ecosystem stringclasses 14 values | vuln_id stringlengths 10 19 | summary stringlengths 4 267 ⌀ | details stringlengths 9 13.5k | aliases stringlengths 17 144 ⌀ | modified_date stringdate 2010-05-27 05:47:00 2022-05-10 08:46:52 | published_date stringdate 2005-12-31 05:00:00 2022-05-10 08:46:50 | severity stringclasses 5 values | score float64 0 10 ⌀ | cwe_id stringclasses 988 values | refs stringlengths 30 17.7k ⌀ | introduced stringlengths 75 4.26k ⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | PYSEC-2020-115 | null | In Tensorflow before versions 2.2.1 and 2.3.1, if a user passes a list of strings to `dlpack.to_dlpack` there is a memory leak following an expected validation failure. The issue occurs because the `status` argument during validation failures is not properly checked. Since each of the above methods can return an error status, the `status` value must be checked before continuing. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1. | {'CVE-2020-15192', 'GHSA-8fxw-76px-3rxv'} | 2021-09-01T08:19:32.462320Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fxw-76px-3rxv', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html'} | null |
PyPI | GHSA-rqjh-jp2r-59cj | nltk is vulnerable to Inefficient Regular Expression Complexity | nltk is vulnerable to Inefficient Regular Expression Complexity | {'CVE-2021-3842'} | 2022-03-03T05:13:29.207812Z | 2022-01-06T22:24:14Z | HIGH | null | {'CWE-1333'} | {'https://github.com/nltk/nltk/commit/2a50a3edc9d35f57ae42a921c621edc160877f4d', 'https://huntr.dev/bounties/761a761e-2be2-430a-8d92-6f74ffe9866a', 'https://nvd.nist.gov/vuln/detail/CVE-2021-3842', 'https://github.com/nltk/nltk'} | null |
PyPI | PYSEC-2021-816 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementations for convolution operators trigger a division by 0 if passed empty filter tensor arguments. 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-6hpv-v2rx-c5g6', 'CVE-2021-41209'} | 2021-12-09T06:35:42.527822Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6hpv-v2rx-c5g6'} | null |
PyPI | GHSA-5qcg-w2cc-xffw | Uncontrolled resource consumption in validators Python package | The validators package 0.12.2 through 0.12.5 for Python enters an infinite loop when validators.domain is called with a crafted domain string. This is fixed in 0.12.6. | {'CVE-2019-19588'} | 2022-03-03T05:13:35.926629Z | 2020-01-21T20:32:09Z | HIGH | null | {'CWE-835'} | {'https://nvd.nist.gov/vuln/detail/CVE-2019-19588', 'https://github.com/kvesteri/validators/issues/86'} | null |
PyPI | PYSEC-2013-3 | null | The renderLocalView function in render/views.py in graphite-web in Graphite 0.9.5 through 0.9.10 uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object. | {'CVE-2013-5093'} | 2021-07-05T00:01:21.746777Z | 2013-09-27T10:08:00Z | null | null | null | {'http://www.exploit-db.com/exploits/27752', 'http://www.osvdb.org/96436', 'http://ceriksen.com/2013/08/20/graphite-remote-code-execution-vulnerability-advisory/', 'https://github.com/graphite-project/graphite-web/blob/master/docs/releases/0_9_11.rst', 'https://github.com/rapid7/metasploit-framework/blob/master/modules/exploits/unix/webapp/graphite_pickle_exec.rb', 'http://www.securityfocus.com/bid/61894', 'http://secunia.com/advisories/54556'} | null |
PyPI | PYSEC-2018-67 | null | In the marshmallow library before 2.15.1 and 3.x before 3.0.0b9 for Python, the schema "only" option treats an empty list as implying no "only" option, which allows a request that was intended to expose no fields to instead expose all fields (if the schema is being filtered dynamically using the "only" option, and there is a user role that produces an empty value for "only"). | {'GHSA-9q2p-fj49-vpxj', 'CVE-2018-17175'} | 2021-09-01T08:44:17.759030Z | 2018-09-18T17:29:00Z | null | null | null | {'https://github.com/marshmallow-code/marshmallow/pull/782', 'https://github.com/marshmallow-code/marshmallow/pull/777', 'https://github.com/marshmallow-code/marshmallow/issues/772', 'https://github.com/advisories/GHSA-9q2p-fj49-vpxj'} | null |
PyPI | GHSA-cwh5-3cw7-4286 | Moderate severity vulnerability that affects tlslite-ng | tlslite-ng version 0.7.3 and earlier, since commit d7b288316bca7bcdd082e6ccff5491e241305233 contains a CWE-354: Improper Validation of Integrity Check Value vulnerability in TLS implementation, tlslite/utils/constanttime.py: ct_check_cbc_mac_and_pad(); line "end_pos = data_len - 1 - mac.digest_size" that can result in an attacker manipulating the TLS ciphertext which will not be detected by receiving tlslite-ng. This attack appears to be exploitable via man in the middle on a network connection. This vulnerability appears to have been fixed after commit 3674815d1b0f7484454995e2737a352e0a6a93d8. | {'CVE-2018-1000159'} | 2022-03-03T05:14:16.096723Z | 2018-07-12T20:30:44Z | MODERATE | null | {'CWE-354'} | {'https://nvd.nist.gov/vuln/detail/CVE-2018-1000159', 'https://github.com/tomato42/tlslite-ng', 'https://github.com/advisories/GHSA-cwh5-3cw7-4286', 'https://github.com/tomato42/tlslite-ng/pull/234'} | null |
PyPI | PYSEC-2021-115 | null | The package glances before 3.2.1 are vulnerable to XML External Entity (XXE) Injection via the use of Fault to parse untrusted XML data, which is known to be vulnerable to XML attacks.
| {'CVE-2021-23418', 'SNYK-PYTHON-GLANCES-1311807', 'GHSA-r2mj-8wgq-73m6'} | 2021-07-29T20:29:05.800424Z | 2021-07-29T18:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-r2mj-8wgq-73m6', 'https://snyk.io/vuln/SNYK-PYTHON-GLANCES-1311807', 'https://github.com/nicolargo/glances/issues/1025', 'https://github.com/nicolargo/glances/commit/4b87e979afdc06d98ed1b48da31e69eaa3a9fb94', 'https://github.com/nicolargo/glances/commit/85d5a6b4af31fcf785d5a61086cbbd166b40b07a', 'https://github.com/nicolargo/glances/commit/9d6051be4a42f692392049fdbfc85d5dfa458b32'} | null |
PyPI | PYSEC-2014-44 | null | Cross-site scripting (XSS) vulnerability in safe_html.py in Plone before 4.2.3 and 4.3 before beta 1 allows remote authenticated users with permissions to edit content to inject arbitrary web script or HTML via unspecified vectors. | {'CVE-2012-5502'} | 2021-09-01T08:44:30.658658Z | 2014-09-30T14:55:00Z | null | null | null | {'https://github.com/plone/Products.CMFPlone/blob/4.2.3/docs/CHANGES.txt', 'http://www.openwall.com/lists/oss-security/2012/11/10/1', 'https://plone.org/products/plone-hotfix/releases/20121106', 'https://plone.org/products/plone/security/advisories/20121106/18'} | null |
PyPI | PYSEC-2021-545 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via `CHECK`-fail in `tf.strings.substr` with invalid arguments. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29617', 'GHSA-mmq6-q8r3-48fm'} | 2021-12-09T06:35:01.587221Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mmq6-q8r3-48fm', 'https://github.com/tensorflow/issues/46900', 'https://github.com/tensorflow/issues/46974', 'https://github.com/tensorflow/tensorflow/commit/890f7164b70354c57d40eda52dcdd7658677c09f'} | null |
PyPI | PYSEC-2021-400 | null | TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing `nullptr`s or via `CHECK`-failures) as well as abuse undefined behavior (binding references to `nullptr`s). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. We will deprecate TensorFlow's boosted trees APIs in subsequent releases. 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-57wx-m983-2f88', 'CVE-2021-41208'} | 2021-11-13T06:52:43.429056Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-57wx-m983-2f88', 'https://github.com/tensorflow/tensorflow/commit/5c8c9a8bfe750f9743d0c859bae112060b216f5c'} | null |
PyPI | PYSEC-2019-153 | null | modulemd 1.3.1 and earlier uses an unsafe function for processing externally provided data, leading to remote code execution. | {'CVE-2017-1002157', 'GHSA-jhjh-ghwx-6h7r'} | 2021-07-05T00:01:22.789825Z | 2019-01-10T21:29:00Z | null | null | null | {'https://pagure.io/modulemd/issue/55', 'https://github.com/advisories/GHSA-jhjh-ghwx-6h7r'} | null |
PyPI | GHSA-j8qc-5fqr-52fp | Division by zero in `Conv2DBackpropFilter` | ### Impact
An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`:
```python
import tensorflow as tf
input_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
filter_sizes = tf.constant([0, 0, 0, 0], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
tf.raw_ops.Conv2DBackpropFilter(
input=input_tensor,
filter_sizes=filter_sizes,
out_backprop=out_backprop,
strides=[1, 1, 1, 1],
use_cudnn_on_gpu=False,
padding='SAME',
explicit_paddings=[],
data_format='NHWC',
dilations=[1, 1, 1, 1]
)
```
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):
```cc
const size_t size_A = output_image_size * filter_total_size;
const size_t size_B = output_image_size * dims.out_depth;
const size_t size_C = filter_total_size * dims.out_depth;
const size_t work_unit_size = size_A + size_B + size_C;
const size_t shard_size = (target_working_set_size + work_unit_size - 1) / work_unit_size;
```
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.
### Patches
We have patched the issue in GitHub commit [c570e2ecfc822941335ad48f6e10df4e21f11c96](https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96).
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-2021-29538'} | 2022-03-03T05:13:10.370231Z | 2021-05-21T14:22:38Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29538', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qc-5fqr-52fp'} | null |
PyPI | PYSEC-2009-4 | null | Algorithmic complexity vulnerability in the forms library in Django 1.0 before 1.0.4 and 1.1 before 1.1.1 allows remote attackers to cause a denial of service (CPU consumption) via a crafted (1) EmailField (email address) or (2) URLField (URL) that triggers a large amount of backtracking in a regular expression. | {'CVE-2009-3695'} | 2021-07-15T02:22:07.960103Z | 2009-10-13T10:30:00Z | null | null | null | {'http://secunia.com/advisories/36968', 'http://www.debian.org/security/2009/dsa-1905', 'http://www.securityfocus.com/bid/36655', 'http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=550457', 'http://secunia.com/advisories/36948', 'http://groups.google.com/group/django-users/browse_thread/thread/15df9e45118dfc51/', 'https://exchange.xforce.ibmcloud.com/vulnerabilities/53727', 'http://www.djangoproject.com/weblog/2009/oct/09/security/', 'http://www.vupen.com/english/advisories/2009/2871', 'http://www.openwall.com/lists/oss-security/2009/10/13/6'} | null |
PyPI | GHSA-ch22-x2v3-v6vq | OTF-001: Improper Input Sanitation: The path parameter of the requested URL is not sanitized before being passed to the QT frontend | Between September 26, 2021 and October 8, 2021, [Radically Open Security](https://www.radicallyopensecurity.com/) conducted a penetration test of OnionShare 2.4, funded by the Open Technology Fund's [Red Team lab](https://www.opentech.fund/labs/red-team-lab/). This is an issue from that penetration test.
- Vulnerability ID: OTF-001
- Vulnerability type: Improper Input Sanitization
- Threat level: Elevated
## Description:
The `path` parameter of the requested URL is not sanitized before being passed to the QT frontend.
## Technical description:
The `path` parameter is not sanitized before being passed to the constructor of the `QLabel`.
https://github.com/onionshare/onionshare/blob/d08d5f0f32f755f504494d80794886f346fbafdb/desktop/src/onionshare/tab/mode/__init__.py#L499-L509
https://github.com/onionshare/onionshare/blob/d08d5f0f32f755f504494d80794886f346fbafdb/desktop/src/onionshare/tab/mode/history.py#L456-L483
https://doc.qt.io/qt-5/qlabel.html#details
> Warning: When passing a QString to the constructor or calling setText(), make sure to sanitize your input, as QLabel tries to guess whether it displays the text as plain text or as rich text, a subset of HTML 4 markup. You may want to call setTextFormat() explicitly, e.g. in case you expect the text to be in plain format but cannot control the text source (for instance when displaying data loaded from the Web).
This path is used in all components for displaying the server access history. This leads to a rendered HTML4 Subset (QT RichText editor) in the Onionshare frontend.
In the following example an adversary injects a crafted image file into an Onionshare instance with receive mode and renders it in the history component of the Onionshare application.
The only requirement is another visit to the shared site with the following parameter attached to the path of the URL:
```
<img src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAIAAAACUFjqAAAAFElEQVQY02Nk+M+ABzAxMIxKYwIAQC0BEwZFOw4AAAAASUVORK5CYII=' />
```
This will be rendered as a green square in the history tab where the path value is supposed to be (the value itself is shown at the bottom of the page).
Possible scenarios where this could lead to remote code execution would be a 0-day in libpng or other internal image rendering (OTF-014 (page 12)) of the QT framework.
The QT documentation indicates that external files could be rendered, but we were unable to find a QT code path allowing for it.
## Impact:
An adversary with knowledge of the Onion service address in public mode or with authentication in private mode can render arbitrary HTML (QT-HTML4 Subset) in the server desktop application. This requires the desktop application with rendered history, therefore the impact is only elevated.
## Recommendation:
- Manually define the text format of the QLabel via `setTextFormat()` | {'CVE-2022-21690'} | 2022-03-03T05:14:16.317559Z | 2022-01-21T23:20:25Z | HIGH | null | {'CWE-79'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-21690', 'https://github.com/onionshare/onionshare/security/advisories/GHSA-ch22-x2v3-v6vq', 'https://github.com/onionshare/onionshare/releases/tag/v2.5', 'https://github.com/onionshare/onionshare'} | null |
PyPI | PYSEC-2018-88 | null | The mpatch_apply function in mpatch.c in Mercurial before 4.6.1 incorrectly proceeds in cases where the fragment start is past the end of the original data, aka OVE-20180430-0004. | {'CVE-2018-13346'} | 2021-08-27T03:22:07.239369Z | 2018-07-06T00:29:00Z | null | null | null | {'https://www.mercurial-scm.org/wiki/WhatsNew#Mercurial_4.6.1_.282018-06-06.29', 'https://access.redhat.com/errata/RHSA-2019:2276', 'https://lists.debian.org/debian-lts-announce/2020/07/msg00032.html', 'https://www.mercurial-scm.org/repo/hg/rev/faa924469635'} | null |
PyPI | PYSEC-2022-33 | null | b2-sdk-python is a python library to access cloud storage provided by backblaze. Linux and Mac releases of the SDK version 1.14.0 and below contain a key disclosure vulnerability that, in certain conditions, can be exploited by local attackers through a time-of-check-time-of-use (TOCTOU) race condition. SDK users of the SqliteAccountInfo format are vulnerable while users of the InMemoryAccountInfo format are safe. The SqliteAccountInfo saves API keys (and bucket name-to-id mapping) in a local database file ($XDG_CONFIG_HOME/b2/account_info, ~/.b2_account_info or a user-defined path). When first created, the file is world readable and is (typically a few milliseconds) later altered to be private to the user. If the directory containing the file is readable by a local attacker then during the brief period between file creation and permission modification, a local attacker can race to open the file and maintain a handle to it. This allows the local attacker to read the contents after the file after the sensitive information has been saved to it. Consumers of this SDK who rely on it to save data using SqliteAccountInfo class should upgrade to the latest version of the SDK. Those who believe a local user might have opened a handle using this race condition, should remove the affected database files and regenerate all application keys. Users should upgrade to b2-sdk-python 1.14.1 or later. | {'CVE-2022-23651', 'GHSA-p867-fxfr-ph2w'} | 2022-03-07T17:33:46.032301Z | 2022-02-23T23:15:00Z | null | null | null | {'https://github.com/Backblaze/b2-sdk-python/commit/62476638986e5b6d7459aca5ef8ce220760226e0', 'https://pypi.org/project/b2sdk/', 'https://github.com/Backblaze/b2-sdk-python/security/advisories/GHSA-p867-fxfr-ph2w'} | null |
PyPI | PYSEC-2020-284 | 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'} | 2021-12-09T06:34:42.248668Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8gv-q7wr-9jf8', 'https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html'} | null |
PyPI | GHSA-h92m-42h4-82f6 | High severity vulnerability that affects postfix-mta-sts-resolver | ## Incorrect query parsing
### Impact
All users of versions prior to 0.5.1 can receive incorrect response from daemon under rare conditions, rendering downgrade of effective STS policy.
### Patches
Problem has been patched in version 0.5.1
### Workarounds
Users may remediate this vulnerability without upgrading by applying [these patches](https://gist.github.com/Snawoot/b9da85d6b26dea5460673b29df1adc6b) to older suppoorted versions.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [postfix-mta-sts-resolver repo](https://github.com/Snawoot/postfix-mta-sts-resolver)
* Email me at [vladislav at vm-0 dot com](mailto:vladislav-ex-gh-advisory@vm-0.com) | {'CVE-2019-16791'} | 2022-03-03T05:14:15.970623Z | 2019-07-05T21:06:58Z | HIGH | null | {'CWE-757'} | {'https://gist.github.com/Snawoot/b9da85d6b26dea5460673b29df1adc6b', 'https://github.com/Snawoot/postfix-mta-sts-resolver/security/advisories/GHSA-h92m-42h4-82f6', 'https://github.com/advisories/GHSA-h92m-42h4-82f6', 'https://nvd.nist.gov/vuln/detail/CVE-2019-16791'} | null |
PyPI | PYSEC-2021-284 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can generate undefined behavior via a reference binding to nullptr in `BoostedTreesCalculateBestGainsPerFeature` and similar attack can occur in `BoostedTreesCalculateBestFeatureSplitV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) does not validate the input values. We have patched the issue in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and in commit 429f009d2b2c09028647dd4bb7b3f6f414bbaad7. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37662', 'GHSA-f5cx-5wr3-5qrc'} | 2021-08-27T03:22:45.118929Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/429f009d2b2c09028647dd4bb7b3f6f414bbaad7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f5cx-5wr3-5qrc', 'https://github.com/tensorflow/tensorflow/commit/9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad'} | null |
PyPI | PYSEC-2022-1 | null | An issue was discovered in Django 2.2 before 2.2.26, 3.2 before 3.2.11, and 4.0 before 4.0.1. UserAttributeSimilarityValidator incurred significant overhead in evaluating a submitted password that was artificially large in relation to the comparison values. In a situation where access to user registration was unrestricted, this provided a potential vector for a denial-of-service attack. | {'CVE-2021-45115', 'GHSA-53qw-q765-4fww'} | 2022-01-05T02:16:15.291872Z | 2022-01-05T00:15:00Z | null | null | null | {'https://groups.google.com/forum/#!forum/django-announce', 'https://www.djangoproject.com/weblog/2022/jan/04/security-releases/', 'https://github.com/advisories/GHSA-53qw-q765-4fww', 'https://docs.djangoproject.com/en/4.0/releases/security/'} | null |
PyPI | PYSEC-2021-791 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of fully connected layers in TFLite is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226). We have patched the issue in GitHub commit 718721986aa137691ee23f03638867151f74935f. 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-2021-37680', 'GHSA-cfpj-3q4c-jhvr'} | 2021-12-09T06:35:39.345760Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cfpj-3q4c-jhvr', 'https://github.com/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f'} | null |
PyPI | GHSA-5h6m-9mvx-m6c5 | Moderate severity vulnerability that affects mayan-edms | An issue was discovered in Mayan EDMS before 3.0.3. The Tags app has XSS because tag label values are mishandled. | {'CVE-2018-16407'} | 2022-03-03T05:14:19.096278Z | 2018-09-06T03:25:03Z | MODERATE | null | {'CWE-79'} | {'https://gitlab.com/mayan-edms/mayan-edms/commit/076468a9225e4630a463c0bbceb8e5b805fe380c', 'https://gitlab.com/mayan-edms/mayan-edms/issues/496', 'https://gitlab.com/mayan-edms/mayan-edms', 'https://github.com/advisories/GHSA-5h6m-9mvx-m6c5', 'https://gitlab.com/mayan-edms/mayan-edms/blob/master/HISTORY.rst', 'https://nvd.nist.gov/vuln/detail/CVE-2018-16407'} | null |
PyPI | PYSEC-2021-74 | null | In SaltStack Salt before 3002.5, authentication to VMware vcenter, vsphere, and esxi servers (in the vmware.py files) does not always validate the SSL/TLS certificate. | {'CVE-2020-28972'} | 2021-03-31T14:15:00Z | 2021-02-27T05:15:00Z | null | null | null | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/YOGNT2XWPOYV7YT75DN7PS4GIYWFKOK5/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/FUGLOJ6NXLCIFRD2JTXBYQEMAEF2B6XH/', 'https://saltproject.io/security_announcements/active-saltstack-cve-release-2021-feb-25/', 'https://security.gentoo.org/glsa/202103-01', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/7GRVZ5WAEI3XFN2BDTL6DDXFS5HYSDVB/'} | null |
PyPI | GHSA-cq94-qf6q-mf2h | Moderate severity vulnerability that affects pysaml2 | Python package pysaml2 version 4.4.0 and earlier reuses the initialization vector across encryptions in the IDP server, resulting in weak encryption of data. | {'CVE-2017-1000246'} | 2022-03-03T05:13:39.961229Z | 2018-07-16T16:50:30Z | MODERATE | null | {'CWE-330'} | {'https://github.com/rohe/pysaml2/issues/417', 'https://nvd.nist.gov/vuln/detail/CVE-2017-1000246', 'https://github.com/advisories/GHSA-cq94-qf6q-mf2h', 'https://github.com/rohe/pysaml2'} | null |
PyPI | GHSA-6j9c-grc6-5m6g | CHECK-fail in SparseConcat | ### Impact
An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.SparseConcat`:
```python
import tensorflow as tf
import numpy as np
indices_1 = tf.constant([[514, 514], [514, 514]], dtype=tf.int64)
indices_2 = tf.constant([[514, 530], [599, 877]], dtype=tf.int64)
indices = [indices_1, indices_2]
values_1 = tf.zeros([0], dtype=tf.int64)
values_2 = tf.zeros([0], dtype=tf.int64)
values = [values_1, values_2]
shape_1 = tf.constant([442, 514, 514, 515, 606, 347, 943, 61, 2], dtype=tf.int64)
shape_2 = tf.zeros([9], dtype=tf.int64)
shapes = [shape_1, shape_2]
tf.raw_ops.SparseConcat(indices=indices, values=values, shapes=shapes, concat_dim=2)
```
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:
```cc
TensorShape input_shape(shapes[0].vec<int64>());
```
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.
```cc
template <class Shape>
TensorShapeBase<Shape>::TensorShapeBase(gtl::ArraySlice<int64> dim_sizes) {
set_tag(REP16);
set_data_type(DT_INVALID);
TF_CHECK_OK(InitDims(dim_sizes));
}
```
In our scenario, this occurs when adding a dimension from the argument results in overflow:
```cc
template <class Shape>
Status TensorShapeBase<Shape>::InitDims(gtl::ArraySlice<int64> dim_sizes) {
...
Status status = Status::OK();
for (int64 s : dim_sizes) {
status.Update(AddDimWithStatus(internal::SubtleMustCopy(s)));
if (!status.ok()) {
return status;
}
}
}
template <class Shape>
Status TensorShapeBase<Shape>::AddDimWithStatus(int64 size) {
...
int64 new_num_elements;
if (kIsPartial && (num_elements() < 0 || size < 0)) {
new_num_elements = -1;
} else {
new_num_elements = MultiplyWithoutOverflow(num_elements(), size);
if (TF_PREDICT_FALSE(new_num_elements < 0)) {
return errors::Internal("Encountered overflow when multiplying ",
num_elements(), " with ", size,
", result: ", new_num_elements);
}
}
...
}
```
This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows.
### Patches
We have patched the issue in GitHub commit [69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c](https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c).
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-2021-29534'} | 2022-03-03T05:13:13.591077Z | 2021-05-21T14:22:24Z | LOW | null | {'CWE-754'} | {'https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6j9c-grc6-5m6g', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29534'} | null |
PyPI | GHSA-32gv-6cf3-wcmq | HTTP/2 DoS Attacks: Ping, Reset, and Settings Floods | ### Impact
Twisted web servers that utilize the optional HTTP/2 support suffer from the following flow-control related vulnerabilities:
Ping flood: https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-9512
Reset flood: https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-9514
Settings flood: https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-9515
A Twisted web server supports HTTP/2 requests if you've installed the [`http2` optional dependency set](https://twistedmatrix.com/documents/19.2.0/installation/howto/optional.html).
### Workarounds
There are no workarounds.
### References
https://github.com/Netflix/security-bulletins/blob/master/advisories/third-party/2019-002.md
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Twisted's Trac](https://twistedmatrix.com/trac/)
| null | 2022-03-14T23:02:03.879779Z | 2022-03-14T22:45:11Z | CRITICAL | null | null | {'https://github.com/twisted/twisted/security/advisories/GHSA-32gv-6cf3-wcmq', 'https://github.com/twisted/twisted'} | null |
PyPI | GHSA-92x2-jw7w-xvvx | Cookie and header exposure in twisted | ### Impact
Cookie and Authorization headers are leaked when following cross-origin redirects in `twited.web.client.RedirectAgent` and `twisted.web.client.BrowserLikeRedirectAgent`. | {'CVE-2022-21712'} | 2022-03-07T20:47:56.478654Z | 2022-02-07T22:36:00Z | HIGH | null | {'CWE-346', 'CWE-200'} | {'https://github.com/twisted/twisted/commit/af8fe78542a6f2bf2235ccee8158d9c88d31e8e2', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21712', 'https://github.com/twisted/twisted/releases/tag/twisted-22.1.0', 'https://lists.debian.org/debian-lts-announce/2022/02/msg00021.html', 'https://github.com/twisted/twisted', 'https://github.com/twisted/twisted/security/advisories/GHSA-92x2-jw7w-xvvx'} | null |
PyPI | PYSEC-2021-512 | 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 caused by an integer overflow in constructing a new tensor shape. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/0908c2f2397c099338b901b067f6495a5b96760b/tensorflow/core/kernels/sparse_split_op.cc#L66-L70) builds a dense shape without checking that the dimensions would not result in overflow. 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. | {'GHSA-xvjm-fvxx-q3hv', 'CVE-2021-29584'} | 2021-12-09T06:34:56.381620Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xvjm-fvxx-q3hv', 'https://github.com/tensorflow/tensorflow/commit/4c0ee937c0f61c4fc5f5d32d9bb4c67428012a60'} | null |
PyPI | PYSEC-2014-13 | null | Requests (aka python-requests) before 2.3.0 allows remote servers to obtain a netrc password by reading the Authorization header in a redirected request. | {'CVE-2014-1829'} | 2021-07-05T00:01:25.632991Z | 2014-10-15T14:55:00Z | null | null | null | {'http://advisories.mageia.org/MGASA-2014-0409.html', 'https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=733108', 'http://www.mandriva.com/security/advisories?name=MDVSA-2015:133', 'http://www.debian.org/security/2015/dsa-3146', 'https://github.com/kennethreitz/requests/issues/1885', 'http://www.ubuntu.com/usn/USN-2382-1'} | null |
PyPI | PYSEC-2021-142 | null | A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747. | {'CVE-2020-14343', 'GHSA-8q59-q68h-6hv4'} | 2021-08-27T03:22:18.913334Z | 2021-02-09T21:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-8q59-q68h-6hv4', 'https://bugzilla.redhat.com/show_bug.cgi?id=1860466'} | null |
PyPI | GHSA-c582-c96p-r5cq | Memory exhaustion in Tensorflow | ### Impact
The [implementation of `ThreadPoolHandle`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/data/experimental/threadpool_dataset_op.cc#L79-L135) can be used to trigger a denial of service attack by allocating too much memory:
```python
import tensorflow as tf
y = tf.raw_ops.ThreadPoolHandle(num_threads=0x60000000,display_name='tf')
```
This is because the `num_threads` argument is only checked to not be negative, but there is no upper bound on its value.
### Patches
We have patched the issue in GitHub commit [e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e](https://github.com/tensorflow/tensorflow/commit/e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e).
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 Yu Tian of Qihoo 360 AIVul Team. | {'CVE-2022-21732'} | 2022-03-03T05:13:08.731915Z | 2022-02-10T00:20:29Z | MODERATE | null | {'CWE-770', 'CWE-400'} | {'https://github.com/tensorflow/tensorflow/commit/e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e', 'https://nvd.nist.gov/vuln/detail/CVE-2022-21732', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/data/experimental/threadpool_dataset_op.cc#L79-L135', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c582-c96p-r5cq', 'https://github.com/tensorflow/tensorflow/'} | null |
PyPI | PYSEC-2018-30 | null | SaltStack Salt before 2017.7.8 and 2018.3.x before 2018.3.3 allow remote attackers to bypass authentication and execute arbitrary commands via salt-api(netapi). | {'CVE-2018-15751'} | 2021-06-10T06:51:17.561337Z | 2018-10-24T22:29:00Z | null | null | null | {'https://lists.debian.org/debian-lts-announce/2020/07/msg00024.html', 'https://docs.saltstack.com/en/latest/topics/releases/2018.3.3.html', 'https://docs.saltstack.com/en/2017.7/topics/releases/2017.7.8.html', 'https://groups.google.com/d/msg/salt-users/dimVF7rpphY/jn3Xv3MbBQAJ', 'https://groups.google.com/d/msg/salt-users/L9xqcJ0UXxs/qgDj42obBQAJ', 'https://usn.ubuntu.com/4459-1/', 'http://lists.opensuse.org/opensuse-security-announce/2020-07/msg00070.html'} | null |
PyPI | GHSA-9p77-mmrw-69c7 | Null-dereference in Tensorflow | ### Impact
When decoding a tensor from protobuf, TensorFlow might do a null-dereference if attributes of some mutable arguments to some operations are missing from the proto. This is [guarded by a `DCHECK`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/full_type_util.cc#L104-L106):
```cc
const auto* attr = attrs.Find(arg->s());
DCHECK(attr != nullptr);
if (attr->value_case() == AttrValue::kList) {
// ...
}
```
However, `DCHECK` is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the dereferencing of the null pointer, whereas in the second case it results in a crash due to the assertion failure.
### Patches
We have patched the issue in GitHub commit [8a513cec4bec15961fbfdedcaa5376522980455c](https://github.com/tensorflow/tensorflow/commit/8a513cec4bec15961fbfdedcaa5376522980455c).
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, 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-2022-23570'} | 2022-03-03T05:13:46.493647Z | 2022-02-09T23:33:35Z | MODERATE | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-23570', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9p77-mmrw-69c7', 'https://github.com/tensorflow/tensorflow/commit/8a513cec4bec15961fbfdedcaa5376522980455c', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/full_type_util.cc#L104-L106', 'https://github.com/tensorflow/tensorflow/'} | null |
PyPI | PYSEC-2021-841 | null | In CKAN, versions 2.9.0 to 2.9.3 are affected by a stored XSS vulnerability via SVG file upload of users’ profile picture. This allows low privileged application users to store malicious scripts in their profile picture. These scripts are executed in a victim’s browser when they open the malicious profile picture | {'GHSA-6w9p-88qg-p3g3', 'CVE-2021-25967'} | 2021-12-13T06:35:10.687046Z | 2021-12-01T14:15:00Z | null | null | null | {'https://www.whitesourcesoftware.com/vulnerability-database/CVE-2021-25967', 'https://github.com/advisories/GHSA-6w9p-88qg-p3g3'} | null |
PyPI | PYSEC-2017-4 | null | A flaw was found in the way Ansible (2.3.x before 2.3.3, and 2.4.x before 2.4.1) passed certain parameters to the jenkins_plugin module. Remote attackers could use this flaw to expose sensitive information from a remote host's logs. This flaw was fixed by not allowing passwords to be specified in the "params" argument, and noting this in the module documentation. | {'CVE-2017-7550'} | 2021-07-02T02:41:33.938371Z | 2017-11-21T17:29:00Z | null | null | null | {'https://github.com/ansible/ansible/issues/30874', 'https://access.redhat.com/errata/RHSA-2017:2966', 'https://bugzilla.redhat.com/show_bug.cgi?id=1473645'} | null |
PyPI | PYSEC-2020-142 | null | A mis-handling of invalid unicode characters in the Java implementation of Tink versions prior to 1.5 allows an attacker to change the ID part of a ciphertext, which result in the creation of a second ciphertext that can decrypt to the same plaintext. This can be a problem with encrypting deterministic AEAD with a single key, and rely on a unique ciphertext-per-plaintext. | {'CVE-2020-8929', 'GHSA-g5vf-v6wf-7w2r'} | 2020-10-29T22:16:00Z | 2020-10-19T13:15:00Z | null | null | null | {'https://github.com/google/tink/security/advisories/GHSA-g5vf-v6wf-7w2r', 'https://github.com/google/tink/commit/93d839a5865b9d950dffdc9d0bc99b71280a8899'} | null |
PyPI | GHSA-86hp-cj9j-33vv | Insertion of Sensitive Information into Log File, Invocation of Process Using Visible Sensitive Information, and Exposure of Sensitive Information to an Unauthorized Actor in Ansible | A security flaw was found in Ansible Engine, all Ansible 2.7.x versions prior to 2.7.17, all Ansible 2.8.x versions prior to 2.8.11 and all Ansible 2.9.x versions prior to 2.9.7, when managing kubernetes using the k8s module. Sensitive parameters such as passwords and tokens are passed to kubectl from the command line, not using an environment variable or an input configuration file. This will disclose passwords and tokens from process list and no_log directive from debug module would not have any effect making these secrets being disclosed on stdout and log files. | {'CVE-2020-1753'} | 2022-04-07T15:17:06.175232Z | 2021-04-07T20:33:26Z | MODERATE | null | {'CWE-532', 'CWE-200'} | {'https://github.com/ansible-collections/kubernetes/pull/51', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/WQVOQD4VAIXXTVQAJKTN7NUGTJFE2PCB/', 'https://www.debian.org/security/2021/dsa-4950', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MRRYUU5ZBLPBXCYG6CFP35D64NP2UB2S/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DKPA4KC3OJSUFASUYMG66HKJE7ADNGFW/', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2020-1753', 'https://github.com/ansible-collections/kubernetes', 'https://nvd.nist.gov/vuln/detail/CVE-2020-1753', 'https://security.gentoo.org/glsa/202006-11'} | null |
PyPI | PYSEC-2022-64 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseCountSparseOutput` is vulnerable to a heap overflow. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'CVE-2022-21740', 'GHSA-44qp-9wwf-734r'} | 2022-03-09T00:17:31.800762Z | 2022-02-03T15:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/2b7100d6cdff36aa21010a82269bc05a6d1cc74a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-44qp-9wwf-734r', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/count_ops.cc#L168-L273', 'https://github.com/tensorflow/tensorflow/commit/adbbabdb0d3abb3cdeac69e38a96de1d678b24b3'} | null |
PyPI | PYSEC-2020-59 | null | ** DISPUTED ** TAXII libtaxii through 1.1.117, as used in EclecticIQ OpenTAXII through 0.2.0 and other products, allows SSRF via an initial http:// substring to the parse method, even when the no_network setting is used for the XML parser. NOTE: the vendor points out that the parse method "wraps the lxml library" and that this may be an issue to "raise ... to the lxml group." | {'GHSA-836c-xg97-8p4h', 'CVE-2020-27197'} | 2020-10-27T19:51:00Z | 2020-10-17T20:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-836c-xg97-8p4h', 'https://github.com/TAXIIProject/libtaxii/issues/246', 'https://github.com/eclecticiq/OpenTAXII/issues/176', 'http://packetstormsecurity.com/files/159662/Libtaxii-1.1.117-OpenTaxi-0.2.0-Server-Side-Request-Forgery.html'} | null |
PyPI | PYSEC-2019-104 | null | ** DISPUTED ** core.py in Mitogen before 0.2.8 has a typo that drops the unidirectional-routing protection mechanism in the case of a child that is initiated by another child. The Ansible extension is unaffected. NOTE: the vendor disputes this issue because it is exploitable only in conjunction with hypothetical other factors, i.e., an affected use case within a library caller, and a bug in the message receiver policy code that led to reliance on this extra protection mechanism. | {'CVE-2019-15149', 'GHSA-8rf6-w2mx-4xjh'} | 2019-08-30T11:38:00Z | 2019-08-18T20:15:00Z | null | null | null | {'https://mitogen.networkgenomics.com/changelog.html#v0-2-8-2019-08-18', 'https://github.com/dw/mitogen/commit/5924af1566763e48c42028399ea0cd95c457b3dc', 'https://github.com/advisories/GHSA-8rf6-w2mx-4xjh'} | null |
PyPI | GHSA-22wc-c9wj-6q2v | VVE-2021-0001: Memory corruption using function calls within arrays | ### Impact
When performing a function call inside an array, there is a memory corruption issue that occurs because of an incorrect pointer to the the tip of the stack.
### Patches
This issue was partially fixed in [VVE-2020-0004](https://github.com/vyperlang/vyper/security/advisories/GHSA-2r3x-4mrv-mcxf), however the fix did not update similar code for arrays, which had a similar issue. The issue is fully fixed in https://github.com/vyperlang/vyper/pull/2345 | null | 2022-03-03T05:13:32.598774Z | 2021-04-19T15:12:05Z | MODERATE | null | {'CWE-129'} | {'https://github.com/vyperlang/vyper/pull/2345', 'https://pypi.org/project/vyper', 'https://github.com/vyperlang/vyper/security/advisories/GHSA-22wc-c9wj-6q2v', 'https://github.com/vyperlang/vyper/commit/11b7b5b7e59bc9dc859d51cd41a924b59fe47c9e'} | null |
PyPI | PYSEC-2021-457 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value. For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, in the interpolation code(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer overflow. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-jfp7-4j67-8r3q', 'CVE-2021-29529'} | 2021-12-09T06:34:47.879310Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jfp7-4j67-8r3q'} | null |
PyPI | PYSEC-2018-104 | null | python-oslo-middleware before versions 3.8.1, 3.19.1, 3.23.1 is vulnerable to an information disclosure. Software using the CatchError class could include sensitive values in a traceback's error message. System users could exploit this flaw to obtain sensitive information from OpenStack component error logs (for example, keystone tokens). | {'CVE-2017-2592'} | 2021-11-16T21:20:29.327956Z | 2018-05-08T17:29:00Z | null | null | null | {'http://lists.openstack.org/pipermail/openstack-announce/2017-January/002002.html', 'https://access.redhat.com/errata/RHSA-2017:0435', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2017-2592', 'https://usn.ubuntu.com/3666-1/', 'http://rhn.redhat.com/errata/RHSA-2017-0435.html', 'https://access.redhat.com/errata/RHSA-2017:0300', 'https://review.openstack.org/#/c/425734/', 'http://www.securityfocus.com/bid/95827', 'https://review.openstack.org/#/c/425732/', 'https://bugs.launchpad.net/keystonemiddleware/+bug/1628031', 'http://rhn.redhat.com/errata/RHSA-2017-0300.html', 'https://review.openstack.org/#/c/425730/'} | null |
PyPI | GHSA-462w-v97r-4m45 | High severity vulnerability that affects Jinja2 | In Pallets Jinja before 2.10.1, str.format_map allows a sandbox escape. | {'CVE-2019-10906'} | 2022-03-03T05:13:52.460090Z | 2019-04-10T14:30:24Z | HIGH | null | null | {'https://nvd.nist.gov/vuln/detail/CVE-2019-10906', 'https://lists.apache.org/thread.html/2b52b9c8b9d6366a4f1b407a8bde6af28d9fc73fdb3b37695fd0d9ac@%3Cdevnull.infra.apache.org%3E', 'https://github.com/advisories/GHSA-462w-v97r-4m45', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/QCDYIS254EJMBNWOG4S5QY6AOTOR4TZU/', 'http://lists.opensuse.org/opensuse-security-announce/2019-05/msg00030.html', 'https://lists.apache.org/thread.html/57673a78c4d5c870d3f21465c7e2946b9f8285c7c57e54c2ae552f02@%3Ccommits.airflow.apache.org%3E', 'https://usn.ubuntu.com/4011-1/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TS7IVZAJBWOHNRDMFJDIZVFCMRP6YIUQ/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/DSW3QZMFVVR7YE3UT4YRQA272TYAL5AF/', 'https://lists.apache.org/thread.html/b2380d147b508bbcb90d2cad443c159e63e12555966ab4f320ee22da@%3Ccommits.airflow.apache.org%3E', 'https://usn.ubuntu.com/4011-2/', 'https://palletsprojects.com/blog/jinja-2-10-1-released', 'https://lists.apache.org/thread.html/46c055e173b52d599c648a98199972dbd6a89d2b4c4647b0500f2284@%3Cdevnull.infra.apache.org%3E', 'https://access.redhat.com/errata/RHSA-2019:1329', 'https://lists.apache.org/thread.html/320441dccbd9a545320f5f07306d711d4bbd31ba43dc9eebcfc602df@%3Cdevnull.infra.apache.org%3E', 'https://access.redhat.com/errata/RHSA-2019:1237', 'https://lists.apache.org/thread.html/7f39f01392d320dfb48e4901db68daeece62fd60ef20955966739993@%3Ccommits.airflow.apache.org%3E', 'http://lists.opensuse.org/opensuse-security-announce/2019-06/msg00064.html', 'https://access.redhat.com/errata/RHSA-2019:1152', 'https://lists.apache.org/thread.html/f0c4a03418bcfe70c539c5dbaf99c04c98da13bfa1d3266f08564316@%3Ccommits.airflow.apache.org%3E', 'https://lists.apache.org/thread.html/09fc842ff444cd43d9d4c510756fec625ef8eb1175f14fd21de2605f@%3Cdevnull.infra.apache.org%3E'} | null |
PyPI | PYSEC-2021-768 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all operations of type `tf.raw_ops.MatrixDiagV*`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of `k` is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. We have patched the issue in GitHub commit f2a673bd34f0d64b8e40a551ac78989d16daad09. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37657', 'GHSA-5xwc-mrhx-5g3m'} | 2021-12-09T06:35:37.257593Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5xwc-mrhx-5g3m', 'https://github.com/tensorflow/tensorflow/commit/f2a673bd34f0d64b8e40a551ac78989d16daad09'} | null |
PyPI | PYSEC-2021-292 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.UpperBound`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/searchsorted_op.cc#L85-L104) does not validate the rank of `sorted_input` argument. A similar issue occurs in `tf.raw_ops.LowerBound`. We have patched the issue in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38. 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-9697-98pf-4rw7', 'CVE-2021-37670'} | 2021-08-27T03:22:45.845259Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/42459e4273c2e47a3232cc16c4f4fff3b3a35c38', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9697-98pf-4rw7'} | null |
PyPI | PYSEC-2021-338 | null | Leo Editor v6.2.1 was discovered to contain a regular expression denial of service (ReDoS) vulnerability in the component plugins/importers/dart.py. | {'GHSA-x38q-xg2h-rxgx', 'CVE-2020-23478'} | 2021-09-26T23:50:00.616119Z | 2021-09-22T20:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-x38q-xg2h-rxgx', 'https://github.com/leo-editor/leo-editor/issues/1597'} | null |
PyPI | PYSEC-2020-292 | 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'} | 2021-12-09T06:34:43.741009Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} | null |
PyPI | GHSA-fx83-3ph3-9j2q | Cross-site Scripting (XSS) in Django REST Framework | A flaw was found in Django REST Framework versions before 3.12.0 and before 3.11.2. When using the browseable API viewer, Django REST Framework fails to properly escape certain strings that can come from user input. This allows a user who can control those strings to inject malicious <script> tags, leading to a cross-site-scripting (XSS) vulnerability. | {'CVE-2020-25626'} | 2022-03-03T05:14:17.513282Z | 2021-03-19T21:32:47Z | MODERATE | null | {'CWE-20', 'CWE-77', 'CWE-79'} | {'https://bugzilla.redhat.com/show_bug.cgi?id=1878635', 'https://security.netapp.com/advisory/ntap-20201016-0003/', 'https://nvd.nist.gov/vuln/detail/CVE-2020-25626'} | null |
PyPI | GHSA-627q-g293-49q7 | Abort caused by allocating a vector that is too large in Tensorflow | ### Impact
During shape inference, TensorFlow can [allocate a large vector](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.cc#L788-L790) based on a value from a tensor controlled by the user:
```cc
const auto num_dims = Value(shape_dim);
std::vector<DimensionHandle> dims;
dims.reserve(num_dims);
```
### Patches
We have patched the issue in GitHub commit [1361fb7e29449629e1df94d44e0427ebec8c83c7](https://github.com/tensorflow/tensorflow/commit/1361fb7e29449629e1df94d44e0427ebec8c83c7).
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-2022-23580'} | 2022-03-03T05:12:37.453930Z | 2022-02-07T22:01:24Z | MODERATE | null | {'CWE-400'} | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.cc#L788-L790', 'https://github.com/tensorflow/tensorflow/commit/1361fb7e29449629e1df94d44e0427ebec8c83c7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-627q-g293-49q7', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23580', 'https://github.com/tensorflow/tensorflow/'} | null |
PyPI | GHSA-jfp7-4j67-8r3q | Heap buffer overflow caused by rounding | ### Impact
An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements:
```python
import tensorflow as tf
l = [256, 328, 361, 17, 361, 361, 361, 361, 361, 361, 361, 361, 361, 361, 384]
images = tf.constant(l, shape=[1, 1, 15, 1], dtype=tf.qint32)
size = tf.constant([12, 6], shape=[2], dtype=tf.int32)
min = tf.constant(80.22522735595703)
max = tf.constant(80.39215850830078)
tf.raw_ops.QuantizedResizeBilinear(images=images, size=size, min=min, max=max,
align_corners=True, half_pixel_centers=True)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value:
```cc
const float in_f = std::floor(in);
interpolation->lower[i] = std::max(static_cast<int64>(in_f), static_cast<int64>(0));
interpolation->upper[i] = std::min(static_cast<int64>(std::ceil(in)), in_size - 1);
```
For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, [in the interpolation code](https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer overflow:
```cc
template <int RESOLUTION, typename T, typename T_SCALE, typename T_CALC>
inline void OutputLerpForChannels(const InterpolationCache<T_SCALE>& xs,
const int64 x, const T_SCALE ys_ilerp,
const int channels, const float min,
const float max, const T* ys_input_lower_ptr,
const T* ys_input_upper_ptr,
T* output_y_ptr) {
const int64 xs_lower = xs.lower[x];
...
for (int c = 0; c < channels; ++c) {
const T top_left = ys_input_lower_ptr[xs_lower + c];
...
}
}
```
For the other cases where `interpolation->upper[i]` is smaller than `interpolation->lower[i]`, we can set them to be equal without affecting the output.
### Patches
We have patched the issue in GitHub commit [f851613f8f0fb0c838d160ced13c134f778e3ce7](https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7).
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-2021-29529'} | 2022-03-03T05:13:48.265071Z | 2021-05-21T14:22:05Z | LOW | null | {'CWE-131', 'CWE-193'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29529', 'https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jfp7-4j67-8r3q'} | null |
PyPI | GHSA-cqhg-xjhh-p8hf | Out-of-bounds reads in Pillow | Pillow before 6.2.3 and 7.x before 7.0.1 has multiple out-of-bounds reads in libImaging/FliDecode.c. | {'CVE-2020-10177'} | 2022-03-03T05:13:58.743050Z | 2020-07-27T21:52:43Z | MODERATE | null | {'CWE-125'} | {'https://github.com/python-pillow/Pillow/commits/master/src/libImaging', 'https://nvd.nist.gov/vuln/detail/CVE-2020-10177', 'https://github.com/python-pillow/Pillow/pull/4538', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/HOKHNWV2VS5GESY7IBD237E7C6T3I427/', 'https://usn.ubuntu.com/4430-1/', 'https://github.com/python-pillow/Pillow/pull/4503', 'https://snyk.io/vuln/SNYK-PYTHON-PILLOW-574573', 'https://github.com/python-pillow/Pillow', 'https://lists.debian.org/debian-lts-announce/2020/08/msg00012.html', 'https://pillow.readthedocs.io/en/stable/releasenotes/6.2.3.html', 'https://usn.ubuntu.com/4430-2/', 'https://pillow.readthedocs.io/en/stable/releasenotes/7.1.0.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/BEBCPE4F2VHTIT6EZA2YZQZLPVDEBJGD/'} | null |
PyPI | PYSEC-2020-338 | null | In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled. This issue is patched in TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched. TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected. Users are encouraged to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0. | {'GHSA-977j-xj7q-2jr9', 'CVE-2020-5215'} | 2021-12-09T06:35:16.944663Z | 2020-01-28T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/5ac1b9e24ff6afc465756edf845d2e9660bd34bf', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-977j-xj7q-2jr9', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.0.1', 'https://github.com/tensorflow/tensorflow/releases/tag/v1.15.2'} | null |
PyPI | GHSA-9w49-m7xh-5r39 | Cross-site scripting in papermerge | Multiple cross-site scripting (XSS) vulnerabilities in Papermerge before 1.5.2 allow remote attackers to inject arbitrary web script or HTML via the rename, tag, upload, or create folder function. The payload can be in a folder, a tag, or a document's filename. If email consumption is configured in Papermerge, a malicious document can be sent by email and is automatically uploaded into the Papermerge web application. Therefore, no authentication is required to exploit XSS if email consumption is configured. Otherwise authentication is required. | {'CVE-2020-29456'} | 2022-03-03T05:13:44.739804Z | 2021-04-20T16:37:56Z | MODERATE | null | {'CWE-79'} | {'https://www.papermerge.com/', 'https://github.com/ciur/papermerge/issues/228', 'https://nvd.nist.gov/vuln/detail/CVE-2020-29456', 'https://github.com/ciur/papermerge/releases/tag/v1.5.2'} | null |
PyPI | PYSEC-2015-28 | null | OpenStack Ironic Inspector (aka ironic-inspector or ironic-discoverd), when debug mode is enabled, might allow remote attackers to access the Flask console and execute arbitrary Python code by triggering an error. | {'CVE-2015-5306'} | 2021-07-25T23:34:38.274751Z | 2015-11-25T20:59:00Z | null | null | null | {'https://access.redhat.com/errata/RHSA-2015:1929', 'http://rhn.redhat.com/errata/RHSA-2015-2685.html', 'https://bugzilla.redhat.com/show_bug.cgi?id=1273698', 'https://bugs.launchpad.net/ironic-inspector/+bug/1506419'} | null |
PyPI | PYSEC-2017-15 | null | The serializer in html5lib before 0.99999999 might allow remote attackers to conduct cross-site scripting (XSS) attacks by leveraging mishandling of special characters in attribute values, a different vulnerability than CVE-2016-9909. | {'CVE-2016-9910'} | 2021-07-05T00:01:21.869008Z | 2017-02-22T16:59:00Z | null | null | null | {'http://www.openwall.com/lists/oss-security/2016/12/08/8', 'https://html5lib.readthedocs.io/en/latest/changes.html#b9', 'http://www.securityfocus.com/bid/95132', 'https://github.com/html5lib/html5lib-python/issues/12', 'https://github.com/html5lib/html5lib-python/issues/11', 'http://www.openwall.com/lists/oss-security/2016/12/06/5', 'https://github.com/html5lib/html5lib-python/commit/9b8d8eb5afbc066b7fac9390f5ec75e5e8a7cab7'} | null |
PyPI | GHSA-79fv-9865-4qcv | Heap buffer overflow in `MaxPoolGrad` | ### Impact
The implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow:
```python
import tensorflow as tf
orig_input = tf.constant([0.0], shape=[1, 1, 1, 1], dtype=tf.float32)
orig_output = tf.constant([0.0], shape=[1, 1, 1, 1], dtype=tf.float32)
grad = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
ksize = [1, 1, 1, 1]
strides = [1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.MaxPoolGrad(
orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,
strides=strides, padding=padding, explicit_paddings=[])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid:
```cc
for (int index = out_start; index < out_end; ++index) {
int input_backprop_index = out_arg_max_flat(index);
FastBoundsCheck(input_backprop_index - in_start, in_end - in_start);
input_backprop_flat(input_backprop_index) += out_backprop_flat(index);
}
```
Whereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access.
### Patches
We have patched the issue in GitHub commit [a74768f8e4efbda4def9f16ee7e13cf3922ac5f7](https://github.com/tensorflow/tensorflow/commit/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7).
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-2021-29579'} | 2022-03-03T05:13:47.961411Z | 2021-05-21T14:26:23Z | LOW | null | {'CWE-787', 'CWE-119'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29579', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-79fv-9865-4qcv', 'https://github.com/tensorflow/tensorflow/commit/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7'} | null |
PyPI | PYSEC-2021-62 | null | python-cryptography 3.2 is vulnerable to Bleichenbacher timing attacks in the RSA decryption API, via timed processing of valid PKCS#1 v1.5 ciphertext. | {'CVE-2020-25659', 'GHSA-hggm-jpg3-v476'} | 2021-01-19T21:48:00Z | 2021-01-11T16:15:00Z | null | null | null | {'https://github.com/pyca/cryptography/pull/5507/commits/ce1bef6f1ee06ac497ca0c837fbd1c7ef6c2472b', 'https://github.com/advisories/GHSA-hggm-jpg3-v476'} | null |
PyPI | PYSEC-2021-787 | 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.SparseFillEmptyRows`. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/sparse_ops.cc#L608-L634) does not validate that the input arguments are not empty tensors. We have patched the issue in GitHub commit 578e634b4f1c1c684d4b4294f9e5281b2133b3ed. 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-2021-37676', 'GHSA-v768-w7m9-2vmm'} | 2021-12-09T06:35:38.998901Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/578e634b4f1c1c684d4b4294f9e5281b2133b3ed', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v768-w7m9-2vmm'} | null |
PyPI | GHSA-2vj5-px25-gjrp | pytorch-lightning is vulnerable to Deserialization of Untrusted Data | pytorch-lightning is vulnerable to Deserialization of Untrusted Data. | {'CVE-2021-4118'} | 2022-03-30T18:33:00.647017Z | 2022-01-06T23:58:59Z | HIGH | null | {'CWE-502'} | {'https://github.com/PyTorchLightning/pytorch-lightning/releases/tag/1.6.0', 'https://nvd.nist.gov/vuln/detail/CVE-2021-4118', 'https://github.com/PyTorchLightning/pytorch-lightning/pull/11099', 'https://github.com/pytorchlightning/pytorch-lightning/commit/62f1e82e032eb16565e676d39e0db0cac7e34ace', 'https://huntr.dev/bounties/31832f0c-e5bb-4552-a12c-542f81f111e6', 'https://github.com/pytorchlightning/pytorch-lightning', 'https://github.com/PyTorchLightning/pytorch-lightning/issues/11045'} | null |
PyPI | PYSEC-2020-154 | null | In Wagtail before versions 2.7.4 and 2.9.3, 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, 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 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. 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. 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, but omitting the |safe filter when outputting the help text. | {'GHSA-2473-9hgq-j7xw', 'CVE-2020-15118'} | 2020-07-28T12:29:00Z | 2020-07-20T18:15:00Z | null | null | null | {'https://github.com/wagtail/wagtail/blob/master/docs/releases/2.9.3.rst', 'https://github.com/wagtail/wagtail/security/advisories/GHSA-2473-9hgq-j7xw', 'https://docs.wagtail.io/en/stable/reference/contrib/forms/index.html#usage', 'https://docs.djangoproject.com/en/3.0/ref/models/fields/#django.db.models.Field.help_text', 'https://github.com/wagtail/wagtail/commit/d9a41e7f24d08c024acc9a3094940199df94db34'} | null |
PyPI | GHSA-hq37-853p-g5cf | Regular Expression Denial of Service in CairoSVG | # Doyensec Vulnerability Advisory
* Regular Expression Denial of Service (REDoS) in cairosvg
* Affected Product: CairoSVG v2.0.0+
* Vendor: https://github.com/Kozea
* Severity: Medium
* Vulnerability Class: Denial of Service
* Author(s): Ben Caller ([Doyensec](https://doyensec.com))
## Summary
When processing SVG files, the python package CairoSVG uses two regular expressions which are vulnerable to Regular Expression Denial of Service (REDoS).
If an attacker provides a malicious SVG, it can make cairosvg get stuck processing the file for a very long time.
## Technical description
The vulnerable regular expressions are
https://github.com/Kozea/CairoSVG/blob/9c4a982b9a021280ad90e89707eacc1d114e4ac4/cairosvg/colors.py#L190-L191
The section between 'rgb(' and the final ')' contains multiple overlapping groups.
Since all three infinitely repeating groups accept spaces, a long string of spaces causes catastrophic backtracking when it is not followed by a closing parenthesis.
The complexity is cubic, so doubling the length of the malicious string of spaces makes processing take 8 times as long.
## Reproduction steps
Create a malicious SVG of the form:
<svg width="1" height="1"><rect fill="rgb( ;"/></svg>
with the following code:
'<svg width="1" height="1"><rect fill="rgb(' + (' ' * 3456) + ';"/></svg>'
Note that there is no closing parenthesis before the semi-colon.
Run cairosvg e.g.:
cairosvg cairo-redos.svg -o x.png
and notice that it hangs at 100% CPU. Increasing the number of spaces increases the processing time with cubic complexity.
## Remediation
Fix the regexes to avoid overlapping parts. Perhaps remove the [ \n\r\t]* groups from the regex, and use .strip() on the returned capture group.
## Disclosure timeline
- 2020-12-30: Vulnerability disclosed via email to CourtBouillon | {'CVE-2021-21236'} | 2022-03-03T05:13:35.572602Z | 2021-01-06T16:57:50Z | MODERATE | null | {'CWE-400'} | {'https://github.com/Kozea/CairoSVG/security/advisories/GHSA-hq37-853p-g5cf', 'https://github.com/Kozea/CairoSVG/commit/cfc9175e590531d90384aa88845052de53d94bf3', 'https://github.com/Kozea/CairoSVG/releases/tag/2.5.1', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21236', 'https://pypi.org/project/CairoSVG/'} | null |
PyPI | GHSA-3ff2-r28g-w7h9 | Heap buffer overflow in `Transpose` | ### Impact
The [shape inference function for `Transpose`](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/ops/array_ops.cc#L121-L185) is vulnerable to a heap buffer overflow:
```python
import tensorflow as tf
@tf.function
def test():
y = tf.raw_ops.Transpose(x=[1,2,3,4],perm=[-10])
return y
test()
```
This occurs whenever `perm` contains negative elements. The shape inference function does not validate that the indices in `perm` are all valid:
```cc
for (int32_t i = 0; i < rank; ++i) {
int64_t in_idx = data[i];
if (in_idx >= rank) {
return errors::InvalidArgument("perm dim ", in_idx,
" is out of range of input rank ", rank);
}
dims[i] = c->Dim(input, in_idx);
}
```
where `Dim(tensor, index)` accepts either a positive index less than the rank of the tensor or the special value `-1` for unknown dimensions.
### Patches
We have patched the issue in GitHub commit [c79ba87153ee343401dbe9d1954d7f79e521eb14](https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14).
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-2021-41216'} | 2022-03-03T05:12:02.115739Z | 2021-11-10T18:57:19Z | MODERATE | null | {'CWE-120', 'CWE-787'} | {'https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3ff2-r28g-w7h9', 'https://github.com/tensorflow/tensorflow', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41216', 'https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/ops/array_ops.cc#L121-L185'} | null |
PyPI | PYSEC-2021-857 | null | Buffer overflow in the array_from_pyobj function of fortranobject.c in NumPy < 1.19, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values. | {'CVE-2021-41496'} | 2021-12-27T21:27:46.586839Z | 2021-12-17T20:15:00Z | null | null | null | {'https://github.com/numpy/numpy/issues/19000'} | null |
PyPI | PYSEC-2018-26 | null | qutebrowser version introduced in v0.11.0 (1179ee7a937fb31414d77d9970bac21095358449) contains a Cross Site Scripting (XSS) vulnerability in history command, qute://history page that can result in Via injected JavaScript code, a website can steal the user's browsing history. This attack appear to be exploitable via the victim must open a page with a specially crafted <title> attribute, and then open the qute://history site via the :history command. This vulnerability appears to have been fixed in fixed in v1.3.3 (4c9360237f186681b1e3f2a0f30c45161cf405c7, to be released today) and v1.4.0 (5a7869f2feaa346853d2a85413d6527c87ef0d9f, released later this week). | {'CVE-2018-1000559', 'GHSA-m4fw-77v7-924m'} | 2021-06-10T06:51:59.879286Z | 2018-06-26T16:29:00Z | null | null | null | {'https://github.com/qutebrowser/qutebrowser/issues/4011', 'https://github.com/qutebrowser/qutebrowser/commit/4c9360237f186681b1e3f2a0f30c45161cf405c7', 'https://github.com/advisories/GHSA-m4fw-77v7-924m', 'https://github.com/qutebrowser/qutebrowser/commit/5a7869f2feaa346853d2a85413d6527c87ef0d9f'} | null |
PyPI | PYSEC-2021-154 | null | TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-772p-x54p-hjrv', 'CVE-2021-29517'} | 2021-08-27T03:22:24.411852Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv'} | null |
PyPI | PYSEC-2021-504 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully. Since the constructor(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-7cqx-92hp-x6wh', 'CVE-2021-29576'} | 2021-12-09T06:34:55.161027Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7cqx-92hp-x6wh', 'https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09'} | null |
PyPI | PYSEC-2018-112 | null | Ajenti version version 2 contains a Improper Error Handling vulnerability in Login JSON request that can result in The requisition leaks a path of the server. This attack appear to be exploitable via By sending a malformed JSON, the tool responds with a traceback error that leaks a path of the server. | {'CVE-2018-1000083'} | 2022-02-17T09:17:11.100025Z | 2018-03-13T15:29:00Z | null | null | null | {'https://pypi.org/project/ajenti-panel', 'https://medium.com/stolabs/security-issues-on-ajenti-d2b7526eaeee', 'https://nvd.nist.gov/vuln/detail/CVE-2018-1000083'} | null |
PyPI | PYSEC-2021-441 | null | TensorFlow is an end-to-end open source platform for machine learning. Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array(https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-452g-f7fp-9jf7', 'CVE-2021-29513'} | 2021-12-09T06:34:45.368024Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/030af767d357d1b4088c4a25c72cb3906abac489', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-452g-f7fp-9jf7'} | null |
PyPI | PYSEC-2019-112 | null | In Archery before 1.3, inserting an XSS payload into a project name (either by creating a new project or editing an existing one) will result in stored XSS on the vulnerability-scan scheduling page. | {'CVE-2019-20008'} | 2020-01-02T14:27:00Z | 2019-12-26T23:15:00Z | null | null | null | {'https://github.com/archerysec/archerysec/releases/tag/v1.3', 'https://github.com/archerysec/archerysec/issues/338', 'https://github.com/archerysec/archerysec/compare/archerysec-v1.2...v1.3'} | null |
PyPI | PYSEC-2022-72 | null | Tensorflow is an Open Source Machine Learning Framework. In multiple places, TensorFlow uses `tempfile.mktemp` to create temporary files. While this is acceptable in testing, in utilities and libraries it is dangerous as a different process can create the file between the check for the filename in `mktemp` and the actual creation of the file by a subsequent operation (a TOC/TOU type of weakness). In several instances, TensorFlow was supposed to actually create a temporary directory instead of a file. This logic bug is hidden away by the `mktemp` function usage. We have patched the issue in several commits, replacing `mktemp` with the safer `mkstemp`/`mkdtemp` functions, according to the usage pattern. Users are advised to upgrade as soon as possible. | {'GHSA-wc4g-r73w-x8mm', 'CVE-2022-23563'} | 2022-03-09T00:17:32.797622Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wc4g-r73w-x8mm'} | null |
PyPI | PYSEC-2012-7 | null | The django.http.HttpRequest.get_host function in Django 1.3.x before 1.3.4 and 1.4.x before 1.4.2 allows remote attackers to generate and display arbitrary URLs via crafted username and password Host header values. | {'CVE-2012-4520'} | 2021-07-15T02:22:08.562601Z | 2012-11-18T23:55:00Z | null | null | null | {'http://www.osvdb.org/86493', 'http://securitytracker.com/id?1027708', 'https://github.com/django/django/commit/9305c0e12d43c4df999c3301a1f0c742264a657e', 'https://www.djangoproject.com/weblog/2012/oct/17/security/', 'http://lists.fedoraproject.org/pipermail/package-announce/2012-October/090970.html', 'http://secunia.com/advisories/51314', 'http://lists.fedoraproject.org/pipermail/package-announce/2012-October/090904.html', 'http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=691145', 'https://github.com/django/django/commit/b45c377f8f488955e0c7069cad3f3dd21910b071', 'http://lists.fedoraproject.org/pipermail/package-announce/2012-October/090666.html', 'http://secunia.com/advisories/51033', 'https://github.com/django/django/commit/92d3430f12171f16f566c9050c40feefb830a4a3', 'http://ubuntu.com/usn/usn-1757-1', 'http://www.openwall.com/lists/oss-security/2012/10/30/4', 'https://bugzilla.redhat.com/show_bug.cgi?id=865164', 'http://www.debian.org/security/2013/dsa-2634', 'http://ubuntu.com/usn/usn-1632-1'} | null |
PyPI | PYSEC-2017-42 | null | The password reset form in Weblate before 2.10.1 provides different error messages depending on whether the email address is associated with an account, which allows remote attackers to enumerate user accounts via a series of requests. | {'CVE-2017-5537'} | 2021-07-05T00:01:28.288013Z | 2017-03-15T15:59:00Z | null | null | null | {'http://www.securityfocus.com/bid/95676', 'http://www.openwall.com/lists/oss-security/2017/01/18/11', 'https://github.com/WeblateOrg/weblate/blob/weblate-2.10.1/docs/changes.rst', 'https://github.com/WeblateOrg/weblate/issues/1317', 'https://github.com/WeblateOrg/weblate/commit/abe0d2a29a1d8e896bfe829c8461bf8b391f1079', 'http://www.openwall.com/lists/oss-security/2017/01/20/1'} | null |
PyPI | GHSA-7xxv-wpxj-mx5v | Out-of-bounds read in typed-ast and cpython may allow an attacker to crash the interpreter process (ast_for_arguments case). | typed_ast 1.3.0 and 1.3.1 has an ast_for_arguments out-of-bounds read. An attacker with the ability to cause a Python interpreter to parse Python source (but not necessarily execute it) may be able to crash the interpreter process. This could be a concern, for example, in a web-based service that parses (but does not execute) Python code. (This issue also affected certain Python 3.8.0-alpha prereleases.) | {'CVE-2019-19275'} | 2022-03-03T05:12:41.201920Z | 2019-12-02T18:03:09Z | HIGH | null | {'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2019-19275', 'https://github.com/python/typed_ast/commit/156afcb26c198e162504a57caddfe0acd9ed7dce', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/LG5H4Q6LFVRX7SFXLBEJMNQFI4T5SCEA/', 'https://bugs.python.org/issue36495', 'https://github.com/python/typed_ast/commit/dc317ac9cff859aa84eeabe03fb5004982545b3b', 'https://github.com/python/cpython/commit/dcfcd146f8e6fc5c2fc16a4c192a0c5f5ca8c53c', 'https://github.com/python/cpython/commit/a4d78362397fc3bced6ea80fbc7b5f4827aec55e'} | null |
PyPI | PYSEC-2021-695 | 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/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29569', 'GHSA-3h8m-483j-7xxm'} | 2021-12-09T06:35:26.658454Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm'} | null |
PyPI | PYSEC-2021-380 | null | Ops CLI version 2.0.4 (and earlier) is affected by a Deserialization of Untrusted Data vulnerability to achieve arbitrary code execution when the checkout_repo function is called on a maliciously crafted file. An attacker can leverage this to execute arbitrary code on the victim machine. | {'CVE-2021-40720'} | 2021-10-24T23:24:39.018050Z | 2021-10-15T15:15:00Z | null | null | null | {'https://helpx.adobe.com/security/products/ops_cli/apsb21-88.html'} | null |
PyPI | PYSEC-2021-35 | null | An issue was discovered in Pillow before 8.1.1. TiffDecode has a heap-based buffer overflow when decoding crafted YCbCr files because of certain interpretation conflicts with LibTIFF in RGBA mode. NOTE: this issue exists because of an incomplete fix for CVE-2020-35654. | {'GHSA-57h3-9rgr-c24m', 'CVE-2021-25289'} | 2021-03-26T14:06:00Z | 2021-03-19T04:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-57h3-9rgr-c24m', 'https://pillow.readthedocs.io/en/stable/releasenotes/8.1.1.html'} | null |
PyPI | GHSA-9w7f-m4j4-j3xw | Gerapy < 0.9.8 may cause remote code execution | ### Impact
project_configure function exist remote code execute in Gerapy < 0.9.8
### Patches
Patched in version 0.9.8, please install with:
```
pip3 install -U gerapy
``` | {'CVE-2021-43857'} | 2022-03-03T05:13:29.100217Z | 2022-01-06T17:36:38Z | HIGH | null | {'CWE-78'} | {'https://github.com/Gerapy/Gerapy/issues/219', 'https://github.com/Gerapy/Gerapy/commit/49bcb19be5e0320e7e1535f34fe00f16a3cf3b28', 'http://packetstormsecurity.com/files/165459/Gerapy-0.9.7-Remote-Code-Execution.html', 'https://github.com/Gerapy/Gerapy/security/advisories/GHSA-9w7f-m4j4-j3xw', 'https://nvd.nist.gov/vuln/detail/CVE-2021-43857', 'https://github.com/Gerapy/Gerapy'} | null |
PyPI | PYSEC-2021-553 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.SparseReshape` can be made to trigger an integral division by 0 exception. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The [reshape functor](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. We have patched the issue in GitHub commit 4923de56ec94fff7770df259ab7f2288a74feb41. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1 as this is the other affected version. | {'GHSA-95xm-g58g-3p88', 'CVE-2021-37640'} | 2021-12-09T06:35:02.412159Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-95xm-g58g-3p88', 'https://github.com/tensorflow/tensorflow/commit/4923de56ec94fff7770df259ab7f2288a74feb41'} | null |
PyPI | PYSEC-2014-52 | null | traverser.py in Plone 2.1 through 4.1, 4.2.x through 4.2.5, and 4.3.x through 4.3.1 allows remote attackers with administrator privileges to cause a denial of service (infinite loop and resource consumption) via unspecified vectors related to "retrieving information for certain resources." | {'CVE-2013-4188'} | 2021-07-25T23:34:45.751265Z | 2014-03-11T19:37:00Z | null | null | null | {'http://plone.org/products/plone-hotfix/releases/20130618', 'http://seclists.org/oss-sec/2013/q3/261', 'http://plone.org/products/plone/security/advisories/20130618-announcement', 'https://bugzilla.redhat.com/show_bug.cgi?id=978449'} | null |
PyPI | PYSEC-2021-103 | null | Wagtail is an open source content management system built on Django. A cross-site scripting vulnerability exists in versions 2.13-2.13.1, versions 2.12-2.12.4, and versions prior to 2.11.8. When the `{% include_block %}` template tag is used to output the value of a plain-text StreamField block (`CharBlock`, `TextBlock` or a similar user-defined block derived from `FieldBlock`), and that block does not specify a template for rendering, the tag output is not properly escaped as HTML. This could allow users to insert arbitrary HTML or scripting. This vulnerability is only exploitable by users with the ability to author StreamField content (i.e. users with 'editor' access to the Wagtail admin). Patched versions have been released as Wagtail 2.11.8 (for the LTS 2.11 branch), Wagtail 2.12.5, and Wagtail 2.13.2 (for the current 2.13 branch). As a workaround, site implementors who are unable to upgrade to a current supported version should audit their use of `{% include_block %}` to ensure it is not used to output `CharBlock` / `TextBlock` values with no associated template. Note that this only applies where `{% include_block %}` is used directly on that block (uses of `include_block` on a block _containing_ a CharBlock / TextBlock, such as a StructBlock, are unaffected). In these cases, the tag can be replaced with Django's `{{ ... }}` syntax - e.g. `{% include_block my_title_block %}` becomes `{{ my_title_block }}`. | {'CVE-2021-32681', 'GHSA-xfrw-hxr5-ghqf'} | 2021-06-22T04:54:57.540693Z | 2021-06-17T17:15:00Z | null | null | null | {'https://github.com/wagtail/wagtail/releases/tag/v2.11.8', 'https://github.com/wagtail/wagtail/releases/tag/v2.12.5', 'https://github.com/wagtail/wagtail/security/advisories/GHSA-xfrw-hxr5-ghqf', 'https://github.com/wagtail/wagtail/releases/tag/v2.13.2'} | null |
PyPI | PYSEC-2018-71 | null | A member of the Plone 2.5-5.1rc1 site could set javascript in the home_page property of his profile, and have this executed when a visitor click the home page link on the author page. | {'CVE-2017-1000482'} | 2021-08-25T04:30:16.873350Z | 2018-01-03T18:29:00Z | null | null | null | {'https://plone.org/security/hotfix/20171128/xss-using-the-home_page-member-property'} | null |
PyPI | GHSA-3w67-q784-6w7c | Division by zero in TFLite's implementation of `GatherNd` | ### Impact
The reference implementation of the `GatherNd` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/reference_ops.h#L966):
```cc
ret.dims_to_count[i] = remain_flat_size / params_shape.Dims(i);
```
An attacker can craft a model such that `params` input would be an empty tensor. In turn, `params_shape.Dims(.)` would be zero, in at least one dimension.
### Patches
We have patched the issue in GitHub commit [8e45822aa0b9f5df4b4c64f221e64dc930a70a9d](https://github.com/tensorflow/tensorflow/commit/8e45822aa0b9f5df4b4c64f221e64dc930a70a9d).
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-29589'} | 2022-03-03T05:12:11.969027Z | 2021-05-21T14:26:51Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/commit/8e45822aa0b9f5df4b4c64f221e64dc930a70a9d', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29589', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3w67-q784-6w7c'} | null |
PyPI | PYSEC-2021-800 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. This is caused by the MLIR optimization of `L2NormalizeReduceAxis` operator. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. We have patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-wf5p-c75w-w3wh', 'CVE-2021-37689'} | 2021-12-09T06:35:40.116575Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/d6b57f461b39fd1aa8c1b870f1b974aac3554955', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-wf5p-c75w-w3wh'} | null |
PyPI | GHSA-cf4q-4cqr-7g7w | SVG with embedded scripts can lead to cross-site scripting attacks in xml2rfc | xml2rfc allows `script` elements in SVG sources.
In HTML output having these script elements can lead to XSS attacks.
Sample XML snippet:
```
<artwork type="svg" src="data:image/svg+xml,%3Csvg viewBox='0 0 10 10' xmlns='http://www.w3.org/2000/svg'%3E%3Cscript%3E window.alert('Test Alert'); %3C/script%3E%3C/svg%3E">
</artwork>
```
### Impact
This vulnerability impacts website that publish HTML drafts and RFCs.
### Patches
This has been fixed in version [3.12.4](https://github.com/ietf-tools/xml2rfc/releases/tag/v3.12.4).
### Workarounds
If SVG source is self-contained within the XML, scraping `script` elements from SVG files.
### References
* https://developer.mozilla.org/en-US/docs/Web/SVG/Element/script
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [xml2rfc](https://github.com/ietf-tools/xml2rfc/)
* Email us at [operational-vulnerability@ietf.org](mailto:operational-vulnerability@ietf.org)
* [Infrastructure and Services Vulnerability Disclosure](https://www.ietf.org/about/administration/policies-procedures/vulnerability-disclosure/)
| null | 2022-04-22T20:30:24.115746Z | 2022-04-22T20:25:53Z | MODERATE | null | {'CWE-79'} | {'https://github.com/ietf-tools/xml2rfc/security/advisories/GHSA-cf4q-4cqr-7g7w', 'https://github.com/ietf-tools/xml2rfc'} | null |
PyPI | PYSEC-2010-11 | null | Race condition in the FTPHandler class in ftpserver.py in pyftpdlib before 0.5.2 allows remote attackers to cause a denial of service (daemon outage) by establishing and then immediately closing a TCP connection, leading to the accept function having an unexpected value of None for the address, or an ECONNABORTED, EAGAIN, or EWOULDBLOCK error, a related issue to CVE-2010-3492. | {'CVE-2010-3494'} | 2021-07-05T00:01:24.878652Z | 2010-10-19T20:00:00Z | null | null | null | {'http://bugs.python.org/issue6706', 'http://www.openwall.com/lists/oss-security/2010/09/11/2', 'http://code.google.com/p/pyftpdlib/issues/detail?id=104', 'http://code.google.com/p/pyftpdlib/source/diff?spec=svn556&r=556&format=side&path=/trunk/pyftpdlib/ftpserver.py', 'http://code.google.com/p/pyftpdlib/source/detail?r=556', 'http://www.openwall.com/lists/oss-security/2010/09/09/6', 'http://www.openwall.com/lists/oss-security/2010/09/24/3', 'http://code.google.com/p/pyftpdlib/issues/detail?id=105', 'http://code.google.com/p/pyftpdlib/source/browse/trunk/HISTORY', 'https://bugs.launchpad.net/zodb/+bug/135108', 'http://www.openwall.com/lists/oss-security/2010/09/22/3'} | null |
PyPI | PYSEC-2020-103 | null | An issue was discovered in SaltStack Salt before 2019.2.4 and 3000 before 3000.2. The salt-master process ClearFuncs class allows access to some methods that improperly sanitize paths. These methods allow arbitrary directory access to authenticated users. | {'CVE-2020-11652'} | 2020-08-20T01:17:00Z | 2020-04-30T17:15:00Z | null | null | null | {'http://packetstormsecurity.com/files/157678/SaltStack-Salt-Master-Minion-Unauthenticated-Remote-Code-Execution.html', 'http://packetstormsecurity.com/files/157560/Saltstack-3000.1-Remote-Code-Execution.html', 'https://lists.debian.org/debian-lts-announce/2020/05/msg00027.html', 'http://support.blackberry.com/kb/articleDetail?articleNumber=000063758', 'http://lists.opensuse.org/opensuse-security-announce/2020-04/msg00047.html', 'https://tools.cisco.com/security/center/content/CiscoSecurityAdvisory/cisco-sa-salt-2vx545AG', 'http://www.vmware.com/security/advisories/VMSA-2020-0009.html', 'https://docs.saltstack.com/en/latest/topics/releases/2019.2.4.html', 'https://usn.ubuntu.com/4459-1/', 'http://lists.opensuse.org/opensuse-security-announce/2020-07/msg00070.html', 'https://www.debian.org/security/2020/dsa-4676', 'https://github.com/saltstack/salt/blob/v3000.2_docs/doc/topics/releases/3000.2.rst'} | null |
PyPI | GHSA-j3f7-7rmc-6wqj | Improper certificate management in AWS IoT Device SDK v2 | The AWS IoT Device SDK v2 for Java, Python, C++ and Node.js appends a user supplied Certificate Authority (CA) to the root CAs instead of overriding it on macOS systems. Additionally, SNI validation is also not enabled when the CA has been “overridden”. TLS handshakes will thus succeed if the peer can be verified either from the user-supplied CA or the system’s default trust-store. Attackers with access to a host’s trust stores or are able to compromise a certificate authority already in the host's trust store (note: the attacker must also be able to spoof DNS in this case) may be able to use this issue to bypass CA pinning. An attacker could then spoof the MQTT broker, and either drop traffic and/or respond with the attacker's data, but they would not be able to forward this data on to the MQTT broker because the attacker would still need the user's private keys to authenticate against the MQTT broker. The 'aws_tls_ctx_options_override_default_trust_store_*' function within the aws-c-io submodule has been updated to address this behavior. This issue affects: Amazon Web Services AWS IoT Device SDK v2 for Java versions prior to 1.5.0 on macOS. Amazon Web Services AWS IoT Device SDK v2 for Python versions prior to 1.7.0 on macOS. Amazon Web Services AWS IoT Device SDK v2 for C++ versions prior to 1.14.0 on macOS. Amazon Web Services AWS IoT Device SDK v2 for Node.js versions prior to 1.6.0 on macOS. Amazon Web Services AWS-C-IO 0.10.7 on macOS. | {'CVE-2021-40831'} | 2022-03-03T05:14:12.538986Z | 2021-11-24T20:35:03Z | MODERATE | null | {'CWE-295'} | {'https://github.com/aws/aws-iot-device-sdk-java-v2', 'https://github.com/aws/aws-iot-device-sdk-java-v2/commit/46375e9b1bfb34109b9ff3b1eff9c770f9daa186', 'https://nvd.nist.gov/vuln/detail/CVE-2021-40831', 'https://github.com/aws/aws-iot-device-sdk-js-v2/commit/22f1989f5bdb0bdd9c912a5a2d255ee6c0854f68', 'https://github.com/aws/aws-iot-device-sdk-python-v2/commit/5aef82573202309063eb540b72cee0e565f85a2d', 'https://github.com/aws/aws-iot-device-sdk-python-v2', 'https://github.com/aws/aws-iot-device-sdk-js-v2', 'https://github.com/aws/aws-iot-device-sdk-cpp-v2', 'https://github.com/awslabs/aws-c-io/'} | null |
PyPI | PYSEC-2020-18 | null | The previous default setting for Airflow's Experimental API was to allow all API requests without authentication, but this poses security risks to users who miss this fact. From Airflow 1.10.11 the default has been changed to deny all requests by default and is documented at https://airflow.apache.org/docs/1.10.11/security.html#api-authentication. Note this change fixes it for new installs but existing users need to change their config to default `[api]auth_backend = airflow.api.auth.backend.deny_all` as mentioned in the Updating Guide: https://github.com/apache/airflow/blob/1.10.11/UPDATING.md#experimental-api-will-deny-all-request-by-default | {'GHSA-hhx9-p69v-cx2j', 'CVE-2020-13927'} | 2020-11-24T17:29:00Z | 2020-11-10T16:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-hhx9-p69v-cx2j', 'https://lists.apache.org/thread.html/r23a81b247aa346ff193670be565b2b8ea4b17ddbc7a35fc099c1aadd%40%3Cdev.airflow.apache.org%3E'} | null |
PyPI | PYSEC-2022-25 | null | UltraJSON (aka ujson) through 5.1.0 has a stack-based buffer overflow in Buffer_AppendIndentUnchecked (called from encode). Exploitation can, for example, use a large amount of indentation. | {'GHSA-fh56-85cw-5pq6', 'CVE-2021-45958'} | 2022-02-07T23:29:33.363244Z | 2022-01-01T00:15:00Z | null | null | null | {'https://github.com/advisories/GHSA-fh56-85cw-5pq6', 'https://github.com/google/oss-fuzz-vulns/blob/main/vulns/ujson/OSV-2021-955.yaml', 'https://github.com/ultrajson/ultrajson/issues/501', 'https://github.com/ultrajson/ultrajson/issues/502#issuecomment-1031747284', 'https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=36009'} | null |
PyPI | PYSEC-2019-145 | null | ansible-playbook -k and ansible cli tools, all versions 2.8.x before 2.8.4, all 2.7.x before 2.7.13 and all 2.6.x before 2.6.19, prompt passwords by expanding them from templates as they could contain special characters. Passwords should be wrapped to prevent templates trigger and exposing them. | {'CVE-2019-10206'} | 2021-07-02T02:41:34.397311Z | 2019-11-22T13:15:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-security-announce/2020-04/msg00021.html', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2019-10206', 'http://lists.opensuse.org/opensuse-security-announce/2020-04/msg00026.html'} | null |
PyPI | PYSEC-2021-416 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseFillEmptyRows` can be made to trigger a heap OOB access. This occurs whenever the size of `indices` does not match the size of `values`. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-rg3m-hqc5-344v', 'CVE-2021-41224'} | 2021-11-13T06:52:45.767410Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v', 'https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b'} | null |
PyPI | GHSA-cmwx-9m2h-x7v4 | Ansible apt_key module does not properly verify key fingerprint | A flaw was found in Ansible before version 2.2.0. The apt_key module does not properly verify key fingerprints, allowing remote adversary to create an OpenPGP key which matches the short key ID and inject this key instead of the correct key. | {'CVE-2016-8614'} | 2022-04-26T18:48:01.214603Z | 2018-10-10T17:23:26Z | HIGH | null | {'CWE-358'} | {'https://nvd.nist.gov/vuln/detail/CVE-2016-8614', 'https://github.com/advisories/GHSA-cmwx-9m2h-x7v4'} | null |
PyPI | GHSA-x345-32rc-8h85 | Denial of service attack via push rule patterns in matrix-synapse | ### Impact
"Push rules" can specify [conditions](https://matrix.org/docs/spec/client_server/r0.6.1#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.
### Patches
The issue is patched by https://github.com/matrix-org/synapse/commit/03318a766cac9f8b053db2214d9c332a977d226c.
### Workarounds
A potential workaround might be to prevent users from making custom push rules, by blocking such requests at a reverse-proxy.
### For more information
If you have any questions or comments about this advisory, email us at security@matrix.org. | {'CVE-2021-29471'} | 2022-04-07T15:17:03.062048Z | 2021-05-13T20:22:51Z | LOW | null | {'CWE-400', 'CWE-331'} | {'https://github.com/matrix-org/synapse/commit/03318a766cac9f8b053db2214d9c332a977d226c', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29471', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-x345-32rc-8h85', 'https://github.com/matrix-org/synapse', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TNNAJOZNMVMXM6AS7RFFKB4QLUJ4IFEY/', 'https://github.com/matrix-org/synapse/releases/tag/v1.33.2'} | null |
PyPI | PYSEC-2017-49 | null | The checkPassword function in python-kerberos does not authenticate the KDC it attempts to communicate with, which allows remote attackers to cause a denial of service (bad response), or have other unspecified impact by performing a man-in-the-middle attack. | {'CVE-2015-3206'} | 2021-07-25T23:34:38.763837Z | 2017-08-25T18:29:00Z | null | null | null | {'https://github.com/apple/ccs-pykerberos/issues/31', 'https://bugzilla.redhat.com/show_bug.cgi?id=1223802', 'https://pypi.python.org/pypi/kerberos', 'http://www.securityfocus.com/bid/74760', 'http://www.openwall.com/lists/oss-security/2015/05/21/3'} | null |
PyPI | GHSA-g8wg-cjwc-xhhp | Heap OOB in nested `tf.map_fn` with `RaggedTensor`s | ### Impact
It is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap:
```python
import tensorflow as tf
x = tf.ragged.constant([[1,2,3], [4,5], [6]])
t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x)
z = tf.ragged.constant([[[1,2,3],[1,2,3],[1,2,3]],[[4,5],[4,5]],[[6]]])
```
The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information.
The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions in the above example.
The same implementation can result in data loss, if input tensor is tweaked:
```python
import tensorflow as tf
x = tf.ragged.constant([[1,2], [3,4,5], [6]])
t = tf.map_fn(lambda r: tf.map_fn(lambda y: r, r), x)
```
Here, the output tensor will only have 2 elements for each inner dimension.
### Patches
We have patched the issue in GitHub commit [4e2565483d0ffcadc719bd44893fb7f609bb5f12](https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### 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 Haris Sahovic. | {'CVE-2021-37679'} | 2022-03-03T05:13:19.482245Z | 2021-08-25T14:41:00Z | HIGH | null | {'CWE-125', 'CWE-681'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g8wg-cjwc-xhhp', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37679', 'https://github.com/tensorflow/tensorflow/commit/4e2565483d0ffcadc719bd44893fb7f609bb5f12'} | null |
PyPI | PYSEC-2021-734 | null | TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-rgvq-pcvf-hx75', 'CVE-2021-29608'} | 2021-12-09T06:35:33.390905Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a', 'https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e', 'https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rgvq-pcvf-hx75'} | null |
PyPI | PYSEC-2021-364 | null | Scrapy-splash is a library which provides Scrapy and JavaScript integration. In affected versions users who use [`HttpAuthMiddleware`](http://doc.scrapy.org/en/latest/topics/downloader-middleware.html#module-scrapy.downloadermiddlewares.httpauth) (i.e. the `http_user` and `http_pass` spider attributes) for Splash authentication will have any non-Splash request expose your credentials to the request target. This includes `robots.txt` requests sent by Scrapy when the `ROBOTSTXT_OBEY` setting is set to `True`. Upgrade to scrapy-splash 0.8.0 and use the new `SPLASH_USER` and `SPLASH_PASS` settings instead to set your Splash authentication credentials safely. If you cannot upgrade, set your Splash request credentials on a per-request basis, [using the `splash_headers` request parameter](https://github.com/scrapy-plugins/scrapy-splash/tree/0.8.x#http-basic-auth), instead of defining them globally using the [`HttpAuthMiddleware`](http://doc.scrapy.org/en/latest/topics/downloader-middleware.html#module-scrapy.downloadermiddlewares.httpauth). Alternatively, make sure all your requests go through Splash. That includes disabling the [robots.txt middleware](https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#topics-dlmw-robots). | {'CVE-2021-41124', 'GHSA-823f-cwm9-4g74'} | 2021-10-11T01:16:42.816754Z | 2021-10-05T21:15:00Z | null | null | null | {'https://github.com/scrapy-plugins/scrapy-splash/security/advisories/GHSA-823f-cwm9-4g74', 'https://github.com/scrapy-plugins/scrapy-splash/commit/2b253e57fe64ec575079c8cdc99fe2013502ea31'} | null |
PyPI | PYSEC-2021-671 | 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 converting sparse tensors to CSR Sparse matrices. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap. If the value at `indices(i, 0)` is such that `indices(i, 0) + 1` is outside the bounds of `csr_row_ptr`, this results in writing outside of bounds of heap allocated data. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29545', 'GHSA-hmg3-c7xj-6qwm'} | 2021-12-09T06:35:22.627279Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1e922ccdf6bf46a3a52641f99fd47d54c1decd13', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hmg3-c7xj-6qwm'} | null |
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