been stored. For example, if you're using the database session backend, you'll likely want to delete any session record from the database and purge that data from any database backups or replicas. In addition, affected organizations who have suffered a database breach while using an affected version should inform their users that their clear text passwords have been compromised. All organizations should encourage users whose passwords were insecurely stored to change these passwords on any sites where they were used. As a workaround, wwitching Django's session storage to use signed cookies instead of the database or cache lessens the impact of this issue, but should not be done without a thorough understanding of the security tradeoffs of using signed cookies rather than a server-side session storage. There is no way to fully mitigate the issue without upgrading. | {'GHSA-vhr6-pvjm-9qwf', 'CVE-2020-15105'} | 2020-07-21T18:06:00Z | 2020-07-10T21:15:00Z | null | null | null | {'https://github.com/Bouke/django-two-factor-auth/blob/master/CHANGELOG.md#112---2020-07-08', 'https://github.com/Bouke/django-two-factor-auth/security/advisories/GHSA-vhr6-pvjm-9qwf', 'https://github.com/Bouke/django-two-factor-auth/commit/454fd9842fa6e8bb772dbf0943976bc8e3335359'} | null | {'https://github.com/Bouke/django-two-factor-auth/commit/454fd9842fa6e8bb772dbf0943976bc8e3335359'} | {'https://github.com/Bouke/django-two-factor-auth/commit/454fd9842fa6e8bb772dbf0943976bc8e3335359'} |
PyPI | PYSEC-2021-572 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37659', 'GHSA-q3g3-h9r4-prrc'} | 2021-12-09T06:35:04.007163Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q3g3-h9r4-prrc', 'https://github.com/tensorflow/tensorflow/commit/93f428fd1768df147171ed674fee1fc5ab8309ec'} | null | {'https://github.com/tensorflow/tensorflow/commit/93f428fd1768df147171ed674fee1fc5ab8309ec'} | {'https://github.com/tensorflow/tensorflow/commit/93f428fd1768df147171ed674fee1fc5ab8309ec'} |
PyPI | GHSA-5xp3-jfq3-5q8x | Improper Input Validation in pip | A flaw was found in python-pip in the way it handled Unicode separators in git references. A remote attacker could possibly use this issue to install a different revision on a repository. The highest threat from this vulnerability is to data integrity. This is fixed in python-pip version 21.1. | {'CVE-2021-3572'} | 2022-04-22T15:47:50.401286Z | 2021-11-15T17:45:01Z | MODERATE | null | {'CWE-20'} | {'https://www.oracle.com/security-alerts/cpuapr2022.html', 'https://github.com/pypa/pip', 'https://nvd.nist.gov/vuln/detail/CVE-2021-3572', 'https://access.redhat.com/errata/RHSA-2021:3254', 'https://bugzilla.redhat.com/show_bug.cgi?id=1962856', 'https://github.com/pypa/pip/commit/e46bdda9711392fec0c45c1175bae6db847cb30b', 'https://packetstormsecurity.com/files/162712/USN-4961-1.txt', 'https://github.com/pypa/pip/pull/9827'} | null | {'https://github.com/pypa/pip/commit/e46bdda9711392fec0c45c1175bae6db847cb30b'} | {'https://github.com/pypa/pip/commit/e46bdda9711392fec0c45c1175bae6db847cb30b'} |
PyPI | GHSA-m342-ff57-4jcc | Heap OOB read in `tf.raw_ops.SparseCountSparseOutput` | ### Impact
The [shape inference functions for `SparseCountSparseOutput`](https://github.com/tensorflow/tensorflow/blob/e0b6e58c328059829c3eb968136f17aa72b6c876/tensorflow/core/ops/count_ops.cc#L43-L50) can trigger a read outside of bounds of heap allocated array:
```python
import tensorflow as tf
@tf.function
def func():
return tf.raw_ops.SparseCountSparseOutput(
indices=[1],
values=[[1]],
dense_shape=[10],
weights=[],
binary_output= True)
func()
```
The function fails to check that the first input (i.e., `indices`) has rank 2:
```cc
auto rank = c->Dim(c->input(0), 1);
```
### Patches
We have patched the issue in GitHub commit [701cfaca222a82afbeeb17496bd718baa65a67d2](https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2).
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-2021-41210'} | 2022-03-03T05:14:12.419108Z | 2021-11-10T19:01:44Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m342-ff57-4jcc', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41210'} | null | {'https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2'} | {'https://github.com/tensorflow/tensorflow/commit/701cfaca222a82afbeeb17496bd718baa65a67d2'} |
PyPI | PYSEC-2021-821 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` has an undefined behavior due to binding a reference to `nullptr`. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-vwhq-49r4-gj9v', 'CVE-2021-41214'} | 2021-12-09T06:35:43.281523Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vwhq-49r4-gj9v'} | null | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} |
PyPI | GHSA-8h46-5m9h-7553 | Heap out of bounds write in `RaggedBinCount` | ### Impact
If the `splits` argument of `RaggedBincount` does not specify a valid [`SparseTensor`](https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow:
```python
import tensorflow as tf
tf.raw_ops.RaggedBincount(splits=[7,8], values= [5, 16, 51, 76, 29, 27, 54, 95],\
size= 59, weights= [0, 0, 0, 0, 0, 0, 0, 0],\
binary_output=False)
```
This will cause a read from outside the bounds of the `splits` tensor buffer in the [implementation of the `RaggedBincount` op](https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L446):
```cc
for (int idx = 0; idx < num_values; ++idx) {
while (idx >= splits(batch_idx)) {
batch_idx++;
}
...
if (bin < size) {
if (binary_output_) {
out(batch_idx - 1, bin) = T(1);
} else {
T value = (weights_size > 0) ? weights(idx) : T(1);
out(batch_idx - 1, bin) += value;
}
}
}
```
Before the `for` loop, `batch_idx` is set to 0. The attacker sets `splits(0)` to be 7, hence the `while` loop does not execute and `batch_idx` remains 0. This then results in writing to `out(-1, bin)`, which is before the heap allocated buffer for the output tensor.
### Patches
We have patched the issue in GitHub commit [eebb96c2830d48597d055d247c0e9aebaea94cd5](https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-29514'} | 2022-03-03T05:13:37.569995Z | 2021-05-21T14:20:51Z | LOW | null | {'CWE-787'} | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8h46-5m9h-7553', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29514'} | null | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} | {'https://github.com/tensorflow/tensorflow/commit/eebb96c2830d48597d055d247c0e9aebaea94cd5'} |
PyPI | PYSEC-2020-122 | null | In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the `splits` tensor has the minimum required number of elements. Code uses this quantity to initialize a different data structure. Since `BatchedMap` is equivalent to a vector, it needs to have at least one element to not be `nullptr`. If user passes a `splits` tensor that is empty or has exactly one element, we get a `SIGABRT` signal raised by the operating system. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'GHSA-x5cp-9pcf-pp3h', 'CVE-2020-15199'} | 2021-09-01T08:19:33.217572Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x5cp-9pcf-pp3h', 'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | PYSEC-2021-43 | null | Products.GenericSetup is a mini-framework for expressing the configured state of a Zope Site as a set of filesystem artifacts. In Products.GenericSetup before version 2.1.1 there is an information disclosure vulnerability - anonymous visitors may view log and snapshot files generated by the Generic Setup Tool. The problem has been fixed in version 2.1.1. Depending on how you have installed Products.GenericSetup, you should change the buildout version pin to 2.1.1 and re-run the buildout, or if you used pip simply do pip install `"Products.GenericSetup>=2.1.1"`. | {'GHSA-jff3-mwp3-f8cw', 'CVE-2021-21360'} | 2021-03-12T18:05:00Z | 2021-03-09T01:15:00Z | null | null | null | {'https://github.com/zopefoundation/Products.GenericSetup/commit/700319512b3615b3871a1f24e096cf66dc488c57', 'https://pypi.org/project/Products.GenericSetup/', 'https://github.com/zopefoundation/Products.GenericSetup/security/advisories/GHSA-jff3-mwp3-f8cw'} | null | {'https://github.com/zopefoundation/Products.GenericSetup/commit/700319512b3615b3871a1f24e096cf66dc488c57'} | {'https://github.com/zopefoundation/Products.GenericSetup/commit/700319512b3615b3871a1f24e096cf66dc488c57'} |
PyPI | PYSEC-2021-749 | null | TensorFlow is an end-to-end open source platform for machine learning. Sending invalid argument for `row_partition_types` of `tf.raw_ops.RaggedTensorToTensor` API results in a null pointer dereference and undefined behavior. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L328) accesses the first element of a user supplied list of values without validating that the provided list is not empty. We have patched the issue in GitHub commit 301ae88b331d37a2a16159b65b255f4f9eb39314. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-hwr7-8gxx-fj5p', 'CVE-2021-37638'} | 2021-12-09T06:35:35.577694Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hwr7-8gxx-fj5p'} | null | {'https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314'} | {'https://github.com/tensorflow/tensorflow/commit/301ae88b331d37a2a16159b65b255f4f9eb39314'} |
PyPI | GHSA-j47f-4232-hvv8 | Heap out of bounds read in `RaggedCross` | ### Impact
An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`:
```python
import tensorflow as tf
ragged_values = []
ragged_row_splits = []
sparse_indices = []
sparse_values = []
sparse_shape = []
dense_inputs_elem = tf.constant([], shape=[92, 0], dtype=tf.int64)
dense_inputs = [dense_inputs_elem]
input_order = "R"
hashed_output = False
num_buckets = 0
hash_key = 0
tf.raw_ops.RaggedCross(ragged_values=ragged_values,
ragged_row_splits=ragged_row_splits,
sparse_indices=sparse_indices,
sparse_values=sparse_values,
sparse_shape=sparse_shape,
dense_inputs=dense_inputs,
input_order=input_order,
hashed_output=hashed_output,
num_buckets=num_buckets,
hash_key=hash_key,
out_values_type=tf.int64,
out_row_splits_type=tf.int64)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments:
```cc
int next_ragged = 0;
int next_sparse = 0;
int next_dense = 0;
for (char c : input_order_) {
if (c == 'R') {
TF_RETURN_IF_ERROR(BuildRaggedFeatureReader(
ragged_values_list[next_ragged], ragged_splits_list[next_ragged],
features));
next_ragged++;
} else if (c == 'S') {
TF_RETURN_IF_ERROR(BuildSparseFeatureReader(
sparse_indices_list[next_sparse], sparse_values_list[next_sparse],
batch_size, features));
next_sparse++;
} else if (c == 'D') {
TF_RETURN_IF_ERROR(
BuildDenseFeatureReader(dense_list[next_dense++], features));
}
...
}
```
Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads.
### Patches
We have patched the issue in GitHub commit [44b7f486c0143f68b56c34e2d01e146ee445134a](https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a).
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-2021-29532'} | 2022-03-03T05:12:53.744101Z | 2021-05-21T14:22:17Z | LOW | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j47f-4232-hvv8', 'https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29532'} | null | {'https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a'} | {'https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a'} |
PyPI | PYSEC-2020-319 | null | In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling `tf.raw_ops.GetSessionHandle` or `tf.raw_ops.GetSessionHandleV2` results in a null pointer dereference In linked snippet, in eager mode, `ctx->session_state()` returns `nullptr`. Since code immediately dereferences this, we get a segmentation fault. The issue is patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-q8gv-q7wr-9jf8', 'CVE-2020-15204'} | 2021-12-09T06:35:13.891462Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8gv-q7wr-9jf8', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1'} | {'https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1'} |
PyPI | PYSEC-2021-460 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29532', 'GHSA-j47f-4232-hvv8'} | 2021-12-09T06:34:48.349137Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j47f-4232-hvv8', 'https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a'} | null | {'https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a'} | {'https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a'} |
PyPI | GHSA-wjw6-2cqr-j4qr | Client metadata path-traversal | ### Impact
In both clients (`tuf/client` and `tuf/ngclient`), there is a path traversal vulnerability that in the worst case can overwrite files ending in `.json` anywhere on the client system on a call to `get_one_valid_targetinfo()`. It occurs because the rolename is used to form the filename, and may contain path traversal characters (ie `../../name.json`).
The impact is mitigated by a few facts:
* It only affects implementations that allow arbitrary rolename selection for delegated targets metadata
* The attack requires the ability to A) insert new metadata for the path-traversing role and B) get the role delegated by an existing targets metadata
* The written file content is heavily restricted since it needs to be a valid, signed targets file. The file extension is always .json.
### Patches
A fix is available in version 0.19 or newer.
### Workarounds
None that do not require code changes. Clients can restrict the allowed character set for rolenames, or they can store metadata in files named in a way that is not vulnerable: neither of these approaches is possible without modifying python-tuf.
### References
- [The issue where this was discovered](https://github.com/theupdateframework/python-tuf/issues/1527)
- [Proof of Concept demonstrating the flaw](https://github.com/jku/path-traversal-poc)
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [python-tuf](https://github.com/theupdateframework/python-tuf/issues)
* Contact the maintainers by email or Slack
| {'CVE-2021-41131'} | 2022-03-03T05:13:15.399393Z | 2021-10-19T20:14:36Z | HIGH | null | {'CWE-22'} | {'https://github.com/theupdateframework/python-tuf/commit/4ad7ae48fda594b640139c3b7eae21ed5155a102', 'https://github.com/theupdateframework/python-tuf/issues/1527', 'https://github.com/theupdateframework/python-tuf/security/advisories/GHSA-wjw6-2cqr-j4qr', 'https://github.com/theupdateframework/python-tuf', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41131'} | null | {'https://github.com/theupdateframework/python-tuf/commit/4ad7ae48fda594b640139c3b7eae21ed5155a102'} | {'https://github.com/theupdateframework/python-tuf/commit/4ad7ae48fda594b640139c3b7eae21ed5155a102'} |
PyPI | PYSEC-2022-53 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `UnravelIndex` is vulnerable to a division by zero caused by an integer overflow bug. 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-34f9-hjfq-rr8j', 'CVE-2022-21729'} | 2022-03-09T00:17:30.437565Z | 2022-02-03T13:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-34f9-hjfq-rr8j', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/unravel_index_op.cc#L36-L135', 'https://github.com/tensorflow/tensorflow/commit/58b34c6c8250983948b5a781b426f6aa01fd47af'} | null | {'https://github.com/tensorflow/tensorflow/commit/58b34c6c8250983948b5a781b426f6aa01fd47af'} | {'https://github.com/tensorflow/tensorflow/commit/58b34c6c8250983948b5a781b426f6aa01fd47af'} |
PyPI | PYSEC-2020-81 | null | libImaging/TiffDecode.c in Pillow before 6.2.2 has a TIFF decoding integer overflow, related to realloc. | {'CVE-2020-5310', 'GHSA-vcqg-3p29-xw73'} | 2020-01-31T04:15:00Z | 2020-01-03T01:15:00Z | null | null | null | {'https://github.com/python-pillow/Pillow/commit/4e2def2539ec13e53a82e06c4b3daf00454100c4', 'https://usn.ubuntu.com/4272-1/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/2MMU3WT2X64GS5WHDPKKC2WZA7UIIQ3A/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/3DUMIBUYGJRAVJCTFUWBRLVQKOUTVX5P/', 'https://github.com/advisories/GHSA-vcqg-3p29-xw73', 'https://pillow.readthedocs.io/en/stable/releasenotes/6.2.2.html'} | null | {'https://github.com/python-pillow/Pillow/commit/4e2def2539ec13e53a82e06c4b3daf00454100c4'} | {'https://github.com/python-pillow/Pillow/commit/4e2def2539ec13e53a82e06c4b3daf00454100c4'} |
PyPI | GHSA-6g85-3hm8-83f9 | CHECK-fail in `QuantizeAndDequantizeV4Grad` | ### Impact
An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.QuantizeAndDequantizeV4Grad`:
```python
import tensorflow as tf
gradient_tensor = tf.constant([0.0], shape=[1])
input_tensor = tf.constant([0.0], shape=[1])
input_min = tf.constant([[0.0]], shape=[1, 1])
input_max = tf.constant([[0.0]], shape=[1, 1])
tf.raw_ops.QuantizeAndDequantizeV4Grad(
gradients=gradient_tensor, input=input_tensor,
input_min=input_min, input_max=input_max, axis=0)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to [`QuantizeAndDequantizePerChannelGradientImpl`](https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306):
```cc
template <typename Device, typename T>
struct QuantizeAndDequantizePerChannelGradientImpl {
static void Compute(const Device& d,
typename TTypes<T, 3>::ConstTensor gradient,
typename TTypes<T, 3>::ConstTensor input,
const Tensor* input_min_tensor,
const Tensor* input_max_tensor,
typename TTypes<T, 3>::Tensor input_backprop,
typename TTypes<T>::Flat input_min_backprop,
typename TTypes<T>::Flat input_max_backprop) {
...
auto input_min = input_min_tensor->vec<T>();
auto input_max = input_max_tensor->vec<T>();
...
}
```
However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK` failure otherwise.
### Patches
We have patched the issue in GitHub commit [20431e9044cf2ad3c0323c34888b192f3289af6b](https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team. | {'CVE-2021-29544'} | 2022-03-03T05:13:03.019061Z | 2021-05-21T14:23:22Z | LOW | null | {'CWE-754'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6g85-3hm8-83f9', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29544', 'https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b'} | null | {'https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b'} | {'https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b'} |
PyPI | PYSEC-2021-175 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for 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. | {'GHSA-j8qc-5fqr-52fp', 'CVE-2021-29538'} | 2021-08-27T03:22:28.180235Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp', 'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} | null | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96'} |
PyPI | PYSEC-2021-525 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `SpaceToBatchNd` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/412c7d9bb8f8a762c5b266c9e73bfa165f29aac8/tensorflow/lite/kernels/space_to_batch_nd.cc#L82-L83). An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-v52p-hfjf-wg88', 'CVE-2021-29597'} | 2021-12-09T06:34:58.435866Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/6d36ba65577006affb272335b7c1abd829010708', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v52p-hfjf-wg88'} | null | {'https://github.com/tensorflow/tensorflow/commit/6d36ba65577006affb272335b7c1abd829010708'} | {'https://github.com/tensorflow/tensorflow/commit/6d36ba65577006affb272335b7c1abd829010708'} |
PyPI | GHSA-rpx7-33j2-xx9x | Arbitrary file deletion in NeMo ASR webapp | ### Description
NVIDIA NeMo contains a vulnerability in ASR WebApp, where Relative Path Traversal (CWE-23) may lead to deletion of any directory through the "../" structure when admin privileges are available.
CVSS Score = 2.0
https://nvd.nist.gov/vuln-metrics/cvss/v3-calculator?vector=AV:L/AC:L/PR:H/UI:R/S:U/C:N/I:L/A:N
### Impact
When the optional tool ASR Webapp (https://github.com/NVIDIA/NeMo/tree/main/tools/asr_webapp) is used prior to commit https://github.com/NVIDIA/NeMo/commit/f7e4ed7e4f7f2fa43765a38c2fafa1b6d1ebd7c0, some interfaces do not verify user input. Malicious attackers can construct malicious requests to delete any directory through the "../" structure.
The vulnerability affects only cases where this particular web app is started by a user with superuser permissions. The web application is *not* a part of the regular pip release or the corresponding container, and impacts only users that clone the repository in its entirety and execute the web app (while also possessing superuser permissions).
## Patches
This vulnerability has been patched via https://github.com/NVIDIA/NeMo/commit/f7e4ed7e4f7f2fa43765a38c2fafa1b6d1ebd7c0, which is effectively any clone of the repository post r1.5.1 branch. Since the web app is not distributed via pip release or container, users who clone the main branch post this commit will possess the patch.
### Workarounds
The changes in commit https://github.com/NVIDIA/NeMo/commit/f7e4ed7e4f7f2fa43765a38c2fafa1b6d1ebd7c0 can be applied to any version of the web app without any adverse effect.
### Additional Information
Exploit Found on: 2021.12.16
Exploit Found by: @haby0
| null | 2022-04-15T19:46:57.518536Z | 2022-02-15T01:57:18Z | LOW | null | null | {'https://github.com/NVIDIA/NeMo', 'https://github.com/NVIDIA/NeMo/security/advisories/GHSA-rpx7-33j2-xx9x', 'https://github.com/NVIDIA/NeMo/commit/f7e4ed7e4f7f2fa43765a38c2fafa1b6d1ebd7c0'} | null | {'https://github.com/NVIDIA/NeMo/commit/f7e4ed7e4f7f2fa43765a38c2fafa1b6d1ebd7c0'} | {'https://github.com/NVIDIA/NeMo/commit/f7e4ed7e4f7f2fa43765a38c2fafa1b6d1ebd7c0'} |
PyPI | PYSEC-2021-461 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to validate user controlled inputs. Whereas `OP_REQUIRES` allows returning an error condition back to the user, the `CHECK_*` macros result in a crash if the condition is false, similar to `assert`. In this case, `height` is 0 from the `images` input. This results in `max_box_row_clamp` being negative and the assertion being falsified, followed by aborting program execution. 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-393f-2jr3-cp69', 'CVE-2021-29533'} | 2021-12-09T06:34:48.500169Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-393f-2jr3-cp69', 'https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87'} | null | {'https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87'} | {'https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87'} |
PyPI | PYSEC-2022-52 | 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'} | 2022-03-09T00:17:30.293594Z | 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-4w8p-x6g8-fv64 | Server-Side Request Forgery in calibreweb | calibreweb prior to version 0.6.16 contains a Server-Side Request Forgery (SSRF) vulnerability. | {'CVE-2022-0339'} | 2022-04-04T21:46:56.538317Z | 2022-02-01T00:48:54Z | MODERATE | null | {'CWE-918'} | {'https://github.com/janeczku/calibre-web/commit/35f6f4c727c887f8f3607fe3233dbc1980d15020', 'https://github.com/janeczku/calibre-web/commit/3b216bfa07ec7992eff03e55d61732af6df9bb92', 'https://nvd.nist.gov/vuln/detail/CVE-2022-0339', 'https://github.com/janeczku/calibre-web', 'https://huntr.dev/bounties/499688c4-6ac4-4047-a868-7922c3eab369', 'https://github.com/janeczku/calibre-web/releases/tag/0.6.16'} | null | {'https://github.com/janeczku/calibre-web/commit/3b216bfa07ec7992eff03e55d61732af6df9bb92', 'https://github.com/janeczku/calibre-web/commit/35f6f4c727c887f8f3607fe3233dbc1980d15020'} | {'https://github.com/janeczku/calibre-web/commit/3b216bfa07ec7992eff03e55d61732af6df9bb92', 'https://github.com/janeczku/calibre-web/commit/35f6f4c727c887f8f3607fe3233dbc1980d15020'} |
PyPI | PYSEC-2021-174 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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-8c89-2vwr-chcq', 'CVE-2021-29537'} | 2021-08-27T03:22:28.012732Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8c89-2vwr-chcq'} | null | {'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694'} | {'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694'} |
PyPI | PYSEC-2014-25 | null | The from_yaml method in serializers.py in Django Tastypie before 0.9.10 does not properly deserialize YAML data, which allows remote attackers to execute arbitrary Python code via vectors related to the yaml.load method. | {'CVE-2011-4104'} | 2021-07-25T23:34:28.733002Z | 2014-10-27T01:55:00Z | null | null | null | {'https://groups.google.com/forum/#!topic/django-tastypie/i2aNGDHTUBI', 'http://www.openwall.com/lists/oss-security/2011/11/02/7', 'https://www.djangoproject.com/weblog/2011/nov/01/piston-and-tastypie-security-releases/', 'http://www.openwall.com/lists/oss-security/2011/11/02/1', 'https://github.com/toastdriven/django-tastypie/commit/e8af315211b07c8f48f32a063233cc3f76dd5bc2'} | null | {'https://github.com/toastdriven/django-tastypie/commit/e8af315211b07c8f48f32a063233cc3f76dd5bc2'} | {'https://github.com/toastdriven/django-tastypie/commit/e8af315211b07c8f48f32a063233cc3f76dd5bc2'} |
PyPI | PYSEC-2021-524 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `EmbeddingLookup` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e4b29809543b250bc9b19678ec4776299dd569ba/tensorflow/lite/kernels/embedding_lookup.cc#L73-L74). An attacker can craft a model such that the first dimension of the `value` input is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-4vrf-ff7v-hpgr', 'CVE-2021-29596'} | 2021-12-09T06:34:58.287349Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4vrf-ff7v-hpgr'} | null | {'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e'} | {'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e'} |
PyPI | PYSEC-2021-748 | null | TensorFlow is an end-to-end open source platform for machine learning. It is possible to trigger a null pointer dereference in TensorFlow by passing an invalid input to `tf.raw_ops.CompressElement`. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/data/compression_utils.cc#L34) was accessing the size of a buffer obtained from the return of a separate function call before validating that said buffer is valid. We have patched the issue in GitHub commit 5dc7f6981fdaf74c8c5be41f393df705841fb7c5. 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-2021-37637', 'GHSA-c9qf-r67m-p7cg'} | 2021-12-09T06:35:35.492639Z | 2021-08-12T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5dc7f6981fdaf74c8c5be41f393df705841fb7c5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c9qf-r67m-p7cg'} | null | {'https://github.com/tensorflow/tensorflow/commit/5dc7f6981fdaf74c8c5be41f393df705841fb7c5'} | {'https://github.com/tensorflow/tensorflow/commit/5dc7f6981fdaf74c8c5be41f393df705841fb7c5'} |
PyPI | PYSEC-2020-318 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'GHSA-xmq7-7fxm-rr79', 'CVE-2020-15203'} | 2021-12-09T06:35:13.671465Z | 2020-09-25T19:15:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79', 'https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83'} | {'https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83'} |
PyPI | GHSA-6445-fm66-fvq2 | Integer overflows in Tensorflow | ### Impact
The [implementation of `AddManySparseToTensorsMap`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_tensors_map_ops.cc) is vulnerable to an integer overflow which results in a `CHECK`-fail when building new `TensorShape` objects (so, an assert failure based denial of service):
```python
import tensorflow as tf
import numpy as np
tf.raw_ops.AddManySparseToTensorsMap(
sparse_indices=[(0,0),(0,1),(0,2),(4,3),(5,0),(5,1)],
sparse_values=[1,1,1,1,1,1],
sparse_shape=[2**32,2**32],
container='',
shared_name='',
name=None)
```
We are missing some validation on the shapes of the input tensors as well as directly constructing a large `TensorShape` with user-provided dimensions. The latter is an instance of [TFSA-2021-198](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md) (CVE-2021-41197) and is easily fixed by replacing a call to `TensorShape` constructor with a call to `BuildTensorShape` static helper factory.
### Patches
We have patched the issue in GitHub commits [b51b82fe65ebace4475e3c54eb089c18a4403f1c](https://github.com/tensorflow/tensorflow/commit/b51b82fe65ebace4475e3c54eb089c18a4403f1c) and [a68f68061e263a88321c104a6c911fe5598050a8](https://github.com/tensorflow/tensorflow/commit/a68f68061e263a88321c104a6c911fe5598050a8).
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-2022-23568'} | 2022-03-03T05:13:28.738493Z | 2022-02-09T23:39:11Z | MODERATE | null | {'CWE-190'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-23568', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/sparse_tensors_map_ops.cc', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/b51b82fe65ebace4475e3c54eb089c18a4403f1c', 'https://github.com/tensorflow/tensorflow/commit/a68f68061e263a88321c104a6c911fe5598050a8', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6445-fm66-fvq2'} | null | {'https://github.com/tensorflow/tensorflow/commit/b51b82fe65ebace4475e3c54eb089c18a4403f1c', 'https://github.com/tensorflow/tensorflow/commit/a68f68061e263a88321c104a6c911fe5598050a8'} | {'https://github.com/tensorflow/tensorflow/commit/b51b82fe65ebace4475e3c54eb089c18a4403f1c', 'https://github.com/tensorflow/tensorflow/commit/a68f68061e263a88321c104a6c911fe5598050a8'} |
PyPI | PYSEC-2021-436 | null | Synapse is a package for Matrix homeservers written in Python 3/Twisted. Prior to version 1.47.1, Synapse instances with the media repository enabled can be tricked into downloading a file from a remote server into an arbitrary directory. No authentication is required for the affected endpoint. The last 2 directories and file name of the path are chosen randomly by Synapse and cannot be controlled by an attacker, which limits the impact. Homeservers with the media repository disabled are unaffected. Homeservers with a federation whitelist are also unaffected, since Synapse will check the remote hostname, including the trailing `../`s, against the whitelist. Server administrators should upgrade to 1.47.1 or later. Server administrators using a reverse proxy could, at the expense of losing media functionality, may block the certain endpoints as a workaround. Alternatively, non-containerized deployments can be adapted to use the hardened systemd config. | {'CVE-2021-41281', 'GHSA-3hfw-x7gx-437c'} | 2021-11-29T23:11:29.870597Z | 2021-11-23T20:15:00Z | null | null | null | {'https://github.com/matrix-org/synapse/security/advisories/GHSA-3hfw-x7gx-437c', 'https://github.com/matrix-org/synapse/commit/91f2bd090', 'https://github.com/matrix-org/synapse/releases/tag/v1.47.1'} | null | {'https://github.com/matrix-org/synapse/commit/91f2bd090'} | {'https://github.com/matrix-org/synapse/commit/91f2bd090'} |
PyPI | PYSEC-2021-573 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if `x` and `v` are empty but the code uses `||` instead of `&&`. We have patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-cm5x-837x-jf3c', 'CVE-2021-37660'} | 2021-12-09T06:35:04.091418Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cm5x-837x-jf3c', 'https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618'} | null | {'https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618'} | {'https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618'} |
PyPI | PYSEC-2018-51 | null | An issue was discovered in Bleach 2.1.x before 2.1.3. Attributes that have URI values weren't properly sanitized if the values contained character entities. Using character entities, it was possible to construct a URI value with a scheme that was not allowed that would slide through unsanitized. | {'GHSA-m9mq-p2f9-cfqv', 'CVE-2018-7753'} | 2021-07-15T02:22:07.141398Z | 2018-03-07T23:29:00Z | null | null | null | {'https://bugs.debian.org/892252', 'https://github.com/advisories/GHSA-m9mq-p2f9-cfqv', 'https://github.com/mozilla/bleach/commit/c5df5789ec3471a31311f42c2d19fc2cf21b35ef', 'https://github.com/mozilla/bleach/releases/tag/v2.1.3'} | null | {'https://github.com/mozilla/bleach/commit/c5df5789ec3471a31311f42c2d19fc2cf21b35ef'} | {'https://github.com/mozilla/bleach/commit/c5df5789ec3471a31311f42c2d19fc2cf21b35ef'} |
PyPI | GHSA-2xgj-xhgf-ggjv | Heap buffer overflow in `BandedTriangularSolve` | ### Impact
An attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`:
```python
import tensorflow as tf
import numpy as np
matrix_array = np.array([])
matrix_tensor = tf.convert_to_tensor(np.reshape(matrix_array,(0,1)),dtype=tf.float32)
rhs_array = np.array([1,1])
rhs_tensor = tf.convert_to_tensor(np.reshape(rhs_array,(1,2)),dtype=tf.float32)
tf.raw_ops.BandedTriangularSolve(matrix=matrix_tensor,rhs=rhs_tensor)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty:
```cc
void ValidateInputTensors(OpKernelContext* ctx, const Tensor& in0, const Tensor& in1) {
OP_REQUIRES(
ctx, in0.dims() >= 2,
errors::InvalidArgument("In[0] ndims must be >= 2: ", in0.dims()));
OP_REQUIRES(
ctx, in1.dims() >= 2,
errors::InvalidArgument("In[1] ndims must be >= 2: ", in1.dims()));
}
```
Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen [in this op's implementation](https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective.
### Patches
We have patched the issue in GitHub commit [ba6822bd7b7324ba201a28b2f278c29a98edbef2](https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2) followed by GitHub commit [0ab290774f91a23bebe30a358fde4e53ab4876a0](https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0).
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 Ye Zhang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29612'} | 2022-03-03T05:12:03.570252Z | 2021-05-21T14:28:37Z | LOW | null | {'CWE-120'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29612', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2xgj-xhgf-ggjv', 'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2', 'https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0'} | null | {'https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0', 'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2'} | {'https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2', 'https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0'} |
PyPI | PYSEC-2021-820 | null | TensorFlow is an open source platform for machine learning. In affected versions the code behind `tf.function` API can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive. This occurs due to using a non-reentrant `Lock` Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-h67m-xg8f-fxcf', 'CVE-2021-41213'} | 2021-12-09T06:35:43.109542Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf', 'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7'} | null | {'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7'} | {'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7'} |
PyPI | PYSEC-2020-123 | 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'} | 2021-09-01T08:19:33.281926Z | 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 | GHSA-c87f-fq5g-63r2 | Cross-site scripting in Unicorn framework | The Unicorn framework through 0.35.3 for Django allows XSS via component.name. | {'CVE-2021-42053'} | 2022-03-03T05:14:06.409650Z | 2021-10-12T17:51:11Z | MODERATE | null | {'CWE-79'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-42053', 'http://packetstormsecurity.com/files/164442/django-unicorn-0.35.3-Cross-Site-Scripting.html', 'https://github.com/adamghill/django-unicorn', 'https://github.com/adamghill/django-unicorn/compare/0.35.3...0.36.0', 'https://github.com/adamghill/django-unicorn/pull/288/commits/aa5b9835d946bd9893ef02e556859e3ea62cc5e2'} | null | {'https://github.com/adamghill/django-unicorn/pull/288/commits/aa5b9835d946bd9893ef02e556859e3ea62cc5e2'} | {'https://github.com/adamghill/django-unicorn/pull/288/commits/aa5b9835d946bd9893ef02e556859e3ea62cc5e2'} |
PyPI | GHSA-2647-c639-qv2j | Server-Side Request Forgery in calibreweb | calibreweb prior to version 0.6.17 is vulnerable to server-side request forgery (SSRF). This is due to an incomplete fix for [CVE-2022-0339](https://github.com/advisories/GHSA-4w8p-x6g8-fv64). The blacklist does not check for `0.0.0.0`, which would result in a payload of `0.0.0.0` resolving to `localhost`. | {'CVE-2022-0766'} | 2022-03-18T21:32:04.972354Z | 2022-03-08T00:00:31Z | CRITICAL | null | {'CWE-918'} | {'https://huntr.dev/bounties/7f2a5bb4-e6c7-4b6a-b8eb-face9e3add7b', 'https://github.com/janeczku/calibre-web/commit/965352c8d96c9eae7a6867ff76b0db137d04b0b8', 'https://github.com/janeczku/calibre-web', 'https://nvd.nist.gov/vuln/detail/CVE-2022-0766'} | null | {'https://github.com/janeczku/calibre-web/commit/965352c8d96c9eae7a6867ff76b0db137d04b0b8'} | {'https://github.com/janeczku/calibre-web/commit/965352c8d96c9eae7a6867ff76b0db137d04b0b8'} |
PyPI | GHSA-r4c4-5fpq-56wg | Heap OOB in boosted trees | ### Impact
An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`:
```python
import tensorflow as tf
tf.raw_ops.BoostedTreesSparseCalculateBestFeatureSplit(
node_id_range=[0,10],
stats_summary_indices=[[1, 2, 3, 0x1000000]],
stats_summary_values=[1.0],
stats_summary_shape=[1,1,1,1],
l1=l2=[1.0],
tree_complexity=[0.5],
min_node_weight=[1.0],
logits_dimension=3,
split_type='inequality')
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range.
### Patches
We have patched the issue in GitHub commit [e84c975313e8e8e38bb2ea118196369c45c51378](https://github.com/tensorflow/tensorflow/commit/e84c975313e8e8e38bb2ea118196369c45c51378).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### 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-2021-37664'} | 2022-03-03T05:13:33.318020Z | 2021-08-25T14:42:20Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r4c4-5fpq-56wg', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37664', 'https://github.com/tensorflow/tensorflow/commit/e84c975313e8e8e38bb2ea118196369c45c51378'} | null | {'https://github.com/tensorflow/tensorflow/commit/e84c975313e8e8e38bb2ea118196369c45c51378'} | {'https://github.com/tensorflow/tensorflow/commit/e84c975313e8e8e38bb2ea118196369c45c51378'} |
PyPI | GHSA-hc6c-75p4-hmq4 | Reference binding to null pointer in `MatrixDiag*` ops | ### Impact
The implementation of [`MatrixDiag*` operations](https://github.com/tensorflow/tensorflow/blob/4c4f420e68f1cfaf8f4b6e8e3eb857e9e4c3ff33/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L195-L197) does not validate that the tensor arguments are non-empty:
```cc
num_rows = context->input(2).flat<int32>()(0);
num_cols = context->input(3).flat<int32>()(0);
padding_value = context->input(4).flat<T>()(0);
```
Thus, users can trigger null pointer dereferences if any of the above tensors are null:
```python
import tensorflow as tf
d = tf.convert_to_tensor([],dtype=tf.float32)
p = tf.convert_to_tensor([],dtype=tf.float32)
tf.raw_ops.MatrixDiagV2(diagonal=d, k=0, num_rows=0, num_cols=0, padding_value=p)
```
Changing from `tf.raw_ops.MatrixDiagV2` to `tf.raw_ops.MatrixDiagV3` still reproduces the issue.
### Patches
We have patched the issue in GitHub commit [a7116dd3913c4a4afd2a3a938573aa7c785fdfc6](https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6).
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 Ye Zhang and Yakun Zhang of Baidu X-Team. | {'CVE-2021-29515'} | 2022-03-03T05:13:41.020454Z | 2021-05-21T14:20:54Z | LOW | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hc6c-75p4-hmq4', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29515'} | null | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6'} | {'https://github.com/tensorflow/tensorflow/commit/a7116dd3913c4a4afd2a3a938573aa7c785fdfc6'} |
PyPI | GHSA-v82p-hv3v-p6qp | Incomplete validation in MKL requantization | ### Impact
Due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays:
```python
import tensorflow as tf
tf.raw_ops.RequantizationRangePerChannel(
input=[],
input_min=[0,0,0,0,0],
input_max=[1,1,1,1,1],
clip_value_max=1)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the `input` tensor.
A similar issue occurs in `MklRequantizePerChannelOp`:
```python
import tensorflow as tf
from tensorflow.python.ops import gen_math_ops
gen_math_ops.requantize_per_channel(
input=[],
input_min=[-100,-100,-100,-100,-100],
input_max=[-100,-100,-100],
requested_output_min=[-100,-100,-100,-100,-100],
requested_output_max=[],
out_type=tf.int)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments.
### Patches
We have patched the issue in GitHub commit [9e62869465573cb2d9b5053f1fa02a81fce21d69](https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69) and in the Github commit [203214568f5bc237603dbab6e1fd389f1572f5c9](https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### 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-2021-37665'} | 2022-03-03T05:13:28.232290Z | 2021-08-25T14:42:16Z | HIGH | null | {'CWE-20'} | {'https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v82p-hv3v-p6qp', 'https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37665'} | null | {'https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69', 'https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9'} | {'https://github.com/tensorflow/tensorflow/commit/203214568f5bc237603dbab6e1fd389f1572f5c9', 'https://github.com/tensorflow/tensorflow/commit/9e62869465573cb2d9b5053f1fa02a81fce21d69'} |
PyPI | PYSEC-2013-21 | null | The is_safe_url function in utils/http.py in Django 1.4.x before 1.4.6, 1.5.x before 1.5.2, and 1.6 before beta 2 treats a URL's scheme as safe even if it is not HTTP or HTTPS, which might introduce cross-site scripting (XSS) or other vulnerabilities into Django applications that use this function, as demonstrated by "the login view in django.contrib.auth.views" and the javascript: scheme. | {'CVE-2013-6044'} | 2021-07-15T02:22:09.088856Z | 2013-10-04T17:55:00Z | null | null | null | {'http://secunia.com/advisories/54476', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/86437', 'https://github.com/django/django/commit/ec67af0bd609c412b76eaa4cc89968a2a8e5ad6a', 'http://www.debian.org/security/2013/dsa-2740', 'http://lists.opensuse.org/opensuse-updates/2013-10/msg00015.html', 'http://seclists.org/oss-sec/2013/q3/411', 'https://github.com/django/django/commit/1a274ccd6bc1afbdac80344c9b6e5810c1162b5f', 'http://www.securityfocus.com/bid/61777', 'http://www.securitytracker.com/id/1028915', 'http://rhn.redhat.com/errata/RHSA-2013-1521.html', 'https://www.djangoproject.com/weblog/2013/aug/13/security-releases-issued', 'http://seclists.org/oss-sec/2013/q3/369', 'https://github.com/django/django/commit/ae3535169af804352517b7fea94a42a1c9c4b762'} | null | {'https://github.com/django/django/commit/ec67af0bd609c412b76eaa4cc89968a2a8e5ad6a', 'https://github.com/django/django/commit/ae3535169af804352517b7fea94a42a1c9c4b762', 'https://github.com/django/django/commit/1a274ccd6bc1afbdac80344c9b6e5810c1162b5f'} | {'https://github.com/django/django/commit/ec67af0bd609c412b76eaa4cc89968a2a8e5ad6a', 'https://github.com/django/django/commit/ae3535169af804352517b7fea94a42a1c9c4b762', 'https://github.com/django/django/commit/1a274ccd6bc1afbdac80344c9b6e5810c1162b5f'} |
PyPI | GHSA-hm45-mgqm-gjm4 | Remote Code Execution (RCE) Exploit on Cross Site Scripting (XSS) Vulnerability | ### Impact
A RCE exploit has been discovered in the Red Discord Bot - Dashboard Webserver: this exploit allows Discord users with specially crafted Server names and Usernames/Nicknames to inject code into the webserver front-end code. By abusing this exploit, it's possible to perform destructive actions and/or access sensitive information.
### Patches
This high severity exploit has been fixed on version `0.1.7a`.
### Workarounds
There are no workarounds, bot owners must upgrade their relevant packages (Dashboard module and Dashboard webserver) in order to patch this issue
### References
- 99d88b8
- a6b9785
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Cog-Creators/Red-Dashboard](https://github.com/Cog-Creators/Red-Dashboard/issues/new/choose)
* Over on the official [Red Server](https://discord.gg/red) or at the Third Party Server [Toxic Layer](https://discord.gg/vQZTdB9) | {'CVE-2020-26249'} | 2022-03-03T05:13:57.306089Z | 2020-12-08T23:55:54Z | HIGH | null | {'CWE-79'} | {'https://github.com/Cog-Creators/Red-Dashboard/security/advisories/GHSA-hm45-mgqm-gjm4', 'https://github.com/Cog-Creators/Red-Dashboard/commit/a6b9785338003ec87fb75305e7d1cc2d40c7ab91', 'https://pypi.org/project/Red-Dashboard', 'https://nvd.nist.gov/vuln/detail/CVE-2020-26249', 'https://github.com/Cog-Creators/Red-Dashboard/commit/99d88b840674674166ce005b784ae8e31e955ab1'} | null | {'https://github.com/Cog-Creators/Red-Dashboard/commit/a6b9785338003ec87fb75305e7d1cc2d40c7ab91', 'https://github.com/Cog-Creators/Red-Dashboard/commit/99d88b840674674166ce005b784ae8e31e955ab1'} | {'https://github.com/Cog-Creators/Red-Dashboard/commit/99d88b840674674166ce005b784ae8e31e955ab1', 'https://github.com/Cog-Creators/Red-Dashboard/commit/a6b9785338003ec87fb75305e7d1cc2d40c7ab91'} |
PyPI | PYSEC-2021-420 | null | TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's `saved_model_cli` tool is vulnerable to a code injection as it calls `eval` on user supplied strings. This can be used by attackers to run arbitrary code on the plaform where the CLI tool runs. However, given that the tool is always run manually, the impact of this is not severe. We have patched this by adding a `safe` flag which defaults to `True` and an explicit warning for users. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-3rcw-9p9x-582v', 'CVE-2021-41228'} | 2021-11-13T06:52:46.380831Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3rcw-9p9x-582v'} | null | {'https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7'} | {'https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7'} |
PyPI | PYSEC-2022-13 | null | Wagtail is a Django based content management system focused on flexibility and user experience. When notifications for new replies in comment threads are sent, they are sent to all users who have replied or commented anywhere on the site, rather than only in the relevant threads. This means that a user could listen in to new comment replies on pages they have not have editing access to, as long as they have left a comment or reply somewhere on the site. A patched version has been released as Wagtail 2.15.2, which restores the intended behaviour - to send notifications for new replies to the participants in the active thread only (editing permissions are not considered). New comments can be disabled by setting `WAGTAILADMIN_COMMENTS_ENABLED = False` in the Django settings file. | {'GHSA-xqxm-2rpm-3889', 'CVE-2022-21683'} | 2022-01-26T19:22:48.977586Z | 2022-01-18T18:15:00Z | null | null | null | {'https://github.com/wagtail/wagtail/commit/5fe901e5d86ed02dbbb63039a897582951266afd', 'https://github.com/wagtail/wagtail/security/advisories/GHSA-xqxm-2rpm-3889', 'https://github.com/wagtail/wagtail/releases/tag/v2.15.2'} | null | {'https://github.com/wagtail/wagtail/commit/5fe901e5d86ed02dbbb63039a897582951266afd'} | {'https://github.com/wagtail/wagtail/commit/5fe901e5d86ed02dbbb63039a897582951266afd'} |
PyPI | PYSEC-2020-135 | null | In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. | {'GHSA-hx2x-85gr-wrpq', 'CVE-2020-15212'} | 2020-10-01T18:20:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq', 'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} |
PyPI | PYSEC-2021-836 | null | Invenio-Drafts-Resources is a submission/deposit module for Invenio, a software framework for research data management. Invenio-Drafts-Resources prior to versions 0.13.7 and 0.14.6 does not properly check permissions when a record is published. The vulnerability is exploitable in a default installation of InvenioRDM. An authenticated a user is able via REST API calls to publish draft records of other users if they know the record identifier and the draft validates (e.g. all require fields filled out). An attacker is not able to modify the data in the record, and thus e.g. *cannot* change a record from restricted to public. The problem is patched in Invenio-Drafts-Resources v0.13.7 and 0.14.6, which is part of InvenioRDM v6.0.1 and InvenioRDM v7.0 respectively. | {'CVE-2021-43781', 'GHSA-xr38-w74q-r8jv'} | 2021-12-09T07:29:22.280279Z | 2021-12-06T18:15:00Z | null | null | null | {'https://github.com/inveniosoftware/invenio-drafts-resources/security/advisories/GHSA-xr38-w74q-r8jv', 'https://github.com/inveniosoftware/invenio-drafts-resources/commit/039b0cff1ad4b952000f4d8c3a93f347108b6626'} | null | {'https://github.com/inveniosoftware/invenio-drafts-resources/commit/039b0cff1ad4b952000f4d8c3a93f347108b6626'} | {'https://github.com/inveniosoftware/invenio-drafts-resources/commit/039b0cff1ad4b952000f4d8c3a93f347108b6626'} |
PyPI | PYSEC-2018-47 | null | Cross-site scripting (XSS) vulnerability in the link dialogue in GUI editor in MoinMoin before 1.9.10 allows remote attackers to inject arbitrary web script or HTML via unspecified vectors. | {'GHSA-42fp-4hm3-j8r7', 'CVE-2017-5934'} | 2021-07-05T00:01:23.080305Z | 2018-10-15T19:29:00Z | null | null | null | {'https://www.debian.org/security/2018/dsa-4318', 'http://lists.opensuse.org/opensuse-security-announce/2018-10/msg00024.html', 'https://github.com/advisories/GHSA-42fp-4hm3-j8r7', 'https://github.com/moinwiki/moin-1.9/commit/70955a8eae091cc88fd9a6e510177e70289ec024', 'http://moinmo.in/SecurityFixes', 'https://lists.debian.org/debian-lts-announce/2018/10/msg00007.html', 'https://usn.ubuntu.com/3794-1/'} | null | {'https://github.com/moinwiki/moin-1.9/commit/70955a8eae091cc88fd9a6e510177e70289ec024'} | {'https://github.com/moinwiki/moin-1.9/commit/70955a8eae091cc88fd9a6e510177e70289ec024'} |
PyPI | PYSEC-2021-135 | null | Synapse is a Matrix reference homeserver written in python (pypi package matrix-synapse). Matrix is an ecosystem for open federated Instant Messaging and VoIP. In Synapse before version 1.33.2 "Push rules" can specify conditions under which they will match, including `event_match`, which matches event content against a pattern including wildcards. Certain patterns can cause very poor performance in the matching engine, leading to a denial-of-service when processing moderate length events. The issue is patched in version 1.33.2. A potential workaround might be to prevent users from making custom push rules, by blocking such requests at a reverse-proxy. | {'GHSA-x345-32rc-8h85', 'CVE-2021-29471'} | 2021-08-27T03:22:06.756710Z | 2021-05-11T15:15:00Z | null | null | null | {'https://github.com/matrix-org/synapse/security/advisories/GHSA-x345-32rc-8h85', 'https://github.com/matrix-org/synapse/releases/tag/v1.33.2', 'https://github.com/matrix-org/synapse/commit/03318a766cac9f8b053db2214d9c332a977d226c'} | null | {'https://github.com/matrix-org/synapse/commit/03318a766cac9f8b053db2214d9c332a977d226c'} | {'https://github.com/matrix-org/synapse/commit/03318a766cac9f8b053db2214d9c332a977d226c'} |
PyPI | GHSA-hxmp-pqch-c8mm | Denial of service attack via incorrect parameters in Matrix Synapse | ### Impact
A malicious or poorly-implemented homeserver can inject malformed events into a room by specifying a different room id in the path of a `/send_join`, `/send_leave`, `/invite` or `/exchange_third_party_invite` request.
This can lead to a denial of service in which future events will not be correctly sent to other servers over federation.
This affects any server which accepts federation requests from untrusted servers.
### Patches
Issue is resolved by https://github.com/matrix-org/synapse/pull/8776.
### Workarounds
Homeserver administrators could limit access to the federation API to trusted servers (for example via `federation_domain_whitelist`). | {'CVE-2020-26257'} | 2022-03-03T05:12:45.625321Z | 2020-12-09T18:21:29Z | LOW | null | {'CWE-400', 'CWE-74', 'CWE-79'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-26257', 'https://github.com/matrix-org/synapse/pull/8776', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-hxmp-pqch-c8mm', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/QR4MMYZKX5N5GYGH4H5LBUUC5TLAFHI7/', 'https://github.com/matrix-org/synapse/blob/develop/CHANGES.md#synapse-1231-2020-12-09', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DBTIU3ZNBFWZ56V4X7JIAD33V5H2GOMC/', 'https://github.com/matrix-org/synapse/commit/3ce2f303f15f6ac3dc352298972dc6e04d9b7a8b'} | null | {'https://github.com/matrix-org/synapse/commit/3ce2f303f15f6ac3dc352298972dc6e04d9b7a8b'} | {'https://github.com/matrix-org/synapse/commit/3ce2f303f15f6ac3dc352298972dc6e04d9b7a8b'} |
PyPI | PYSEC-2021-565 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.BoostedTreesCreateEnsemble` can result in a use after free error if an attacker supplies specially crafted arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent `free`-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed. We have patched the issue in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37652', 'GHSA-m7fm-4jfh-jrg6'} | 2021-12-09T06:35:03.429357Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m7fm-4jfh-jrg6'} | null | {'https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab'} | {'https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab'} |
PyPI | GHSA-83rh-hx5x-q9p5 | Out-of-bounds Read in OpenCV | In OpenCV 3.3.1 (corresponding with OpenCV-Python 3.3.1.11), a heap-based buffer over-read exists in the function cv::HdrDecoder::checkSignature in modules/imgcodecs/src/grfmt_hdr.cpp. | {'CVE-2017-18009'} | 2022-03-03T05:13:35.314834Z | 2021-10-12T22:03:23Z | HIGH | null | {'CWE-125'} | {'http://www.securityfocus.com/bid/106945', 'https://github.com/opencv/opencv/issues/10479', 'https://nvd.nist.gov/vuln/detail/CVE-2017-18009', 'https://github.com/opencv/opencv/pull/10480/commits/4ca89db22dea962690f31c1781bce5937ee91837', 'https://github.com/opencv/opencv-python'} | null | {'https://github.com/opencv/opencv/pull/10480/commits/4ca89db22dea962690f31c1781bce5937ee91837'} | {'https://github.com/opencv/opencv/pull/10480/commits/4ca89db22dea962690f31c1781bce5937ee91837'} |
PyPI | GHSA-75f6-78jr-4656 | Null pointer dereference in `EditDistance` | ### Impact
An attacker can trigger a null pointer dereference in the implementation of `tf.raw_ops.EditDistance`:
```python
import tensorflow as tf
hypothesis_indices = tf.constant([247, 247, 247], shape=[1, 3], dtype=tf.int64)
hypothesis_values = tf.constant([-9.9999], shape=[1], dtype=tf.float32)
hypothesis_shape = tf.constant([0, 0, 0], shape=[3], dtype=tf.int64)
truth_indices = tf.constant([], shape=[0, 3], dtype=tf.int64)
truth_values = tf.constant([], shape=[0], dtype=tf.float32)
truth_shape = tf.constant([0, 0, 0], shape=[3], dtype=tf.int64)
tf.raw_ops.EditDistance(
hypothesis_indices=hypothesis_indices, hypothesis_values=hypothesis_values,
hypothesis_shape=hypothesis_shape, truth_indices=truth_indices,
truth_values=truth_values, truth_shape=truth_shape, normalize=True)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/79865b542f9ffdc9caeb255631f7c56f1d4b6517/tensorflow/core/kernels/edit_distance_op.cc#L103-L159) has incomplete validation of the input parameters.
In the above scenario, an attacker causes an allocation of an empty tensor for the output:
```cc
OP_REQUIRES_OK(ctx, ctx->allocate_output("output", output_shape, &output));
auto output_t = output->flat<float>();
output_t.setZero();
```
Because `output_shape` has 0 elements, the result of `output->flat<T>()` has an empty buffer, so calling `setZero` would result in a null dereference.
### Patches
We have patched the issue in GitHub commit [f4c364a5d6880557f6f5b6eb5cee2c407f0186b3](https://github.com/tensorflow/tensorflow/commit/f4c364a5d6880557f6f5b6eb5cee2c407f0186b3).
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-2021-29564'} | 2022-03-03T05:12:50.250682Z | 2021-05-21T14:25:08Z | LOW | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-75f6-78jr-4656', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29564', 'https://github.com/tensorflow/tensorflow/commit/f4c364a5d6880557f6f5b6eb5cee2c407f0186b3'} | null | {'https://github.com/tensorflow/tensorflow/commit/f4c364a5d6880557f6f5b6eb5cee2c407f0186b3'} | {'https://github.com/tensorflow/tensorflow/commit/f4c364a5d6880557f6f5b6eb5cee2c407f0186b3'} |
PyPI | PYSEC-2021-709 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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-9xh4-23q4-v6wr', 'CVE-2021-29583'} | 2021-12-09T06:35:29.040868Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9xh4-23q4-v6wr', 'https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0'} | null | {'https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0'} | {'https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0'} |
PyPI | GHSA-c2w9-48qc-qpj4 | High severity vulnerability that affects ansible | An exploitable vulnerability exists in the yaml loading functionality of ansible-vault before 1.0.5. A specially crafted vault can execute arbitrary python commands resulting in command execution. An attacker can insert python into the vault to trigger this vulnerability. | {'CVE-2017-2809'} | 2022-03-03T05:12:59.249805Z | 2018-07-13T15:16:54Z | HIGH | null | {'CWE-94'} | {'https://github.com/tomoh1r/ansible-vault/commit/3f8f659ef443ab870bb19f95d43543470168ae04', 'https://nvd.nist.gov/vuln/detail/CVE-2017-2809', 'https://www.talosintelligence.com/vulnerability_reports/TALOS-2017-0305', 'https://github.com/tomoh1r/ansible-vault', 'http://www.securityfocus.com/bid/100824', 'https://github.com/advisories/GHSA-c2w9-48qc-qpj4', 'https://github.com/tomoh1r/ansible-vault/blob/v1.0.5/CHANGES.txt', 'https://github.com/tomoh1r/ansible-vault/issues/4'} | null | {'https://github.com/tomoh1r/ansible-vault/commit/3f8f659ef443ab870bb19f95d43543470168ae04'} | {'https://github.com/tomoh1r/ansible-vault/commit/3f8f659ef443ab870bb19f95d43543470168ae04'} |
PyPI | PYSEC-2021-359 | null | Flask-AppBuilder is an application development framework, built on top of Flask. In affected versions if using Flask-AppBuilder OAuth, an attacker can share a carefully crafted URL with a trusted domain for an application built with Flask-AppBuilder, this URL can redirect a user to a malicious site. This is an open redirect vulnerability. To resolve this issue upgrade to Flask-AppBuilder 3.2.2 or above. If upgrading is infeasible users may filter HTTP traffic containing `?next={next-site}` where the `next-site` domain is different from the application you are protecting as a workaround. | {'GHSA-624f-cqvr-3qw4', 'CVE-2021-32805'} | 2021-10-11T01:16:40.573932Z | 2021-09-08T18:15:00Z | null | null | null | {'https://github.com/dpgaspar/Flask-AppBuilder/commit/6af28521589599b1dbafd6313256229ee9a4fa74', 'https://github.com/dpgaspar/Flask-AppBuilder/security/advisories/GHSA-624f-cqvr-3qw4'} | null | {'https://github.com/dpgaspar/Flask-AppBuilder/commit/6af28521589599b1dbafd6313256229ee9a4fa74'} | {'https://github.com/dpgaspar/Flask-AppBuilder/commit/6af28521589599b1dbafd6313256229ee9a4fa74'} |
PyPI | GHSA-5crj-c72x-m7gq | Null pointer exception when `Exit` node is not preceded by `Enter` op | ### Impact
The [process of building the control flow graph](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/common_runtime/immutable_executor_state.cc#L284-L346) for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not:
```python
import tensorflow as tf
@tf.function
def func():
return tf.raw_ops.Exit(data=[False,False])
func()
```
This occurs because the code assumes that the first node in the pairing (e.g., an `Enter` node) always exists when encountering the second node (e.g., an `Exit` node):
```cc
...
} else if (IsExit(curr_node)) {
// Exit to the parent frame.
parent = parent_nodes[curr_id];
frame_name = cf_info->frame_names[parent->id()];
...
```
When this is not the case, `parent` is `nullptr` so dereferencing it causes a crash.
### Patches
We have patched the issue in GitHub commit [05cbebd3c6bb8f517a158b0155debb8df79017ff](https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff).
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-2021-41217'} | 2022-03-03T05:13:47.828697Z | 2021-11-10T18:55:11Z | MODERATE | null | {'CWE-476'} | {'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41217', 'https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5crj-c72x-m7gq'} | null | {'https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff'} | {'https://github.com/tensorflow/tensorflow/commit/05cbebd3c6bb8f517a158b0155debb8df79017ff'} |
PyPI | GHSA-cqv6-3phm-hcwx | Access to invalid memory during shape inference in `Cudnn*` ops | ### Impact
The [shape inference code](https://github.com/tensorflow/tensorflow/blob/9ff27787893f76d6971dcd1552eb5270d254f31b/tensorflow/core/ops/cudnn_rnn_ops.cc) for the `Cudnn*` operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow:
```python
import tensorflow as tf
@tf.function
def func():
return tf.raw_ops.CudnnRNNV3(
input=[0.1, 0.1],
input_h=[0.5],
input_c=[0.1, 0.1, 0.1],
params=[0.5, 0.5],
sequence_lengths=[-1, 0, 1])
func()
```
This occurs because the ranks of the `input`, `input_h` and `input_c` parameters are not validated, but code assumes they have certain values:
```cc
auto input_shape = c->input(0);
auto input_h_shape = c->input(1);
auto seq_length = c->Dim(input_shape, 0);
auto batch_size = c->Dim(input_shape, 1); // assumes rank >= 2
auto num_units = c->Dim(input_h_shape, 2); // assumes rank >= 3
```
### Patches
We have patched the issue in GitHub commit [af5fcebb37c8b5d71c237f4e59c6477015c78ce6](https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6).
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-2021-41221'} | 2022-03-03T05:13:53.720856Z | 2021-11-10T18:50:17Z | HIGH | null | {'CWE-120', 'CWE-787'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqv6-3phm-hcwx', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41221', 'https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6'} | null | {'https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6'} | {'https://github.com/tensorflow/tensorflow/commit/af5fcebb37c8b5d71c237f4e59c6477015c78ce6'} |
PyPI | GHSA-f4g9-h89h-jgv9 | SAML XML Signature wrapping in PySAML2 | ### Impact
All users of pysaml2 that use the default `CryptoBackendXmlSec1` backend and need to verify signed SAML documents are impacted. `pysaml2 <= 6.4.1` does not validate the SAML document against an XML schema. This allows invalid XML documents to trick the verification process, by presenting elements with a valid signature inside elements whose content has been malformed. The verification is offloaded to `xmlsec1` and `xmlsec1` will not validate every signature in the given document, but only the first it finds in the given scope.
### Patches
Users should upgrade to pysaml2 `v6.5.0`.
### Workarounds
No workaround provided at this point.
### References
No references provided at this point.
### Credits
- Victor Schönfelder Garcia (isits AG International School of IT Security)
- Juraj Somorovsky (Paderborn University)
- Vladislav Mladenov (Ruhr University Bochum)
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [pysaml2](https://github.com/IdentityPython/pysaml2)
* Email us at [the incident-response address](mailto:incident-response@idpy.org) | {'CVE-2021-21238'} | 2022-03-03T05:13:23.568436Z | 2021-01-21T14:12:16Z | LOW | null | {'CWE-347'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-21238', 'https://pypi.org/project/pysaml2', 'https://github.com/IdentityPython/pysaml2/releases/tag/v6.5.0', 'https://github.com/IdentityPython/pysaml2/security/advisories/GHSA-f4g9-h89h-jgv9', 'https://github.com/IdentityPython/pysaml2/commit/1d8fd268f5bf887480a403a7a5ef8f048157cc14'} | null | {'https://github.com/IdentityPython/pysaml2/commit/1d8fd268f5bf887480a403a7a5ef8f048157cc14'} | {'https://github.com/IdentityPython/pysaml2/commit/1d8fd268f5bf887480a403a7a5ef8f048157cc14'} |
PyPI | PYSEC-2020-79 | null | In libImaging/Jpeg2KDecode.c in Pillow before 7.1.0, there are multiple out-of-bounds reads via a crafted JP2 file. | {'GHSA-vj42-xq3r-hr3r', 'CVE-2020-10994'} | 2020-07-27T19:15:00Z | 2020-06-25T19:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-vj42-xq3r-hr3r', 'https://github.com/python-pillow/Pillow/pull/4505', 'https://github.com/python-pillow/Pillow/commits/master/src/libImaging/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/HOKHNWV2VS5GESY7IBD237E7C6T3I427/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/BEBCPE4F2VHTIT6EZA2YZQZLPVDEBJGD/', 'https://github.com/python-pillow/Pillow/pull/4538', 'https://usn.ubuntu.com/4430-2/', 'https://pillow.readthedocs.io/en/stable/releasenotes/7.1.0.html', 'https://usn.ubuntu.com/4430-1/', 'https://pillow.readthedocs.io/en/stable/releasenotes/'} | null | {'https://github.com/python-pillow/Pillow/commits/master/src/libImaging/'} | {'https://github.com/python-pillow/Pillow/commits/master/src/libImaging/'} |
PyPI | PYSEC-2021-477 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-x83m-p7pv-ch8v', 'CVE-2021-29549'} | 2021-12-09T06:34:50.949292Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x83m-p7pv-ch8v', 'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16'} | null | {'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16'} | {'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16'} |
PyPI | PYSEC-2021-532 | null | TensorFlow is an end-to-end open source platform for machine learning. The TFLite implementation of hashtable lookup is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/hashtable_lookup.cc#L114-L115) An attacker can craft a model such that `values`'s first dimension would be 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-8rm6-75mf-7r7r', 'CVE-2021-29604'} | 2021-12-09T06:34:59.535781Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rm6-75mf-7r7r', 'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2'} | null | {'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2'} | {'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2'} |
PyPI | PYSEC-2021-162 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b40060c9f697b044e3107917c797ba052f4506ab/tensorflow/core/kernels/conv_grad_input_ops.h#L625-L655) does a division by a quantity that is controlled by the caller. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-xm2v-8rrw-w9pm', 'CVE-2021-29525'} | 2021-08-27T03:22:25.775857Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xm2v-8rrw-w9pm', 'https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535'} | null | {'https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535'} | {'https://github.com/tensorflow/tensorflow/commit/2be2cdf3a123e231b16f766aa0e27d56b4606535'} |
PyPI | PYSEC-2021-498 | 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/ef0c008ee84bad91ec6725ddc42091e19a30cf0e/tensorflow/core/kernels/maxpooling_op.cc#L1016-L1017) uses the same value to index in two different arrays but there is no guarantee that the sizes are identical. 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-2021-29570', 'GHSA-545v-42p7-98fq'} | 2021-12-09T06:34:54.218525Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-545v-42p7-98fq', 'https://github.com/tensorflow/tensorflow/commit/dcd7867de0fea4b72a2b34bd41eb74548dc23886'} | null | {'https://github.com/tensorflow/tensorflow/commit/dcd7867de0fea4b72a2b34bd41eb74548dc23886'} | {'https://github.com/tensorflow/tensorflow/commit/dcd7867de0fea4b72a2b34bd41eb74548dc23886'} |
PyPI | GHSA-c3xq-cj8f-7829 | Inadequate Encryption Strength in python-keystoneclient | python-keystoneclient version 0.2.3 to 0.2.5 has middleware memcache encryption bypass | {'CVE-2013-2166'} | 2022-03-23T20:00:07.261776Z | 2021-10-12T16:31:43Z | CRITICAL | null | {'CWE-326'} | {'http://www.openwall.com/lists/oss-security/2013/06/19/5', 'http://www.securityfocus.com/bid/60684', 'https://github.com/openstack/python-keystoneclient', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2013-2166', 'http://lists.fedoraproject.org/pipermail/package-announce/2013-August/113944.html', 'https://nvd.nist.gov/vuln/detail/CVE-2013-2166', 'https://github.com/openstack/python-keystoneclient/commit/eeefb784f24c37d5f56a421e1ccc911cace9385e', 'http://rhn.redhat.com/errata/RHSA-2013-0992.html', 'https://access.redhat.com/security/cve/cve-2013-2166', 'https://bugzilla.suse.com/show_bug.cgi?id=CVE-2013-2166', 'https://security-tracker.debian.org/tracker/CVE-2013-2166'} | null | {'https://github.com/openstack/python-keystoneclient/commit/eeefb784f24c37d5f56a421e1ccc911cace9385e'} | {'https://github.com/openstack/python-keystoneclient/commit/eeefb784f24c37d5f56a421e1ccc911cace9385e'} |
PyPI | GHSA-rv6r-3f5q-9rgx | Twisted SSH client and server deny of service during SSH handshake. | ### Impact
The Twisted SSH client and server implementation naively accepted an infinite amount of data for the peer's SSH version identifier.
A malicious peer can trivially craft a request that uses all available memory and crash the server, resulting in denial of service. The attack is as simple as `nc -rv localhost 22 < /dev/zero`.
### Patches
The issue was fix in GitHub commit https://github.com/twisted/twisted/commit/98387b39e9f0b21462f6abc7a1325dc370fcdeb1
A fix is available in Twisted 22.2.0.
### Workarounds
* Limit access to the SSH server only to trusted source IP addresses.
* Connect over SSH only to trusted destination IP addresses.
### References
Reported at https://twistedmatrix.com/trac/ticket/10284
Discussions at https://github.com/twisted/twisted/security/advisories/GHSA-rv6r-3f5q-9rgx
### For more information
Found by vin01 | {'CVE-2022-21716'} | 2022-04-22T15:47:19.222481Z | 2022-03-03T19:02:08Z | HIGH | null | {'CWE-120'} | {'https://github.com/twisted/twisted/commit/98387b39e9f0b21462f6abc7a1325dc370fcdeb1', 'https://github.com/twisted/twisted/releases/tag/twisted-22.2.0', 'https://github.com/twisted/twisted/security/advisories/GHSA-rv6r-3f5q-9rgx', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21716', 'https://lists.debian.org/debian-lts-announce/2022/03/msg00009.html', 'https://www.oracle.com/security-alerts/cpuapr2022.html', 'https://github.com/twisted/twisted/commit/89c395ee794e85a9657b112c4351417850330ef9', 'https://twistedmatrix.com/trac/ticket/10284', 'https://github.com/twisted/twisted'} | null | {'https://github.com/twisted/twisted/commit/89c395ee794e85a9657b112c4351417850330ef9', 'https://github.com/twisted/twisted/commit/98387b39e9f0b21462f6abc7a1325dc370fcdeb1'} | {'https://github.com/twisted/twisted/commit/98387b39e9f0b21462f6abc7a1325dc370fcdeb1', 'https://github.com/twisted/twisted/commit/89c395ee794e85a9657b112c4351417850330ef9'} |
PyPI | GHSA-9vpm-rcf4-9wqw | Division by 0 in `MaxPoolGradWithArgmax` | ### Impact
The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` is vulnerable to a division by 0:
```python
import tensorflow as tf
input = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
grad = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
argmax = tf.constant([], shape=[0], dtype=tf.int64)
ksize = [1, 1, 1, 1]
strides = [1, 1, 1, 1]
tf.raw_ops.MaxPoolGradWithArgmax(
input=input, grad=grad, argmax=argmax, ksize=ksize, strides=strides,
padding='SAME', include_batch_in_index=False)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/279bab6efa22752a2827621b7edb56a730233bd8/tensorflow/core/kernels/maxpooling_op.cc#L1033-L1034) fails to validate that the batch dimension of the tensor is non-zero, before dividing by this quantity.
### Patches
We have patched the issue in GitHub commit [376c352a37ce5a68b721406dc7e77ac4b6cf483d](https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d).
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-2021-29573'} | 2022-03-03T05:14:00.057449Z | 2021-05-21T14:26:07Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vpm-rcf4-9wqw', 'https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29573'} | null | {'https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d'} | {'https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d'} |
PyPI | PYSEC-2021-549 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.SparseDenseCwiseDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L56) uses a common class for all binary operations but fails to treat the division by 0 case separately. We have patched the issue in GitHub commit d9204be9f49520cdaaeb2541d1dc5187b23f31d9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37636', 'GHSA-hp4c-x6r7-6555'} | 2021-12-09T06:35:02.050023Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/d9204be9f49520cdaaeb2541d1dc5187b23f31d9', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hp4c-x6r7-6555'} | null | {'https://github.com/tensorflow/tensorflow/commit/d9204be9f49520cdaaeb2541d1dc5187b23f31d9'} | {'https://github.com/tensorflow/tensorflow/commit/d9204be9f49520cdaaeb2541d1dc5187b23f31d9'} |
PyPI | PYSEC-2020-119 | null | In Tensorflow version 2.3.0, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'GHSA-pg59-2f92-5cph', 'CVE-2020-15196'} | 2021-09-01T08:19:33.034745Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-pg59-2f92-5cph', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | GHSA-27j5-4p9v-pp67 | `std::abort` raised from `TensorListReserve` | ### Impact
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:
```python
import tensorflow as tf
tf.raw_ops.TensorListReserve(
element_shape = tf.constant([1]),
num_elements=tf.constant([-1]),
element_dtype = tf.int32)
```
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.
### Patches
We have patched the issue in GitHub commit [8a6e874437670045e6c7dc6154c7412b4a2135e2](https://github.com/tensorflow/tensorflow/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.
### 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-2021-37644'} | 2022-03-03T05:13:01.903089Z | 2021-08-25T14:43:40Z | MODERATE | null | {'CWE-617'} | {'https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-27j5-4p9v-pp67', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37644'} | null | {'https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2'} | {'https://github.com/tensorflow/tensorflow/commit/8a6e874437670045e6c7dc6154c7412b4a2135e2'} |
PyPI | GHSA-37cf-r3w2-gjfw | django-nopassword stores secrets in cleartext | django-nopassword before 5.0.0 stores cleartext secrets in the database. | {'CVE-2019-10682'} | 2022-03-03T05:12:46.065476Z | 2020-06-05T16:09:19Z | HIGH | null | {'CWE-522', 'CWE-312'} | {'https://github.com/relekang/django-nopassword/commit/d8b4615f5fbfe3997d96cf4cb3e342406396193c', 'https://github.com/relekang/django-nopassword/compare/v4.0.1...v5.0.0', 'https://nvd.nist.gov/vuln/detail/CVE-2019-10682', 'https://github.com/relekang/django-nopassword/blob/8e8cfc765ee00adfed120c2c79bf71ef856e9022/nopassword/models.py#L14'} | null | {'https://github.com/relekang/django-nopassword/commit/d8b4615f5fbfe3997d96cf4cb3e342406396193c'} | {'https://github.com/relekang/django-nopassword/commit/d8b4615f5fbfe3997d96cf4cb3e342406396193c'} |
PyPI | PYSEC-2022-110 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `ConcatV2` can be used to trigger a denial of service attack via a segfault caused by a type confusion. The `axis` argument is translated into `concat_dim` in the `ConcatShapeHelper` helper function. Then, a value for `min_rank` is computed based on `concat_dim`. This is then used to validate that the `values` tensor has at least the required rank. However, `WithRankAtLeast` receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that `min_rank` is a 32-bits value and the value of `axis`, the `rank` argument is a negative value, so the error check is bypassed. 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-2022-21731', 'GHSA-m4hf-j54p-p353'} | 2022-03-09T00:18:23.826464Z | 2022-02-03T12:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/08d7b00c0a5a20926363849f611729f53f3ec022', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/shape_inference.cc#L345-L358', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/framework/common_shape_fns.cc#L1961-L2059', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m4hf-j54p-p353'} | null | {'https://github.com/tensorflow/tensorflow/commit/08d7b00c0a5a20926363849f611729f53f3ec022'} | {'https://github.com/tensorflow/tensorflow/commit/08d7b00c0a5a20926363849f611729f53f3ec022'} |
PyPI | PYSEC-2021-375 | null | Cobbler before 3.3.0 allows authorization bypass for modification of settings. | {'GHSA-cr3f-r24j-3chw', 'CVE-2021-40325'} | 2021-10-19T21:47:31.769581Z | 2021-10-04T06:15:00Z | null | null | null | {'https://github.com/cobbler/cobbler/commit/d8f60bbf14a838c8c8a1dba98086b223e35fe70a', 'https://github.com/cobbler/cobbler/releases/tag/v3.3.0', 'https://github.com/advisories/GHSA-cr3f-r24j-3chw'} | null | {'https://github.com/cobbler/cobbler/commit/d8f60bbf14a838c8c8a1dba98086b223e35fe70a'} | {'https://github.com/cobbler/cobbler/commit/d8f60bbf14a838c8c8a1dba98086b223e35fe70a'} |
PyPI | PYSEC-2021-725 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `Split` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e2752089ef7ce9bcf3db0ec618ebd23ea119d0c7/tensorflow/lite/kernels/split.cc#L63-L65). An attacker can craft a model such that `num_splits` 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-2021-29599', 'GHSA-97wf-p777-86jq'} | 2021-12-09T06:35:31.883346Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-97wf-p777-86jq', 'https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d'} | null | {'https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d'} | {'https://github.com/tensorflow/tensorflow/commit/b22786e7e9b7bdb6a56936ff29cc7e9968d7bc1d'} |
PyPI | PYSEC-2021-230 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `BatchToSpaceNd` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/b5ed552fe55895aee8bd8b191f744a069957d18d/tensorflow/lite/kernels/batch_to_space_nd.cc#L81-L82). An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-cfx7-2xpc-8w4h', 'CVE-2021-29593'} | 2021-08-27T03:22:37.941172Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cfx7-2xpc-8w4h', 'https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044'} | null | {'https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044'} | {'https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044'} |
PyPI | GHSA-p45v-v4pw-77jr | Division by 0 in `QuantizedBatchNormWithGlobalNormalization` | ### Impact
An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`:
```python
import tensorflow as tf
t = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.quint8)
t_min = tf.constant(-10.0, dtype=tf.float32)
t_max = tf.constant(-10.0, dtype=tf.float32)
m = tf.constant([], shape=[0], dtype=tf.quint8)
m_min = tf.constant(-10.0, dtype=tf.float32)
m_max = tf.constant(-10.0, dtype=tf.float32)
v = tf.constant([], shape=[0], dtype=tf.quint8)
v_min = tf.constant(-10.0, dtype=tf.float32)
v_max = tf.constant(-10.0, dtype=tf.float32)
beta = tf.constant([], shape=[0], dtype=tf.quint8)
beta_min = tf.constant(-10.0, dtype=tf.float32)
beta_max = tf.constant(-10.0, dtype=tf.float32)
gamma = tf.constant([], shape=[0], dtype=tf.quint8)
gamma_min = tf.constant(-10.0, dtype=tf.float32)
gamma_max = tf.constant(-10.0, dtype=tf.float32)
tf.raw_ops.QuantizedBatchNormWithGlobalNormalization(
t=t, t_min=t_min, t_max=t_max, m=m, m_min=m_min, m_max=m_max,
v=v, v_min=v_min, v_max=v_max, beta=beta, beta_min=beta_min,
beta_max=beta_max, gamma=gamma, gamma_min=gamma_min,
gamma_max=gamma_max, out_type=tf.qint32,
variance_epsilon=0.1, scale_after_normalization=True)
```
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).
### Patches
We have patched the issue in GitHub commit [d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b](https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b).
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-2021-29548'} | 2022-03-03T05:14:08.985186Z | 2021-05-21T14:23:34Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p45v-v4pw-77jr', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29548', 'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} | null | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} |
PyPI | GHSA-c94w-c95p-phf8 | Integer overflow in Tensorflow | ### Impact
The [implementation of `OpLevelCostEstimator::CalculateTensorSize`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L1552-L1558) is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements:
```cc
int64_t OpLevelCostEstimator::CalculateTensorSize(
const OpInfo::TensorProperties& tensor, bool* found_unknown_shapes) {
int64_t count = CalculateTensorElementCount(tensor, found_unknown_shapes);
int size = DataTypeSize(BaseType(tensor.dtype()));
VLOG(2) << "Count: " << count << " DataTypeSize: " << size;
return count * size;
}
```
Here, `count` and `size` can be large enough to cause `count * size` to overflow.
### Patches
We have patched the issue in GitHub commit [fcd18ce3101f245b083b30655c27b239dc72221e](https://github.com/tensorflow/tensorflow/commit/fcd18ce3101f245b083b30655c27b239dc72221e).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. | {'CVE-2022-23575'} | 2022-03-03T05:13:34.318775Z | 2022-02-10T00:32:59Z | MODERATE | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c94w-c95p-phf8', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23575', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L1552-L1558', 'https://github.com/tensorflow/tensorflow/commit/fcd18ce3101f245b083b30655c27b239dc72221e'} | null | {'https://github.com/tensorflow/tensorflow/commit/fcd18ce3101f245b083b30655c27b239dc72221e'} | {'https://github.com/tensorflow/tensorflow/commit/fcd18ce3101f245b083b30655c27b239dc72221e'} |
PyPI | PYSEC-2021-660 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.SparseConcat`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b432a38fe0e1b4b904a6c222cbce794c39703e87/tensorflow/core/kernels/sparse_concat_op.cc#L76) takes the values specified in `shapes[0]` as dimensions for the output shape. The `TensorShape` constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when `InitDims`(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29534', 'GHSA-6j9c-grc6-5m6g'} | 2021-12-09T06:35:20.598497Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6j9c-grc6-5m6g'} | null | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c'} | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c'} |
PyPI | GHSA-cmgw-8vpc-rc59 | Segfault on strings tensors with mistmatched dimensions, due to Go code | ### Impact
Under certain conditions, Go code can trigger a segfault in string deallocation.
For string tensors, `C.TF_TString_Dealloc` is called during garbage collection within a finalizer function. However, tensor structure isn't checked until encoding to avoid a performance penalty. The current method for dealloc assumes that encoding succeeded, but segfaults when a string tensor is garbage collected whose encoding failed (e.g., due to mismatched dimensions).
To fix this, the call to set the finalizer function is deferred until `NewTensor` returns and, if encoding failed for a string tensor, deallocs are determined based on bytes written.
### Patches
We have patched the issue in GitHub commit [8721ba96e5760c229217b594f6d2ba332beedf22](https://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22) (merging [#50508](https://github.com/tensorflow/tensorflow/pull/50508)).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, which is the other affected version.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported externally via a [fixing PR](https://github.com/tensorflow/tensorflow/pull/50508). | {'CVE-2021-37692'} | 2022-03-03T05:09:55.555709Z | 2021-08-25T14:38:57Z | MODERATE | null | {'CWE-20'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37692', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cmgw-8vpc-rc59', 'https://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22', 'https://github.com/tensorflow/tensorflow/pull/50508'} | null | {'https://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22'} | {'https://github.com/tensorflow/tensorflow/commit/8721ba96e5760c229217b594f6d2ba332beedf22'} |
PyPI | PYSEC-2020-230 | null | In Django User Sessions (django-user-sessions) before 1.7.1, the views provided allow users to terminate specific sessions. The session key is used to identify sessions, and thus included in the rendered HTML. In itself this is not a problem. However if the website has an XSS vulnerability, the session key could be extracted by the attacker and a session takeover could happen. | {'GHSA-5fq8-3q2f-4m5g', 'CVE-2020-5224'} | 2021-08-27T03:21:57.616152Z | 2020-01-24T20:15:00Z | null | null | null | {'https://github.com/Bouke/django-user-sessions/security/advisories/GHSA-5fq8-3q2f-4m5g', 'https://github.com/jazzband/django-user-sessions/commit/f0c4077e7d1436ba6d721af85cee89222ca5d2d9'} | null | {'https://github.com/jazzband/django-user-sessions/commit/f0c4077e7d1436ba6d721af85cee89222ca5d2d9'} | {'https://github.com/jazzband/django-user-sessions/commit/f0c4077e7d1436ba6d721af85cee89222ca5d2d9'} |
PyPI | GHSA-qx3f-p745-w4hr | Integer overflow in Tensorflow | ### Impact
The implementation of `Range` suffers from integer overflows. These can trigger undefined behavior or, in some scenarios, extremely large allocations.
### Patches
We have patched the issue in GitHub commit [f0147751fd5d2ff23251149ebad9af9f03010732](https://github.com/tensorflow/tensorflow/commit/f0147751fd5d2ff23251149ebad9af9f03010732) (merging [#51733](https://github.com/tensorflow/tensorflow/pull/51733)).
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 externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/52676). | {'CVE-2022-23562'} | 2022-03-03T05:14:16.853671Z | 2022-02-09T23:54:07Z | HIGH | null | {'CWE-190'} | {'https://github.com/tensorflow/tensorflow/commit/f0147751fd5d2ff23251149ebad9af9f03010732', 'https://github.com/tensorflow/tensorflow/', 'https://github.com/tensorflow/tensorflow/issues/52676', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23562', 'https://github.com/tensorflow/tensorflow/pull/51733', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qx3f-p745-w4hr'} | null | {'https://github.com/tensorflow/tensorflow/commit/f0147751fd5d2ff23251149ebad9af9f03010732'} | {'https://github.com/tensorflow/tensorflow/commit/f0147751fd5d2ff23251149ebad9af9f03010732'} |
PyPI | GHSA-vxp9-wv2f-wqmw | High severity vulnerability that affects superset | Versions of Superset prior to 0.23 used an unsafe load method from the pickle library to deserialize data leading to possible remote code execution. Note Superset 0.23 was released prior to any Superset release under the Apache Software Foundation. | {'CVE-2018-8021'} | 2022-03-23T22:15:05.776608Z | 2018-11-09T17:40:56Z | CRITICAL | null | {'CWE-502'} | {'https://github.com/apache/superset/commit/2c72a7ae4fc0a8bac1f037a79efa90e1c5549710', 'https://www.exploit-db.com/exploits/45933/', 'https://github.com/apache/superset/pull/4243', 'https://nvd.nist.gov/vuln/detail/CVE-2018-8021', 'https://github.com/apache/superset'} | null | {'https://github.com/apache/superset/commit/2c72a7ae4fc0a8bac1f037a79efa90e1c5549710'} | {'https://github.com/apache/superset/commit/2c72a7ae4fc0a8bac1f037a79efa90e1c5549710'} |
PyPI | GHSA-m85c-9mf8-m2m6 | Critical severity vulnerability that affects confire | An exploitable vulnerability exists in the YAML parsing functionality in config.py in Confire 0.2.0. Due to the user-specific configuration being loaded from "~/.confire.yaml" using the yaml.load function, a YAML parser can execute arbitrary Python commands resulting in command execution. An attacker can insert Python into loaded YAML to trigger this vulnerability. | {'CVE-2017-16763'} | 2022-03-03T05:13:26.050847Z | 2018-07-18T18:28:26Z | CRITICAL | null | null | {'https://github.com/bbengfort/confire/commit/8cc86a5ec2327e070f1d576d61bbaadf861597ea', 'https://github.com/advisories/GHSA-m85c-9mf8-m2m6', 'https://github.com/bbengfort/confire', 'https://joel-malwarebenchmark.github.io/blog/2017/11/12/cve-2017-16763-configure-loaded-through-confire/', 'https://github.com/bbengfort/confire/issues/24', 'https://nvd.nist.gov/vuln/detail/CVE-2017-16763'} | null | {'https://github.com/bbengfort/confire/commit/8cc86a5ec2327e070f1d576d61bbaadf861597ea'} | {'https://github.com/bbengfort/confire/commit/8cc86a5ec2327e070f1d576d61bbaadf861597ea'} |
PyPI | GHSA-cpf4-wx82-gxp6 | Segfault due to negative splits in `SplitV` | ### Impact
The [implementation](https://github.com/tensorflow/tensorflow/blob/e71b86d47f8bc1816bf54d7bddc4170e47670b97/tensorflow/core/kernels/split_v_op.cc#L49-L205) of `SplitV` can trigger a segfault is an attacker supplies negative arguments:
```python
import tensorflow as tf
tf.raw_ops.SplitV(
value=tf.constant([]),
size_splits=[-1, -2]
,axis=0,
num_split=2)
```
This occurs whenever `size_splits` contains more than one value and at least one value is negative.
### Patches
We have patched the issue in GitHub commit [25d622ffc432acc736b14ca3904177579e733cc6](https://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6).
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-2021-41222'} | 2022-03-03T05:13:07.507477Z | 2021-11-10T18:48:15Z | MODERATE | null | {'CWE-682'} | {'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41222', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cpf4-wx82-gxp6', 'https://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6'} | null | {'https://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6'} | {'https://github.com/tensorflow/tensorflow/commit/25d622ffc432acc736b14ca3904177579e733cc6'} |
PyPI | PYSEC-2017-8 | null | HKDF in cryptography before 1.5.2 returns an empty byte-string if used with a length less than algorithm.digest_size. | {'CVE-2016-9243'} | 2021-07-05T00:01:17.464237Z | 2017-03-27T17:59:00Z | null | null | null | {'http://www.securityfocus.com/bid/94216', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/WQ5G7KHKZC4SI23JE7277KZXM57GEQKT/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/5R2ZOBMPWDFFHUZ6QOZZY36A6H5CGJXL/', 'https://github.com/pyca/cryptography/issues/3211', 'https://cryptography.io/en/latest/changelog', 'https://github.com/pyca/cryptography/commit/b924696b2e8731f39696584d12cceeb3aeb2d874', 'http://www.ubuntu.com/usn/USN-3138-1', 'http://www.openwall.com/lists/oss-security/2016/11/09/2', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/U23KDR2M2N7W2ZSREG63BVW7D4VC6CIZ/'} | null | {'https://github.com/pyca/cryptography/commit/b924696b2e8731f39696584d12cceeb3aeb2d874'} | {'https://github.com/pyca/cryptography/commit/b924696b2e8731f39696584d12cceeb3aeb2d874'} |
PyPI | PYSEC-2022-87 | null | Tensorflow is an Open Source Machine Learning Framework. If a graph node is invalid, TensorFlow can leak memory in the implementation of `ImmutableExecutorState::Initialize`. Here, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak. 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-8r7c-3cm2-3h8f', 'CVE-2022-23578'} | 2022-03-09T00:17:34.646320Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8r7c-3cm2-3h8f', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/common_runtime/immutable_executor_state.cc#L84-L262', 'https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd'} | null | {'https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd'} | {'https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd'} |
PyPI | PYSEC-2022-147 | null | Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that any binary op would trigger `CHECK` failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the `dtype` no longer matches the `dtype` expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If `Tin` and `Tout` don't match the type of data in `out` and `input_*` tensors then `flat<*>` would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a `CHECK` crash, hence a denial of service. 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-2022-23583', 'GHSA-gjqc-q9g6-q2j3'} | 2022-03-09T00:18:28.843331Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/cwise_ops_common.h#L88-L137', 'https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gjqc-q9g6-q2j3'} | null | {'https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9'} | {'https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9'} |
PyPI | PYSEC-2018-108 | null | The SSH server implementation of AsyncSSH before 1.12.1 does not properly check whether authentication is completed before processing other requests. A customized SSH client can simply skip the authentication step. | {'CVE-2018-7749'} | 2021-12-13T06:35:08.979709Z | 2018-03-12T19:29:00Z | null | null | null | {'https://github.com/ronf/asyncssh/commit/c161e26cdc0d41b745b63d9f17b437f073bf7ba4', 'https://groups.google.com/forum/#!msg/asyncssh-announce/57_5O7kiHSA/8BXZ_hxHAQAJ'} | null | {'https://github.com/ronf/asyncssh/commit/c161e26cdc0d41b745b63d9f17b437f073bf7ba4'} | {'https://github.com/ronf/asyncssh/commit/c161e26cdc0d41b745b63d9f17b437f073bf7ba4'} |
PyPI | GHSA-grfj-wjv9-4f9v | Open redirect in Jupyter Server | ### Impact
_What kind of vulnerability is it? Who is impacted?_
Open redirect vulnerability - a maliciously crafted link to a jupyter server could redirect the browser to a different website.
All jupyter servers are technically affected, however, these maliciously crafted links can only be reasonably made for known jupyter server hosts. A link to your jupyter server may *appear* safe, but ultimately redirect to a spoofed server on the public internet.
This originated in jupyter/notebook: https://github.com/jupyter/notebook/security/advisories/GHSA-c7vm-f5p4-8fqh
### Patches
_Has the problem been patched? What versions should users upgrade to?_
jupyter_server 1.0.6
### References
[OWASP page on open redirects](https://cheatsheetseries.owasp.org/cheatsheets/Unvalidated_Redirects_and_Forwards_Cheat_Sheet.html)
### For more information
If you have any questions or comments about this advisory, or vulnerabilities to report, please email our security list [security@ipython.org](mailto:security@ipython.org).
Credit: zhuonan li of Alibaba Application Security Team | {'CVE-2020-26232'} | 2022-03-03T05:13:24.890200Z | 2020-11-24T21:20:52Z | MODERATE | null | {'CWE-601'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-26232', 'https://github.com/jupyter/jupyter_server/security/advisories/GHSA-grfj-wjv9-4f9v', 'https://github.com/jupyter-server/jupyter_server/commit/3d83e49090289c431da253e2bdb8dc479cbcb157', 'https://github.com/jupyter-server/jupyter_server/blob/master/CHANGELOG.md#106---2020-11-18'} | null | {'https://github.com/jupyter-server/jupyter_server/commit/3d83e49090289c431da253e2bdb8dc479cbcb157'} | {'https://github.com/jupyter-server/jupyter_server/commit/3d83e49090289c431da253e2bdb8dc479cbcb157'} |
PyPI | GHSA-w74j-v8xh-3w5h | Reference binding to nullptr in unicode encoding | ### Impact
An attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.UnicodeEncode`:
```python
import tensorflow as tf
from tensorflow.python.ops import gen_string_ops
gen_string_ops.unicode_encode(
input_values=[],
input_splits=[],
output_encoding='UTF-8',
errors='ignore',
replacement_char='a')
```
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:
```cc
const Tensor& input_splits = context->input(1);
const auto input_splits_flat = input_splits.flat<SPLITS_TYPE>();
TensorShape output_shape({input_splits.dim_size(0) - 1});
```
### Patches
We have patched the issue in GitHub commit [2e0ee46f1a47675152d3d865797a18358881d7a6](https://github.com/tensorflow/tensorflow/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.
### 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-2021-37667'} | 2022-03-03T05:13:33.464306Z | 2021-08-25T14:42:09Z | HIGH | null | {'CWE-824'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/2e0ee46f1a47675152d3d865797a18358881d7a6', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37667', '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-2022-68 | null | Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both `embedding_size` and `lookup_size` are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write. Users are advised to upgrade to a patched version. | {'GHSA-98p5-x8x4-c9m5', 'CVE-2022-23559'} | 2022-03-09T00:17:32.298949Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189', 'https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4', 'https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01', 'https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5'} | null | {'https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01', 'https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043', 'https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4'} | {'https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01', 'https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043', 'https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4'} |
PyPI | PYSEC-2021-772 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a denial of service in `boosted_trees_create_quantile_stream_resource` by using negative arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that `num_streams` only contains non-negative numbers. In turn, [this results in using this value to allocate memory](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). However, `reserve` receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. We have patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37661', 'GHSA-gf88-j2mg-cc82'} | 2021-12-09T06:35:37.616060Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gf88-j2mg-cc82'} | null | {'https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992'} | {'https://github.com/tensorflow/tensorflow/commit/8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992'} |
PyPI | PYSEC-2021-288 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToVariant`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L129) has an incomplete validation of the splits values, missing the case when the argument would be empty. We have patched the issue in GitHub commit be7a4de6adfbd303ce08be4332554dff70362612. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37666', 'GHSA-w4xf-2pqw-5mq7'} | 2021-08-27T03:22:45.481654Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w4xf-2pqw-5mq7'} | null | {'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612'} | {'https://github.com/tensorflow/tensorflow/commit/be7a4de6adfbd303ce08be4332554dff70362612'} |
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