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stringdate 2019-03-26 14:13:00
2022-05-10 08:46:52
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stringdate 2012-06-17 03:41:00
2022-05-10 08:46:50
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PyPI | PYSEC-2021-198 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from `tf.raw_ops.LoadAndRemapMatrix`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) assumes that the `ckpt_path` is always a valid scalar. However, an attacker can send any other tensor as the first argument of `LoadAndRemapMatrix`. This would cause the rank `CHECK` in `scalar<T>()()` to trigger and terminate the process. 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-2021-29561', 'GHSA-gvm4-h8j3-rjrq'} | 2021-08-27T03:22:32.310582Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gvm4-h8j3-rjrq', 'https://github.com/tensorflow/tensorflow/commit/77dd114513d7796e1e2b8aece214a380af26fbf4'} | null | {'https://github.com/tensorflow/tensorflow/commit/77dd114513d7796e1e2b8aece214a380af26fbf4'} | {'https://github.com/tensorflow/tensorflow/commit/77dd114513d7796e1e2b8aece214a380af26fbf4'} |
PyPI | GHSA-hj2j-77xm-mc5v | High severity vulnerability that affects Jinja2 | In Pallets Jinja before 2.8.1, str.format allows a sandbox escape. | {'CVE-2016-10745'} | 2022-03-03T05:14:16.206594Z | 2019-04-10T14:30:13Z | HIGH | null | {'CWE-134'} | {'http://lists.opensuse.org/opensuse-security-announce/2019-05/msg00030.html', 'https://nvd.nist.gov/vuln/detail/CVE-2016-10745', 'http://lists.opensuse.org/opensuse-security-announce/2019-06/msg00064.html', 'https://access.redhat.com/errata/RHSA-2019:4062', 'https://access.redhat.com/errata/RHSA-2019:3964', 'https://access.redhat.com/errata/RHSA-2019:1022', 'https://github.com/pallets/jinja', 'https://palletsprojects.com/blog/jinja-281-released/', 'https://github.com/advisories/GHSA-hj2j-77xm-mc5v', 'https://access.redhat.com/errata/RHSA-2019:1237', 'https://usn.ubuntu.com/4011-1/', 'https://usn.ubuntu.com/4011-2/', 'https://github.com/pallets/jinja/commit/9b53045c34e61013dc8f09b7e52a555fa16bed16', 'https://access.redhat.com/errata/RHSA-2019:1260'} | null | {'https://github.com/pallets/jinja/commit/9b53045c34e61013dc8f09b7e52a555fa16bed16'} | {'https://github.com/pallets/jinja/commit/9b53045c34e61013dc8f09b7e52a555fa16bed16'} |
PyPI | GHSA-hf6p-4rv2-9qrp | Path Traversal in bikshed | This affects the package bikeshed before 3.0.0. This can occur when an untrusted source file containing include, include-code or include-raw block is processed. The contents of arbitrary files could be disclosed in the HTML output. | {'CVE-2021-23423'} | 2022-03-03T05:13:55.271297Z | 2021-08-30T16:25:42Z | MODERATE | null | {'CWE-22'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-23423', 'https://github.com/tabatkins/bikeshed', 'https://snyk.io/vuln/SNYK-PYTHON-BIKESHED-1537647', 'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd'} | null | {'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd'} | {'https://github.com/tabatkins/bikeshed/commit/b2f668fca204260b1cad28d5078e93471cb6b2dd'} |
PyPI | PYSEC-2017-36 | null | Directory traversal vulnerability in minion id validation in SaltStack Salt before 2016.3.8, 2016.11.x before 2016.11.8, and 2017.7.x before 2017.7.2 allows remote minions with incorrect credentials to authenticate to a master via a crafted minion ID. NOTE: this vulnerability exists because of an incomplete fix for CVE-2017-12791. | {'CVE-2017-14695'} | 2021-07-05T00:01:26.552235Z | 2017-10-24T17:29:00Z | null | null | null | {'http://lists.opensuse.org/opensuse-updates/2017-10/msg00075.html', 'https://docs.saltstack.com/en/latest/topics/releases/2017.7.2.html', 'https://docs.saltstack.com/en/latest/topics/releases/2016.3.8.html', 'https://bugzilla.redhat.com/show_bug.cgi?id=1500748', 'http://lists.opensuse.org/opensuse-updates/2017-10/msg00073.html', 'https://github.com/saltstack/salt/commit/80d90307b07b3703428ecbb7c8bb468e28a9ae6d', 'https://docs.saltstack.com/en/latest/topics/releases/2016.11.8.html'} | null | {'https://github.com/saltstack/salt/commit/80d90307b07b3703428ecbb7c8bb468e28a9ae6d'} | {'https://github.com/saltstack/salt/commit/80d90307b07b3703428ecbb7c8bb468e28a9ae6d'} |
PyPI | GHSA-cvgx-3v3q-m36c | Heap OOB in shape inference for `QuantizeV2` | ### Impact
The [shape inference code for `QuantizeV2`](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/framework/common_shape_fns.cc#L2509-L2530) can trigger a read outside of bounds of heap allocated array:
```python
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeV2(
input=[1.0,1.0],
min_range=[1.0,10.0],
max_range=[1.0,10.0],
T=tf.qint32,
mode='MIN_COMBINED',
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-100,
ensure_minimum_range=10)
return data
test()
```
This occurs whenever `axis` is a negative value less than `-1`. In this case, we are accessing data before the start of a heap buffer:
```cc
int axis = -1;
Status s = c->GetAttr("axis", &axis);
if (!s.ok() && s.code() != error::NOT_FOUND) {
return s;
}
...
if (axis != -1) {
...
TF_RETURN_IF_ERROR(
c->Merge(c->Dim(minmax, 0), c->Dim(input, axis), &depth));
}
```
The code allows `axis` to be an optional argument (`s` would contain an `error::NOT_FOUND` error code). Otherwise, it assumes that `axis` is a valid index into the dimensions of the `input` tensor. If `axis` is less than `-1` then this results in a heap OOB read.
### Patches
We have patched the issue in GitHub commit [a0d64445116c43cf46a5666bd4eee28e7a82f244](https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244).
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.
### 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-2021-41211'} | 2021-11-08T22:32:45Z | 2021-11-10T19:01:03Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvgx-3v3q-m36c', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41211'} | null | {'https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244'} | {'https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244'} |
PyPI | PYSEC-2018-52 | null | A flaw was found in python-cryptography versions between >=1.9.0 and <2.3. The finalize_with_tag API did not enforce a minimum tag length. If a user did not validate the input length prior to passing it to finalize_with_tag an attacker could craft an invalid payload with a shortened tag (e.g. 1 byte) such that they would have a 1 in 256 chance of passing the MAC check. GCM tag forgeries can cause key leakage. | {'CVE-2018-10903', 'GHSA-fcf9-3qw3-gxmj'} | 2021-07-15T02:22:07.445715Z | 2018-07-30T16:29:00Z | null | null | null | {'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2018-10903', 'https://access.redhat.com/errata/RHSA-2018:3600', 'https://usn.ubuntu.com/3720-1/', 'https://github.com/advisories/GHSA-fcf9-3qw3-gxmj', 'https://github.com/pyca/cryptography/pull/4342/commits/688e0f673bfbf43fa898994326c6877f00ab19ef'} | null | {'https://github.com/pyca/cryptography/pull/4342/commits/688e0f673bfbf43fa898994326c6877f00ab19ef'} | {'https://github.com/pyca/cryptography/pull/4342/commits/688e0f673bfbf43fa898994326c6877f00ab19ef'} |
PyPI | GHSA-cq27-v7xp-c356 | Moderate severity vulnerability that affects pycrypto | Heap-based buffer overflow in the ALGnew function in block_templace.c in Python Cryptography Toolkit (aka pycrypto) allows remote attackers to execute arbitrary code as demonstrated by a crafted iv parameter to cryptmsg.py. | {'CVE-2013-7459'} | 2022-03-03T05:12:41.100141Z | 2018-12-14T18:51:38Z | CRITICAL | null | {'CWE-119'} | {'https://github.com/dlitz/pycrypto', 'https://github.com/dlitz/pycrypto/commit/8dbe0dc3eea5c689d4f76b37b93fe216cf1f00d4', 'https://security.gentoo.org/glsa/201702-14', 'https://pony7.fr/ctf:public:32c3:cryptmsg', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/RJ37R2YLX56YZABFNAOWV4VTHTGYREAE/', 'https://github.com/advisories/GHSA-cq27-v7xp-c356', 'https://nvd.nist.gov/vuln/detail/CVE-2013-7459', 'https://bugzilla.redhat.com/show_bug.cgi?id=1409754', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/C6BWNADPLKDBBQBUT3P75W7HAJCE7M3B/', 'https://github.com/dlitz/pycrypto/issues/176', 'http://www.securityfocus.com/bid/95122', 'http://www.openwall.com/lists/oss-security/2016/12/27/8'} | null | {'https://github.com/dlitz/pycrypto/commit/8dbe0dc3eea5c689d4f76b37b93fe216cf1f00d4'} | {'https://github.com/dlitz/pycrypto/commit/8dbe0dc3eea5c689d4f76b37b93fe216cf1f00d4'} |
PyPI | PYSEC-2021-823 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference function for `Transpose` is vulnerable to a heap buffer overflow. This occurs whenever `perm` contains negative elements. The shape inference function does not validate that the indices in `perm` are all valid. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41216', 'GHSA-3ff2-r28g-w7h9'} | 2021-12-09T06:35:43.595346Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3ff2-r28g-w7h9'} | null | {'https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14'} | {'https://github.com/tensorflow/tensorflow/commit/c79ba87153ee343401dbe9d1954d7f79e521eb14'} |
PyPI | PYSEC-2020-120 | null | In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1. | {'CVE-2020-15197', 'GHSA-qc53-44cj-vfvx'} | 2021-09-01T08:19:33.096342Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qc53-44cj-vfvx', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} | {'https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02'} |
PyPI | PYSEC-2021-570 | 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'} | 2021-12-09T06:35:03.842863Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f2a673bd34f0d64b8e40a551ac78989d16daad09', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5xwc-mrhx-5g3m'} | null | {'https://github.com/tensorflow/tensorflow/commit/f2a673bd34f0d64b8e40a551ac78989d16daad09'} | {'https://github.com/tensorflow/tensorflow/commit/f2a673bd34f0d64b8e40a551ac78989d16daad09'} |
PyPI | PYSEC-2021-120 | null | Webrecorder pywb before 2.6.0 allows XSS because it does not ensure that Jinja2 templates are autoescaped. | {'GHSA-947x-pv47-pp3q', 'CVE-2021-39286'} | 2021-08-18T20:29:26.806388Z | 2021-08-18T18:15:00Z | null | null | null | {'https://github.com/webrecorder/pywb/compare/v-2.5.0...v-2.6.0', 'https://github.com/webrecorder/pywb/commit/f7bd84cdacdd665ff73ae8d09a202f60be2ebae9', 'https://github.com/advisories/GHSA-947x-pv47-pp3q'} | null | {'https://github.com/webrecorder/pywb/commit/f7bd84cdacdd665ff73ae8d09a202f60be2ebae9'} | {'https://github.com/webrecorder/pywb/commit/f7bd84cdacdd665ff73ae8d09a202f60be2ebae9'} |
PyPI | GHSA-8wr4-2wm6-w3pr | B2 Command Line Tool TOCTOU application key disclosure | ### Impact
Linux and Mac releases of the B2 command-line tool version 3.2.0 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.
The command line tool 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 `b2 authorize-account` is first run. This happens regardless of whether a valid key is provided or not. 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 is readable by a local attacker and the user did not yet run `b2 authorize-account` 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.
### Remediation
Users that have not yet run `b2 authorize-account` should upgrade to B2 Command-Line Tool v3.2.1 before running it.
Users that have run `b2 authorize-account` are safe if at the time of the file creation no other local users had read access to the local configuration file.
Users that have run `b2 authorize-account` where the designated path could be opened by another local user should upgrade to B2 Command-Line Tool v3.2.1 and remove the database and regenerate all application keys. Note that `b2 clear-account` does not remove the database file and it should not be used to ensure that all open handles to the file are invalidated.
### Workarounds
If B2 Command-Line Tool cannot be upgraded to v3.2.1 due to a dependency conflict, a binary release can be used instead. Alternatively a new version could be installed within a virtualenv, or the permissions can be changed to prevent local users from opening the database file.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [B2 Command-Line Tool](https://github.com/Backblaze/B2_Command_Line_Tool) mentioning the CVE id in the issue title
* Email us at [security@backblaze.com](mailto:security@backblaze.com)
| {'CVE-2022-23653'} | 2022-03-08T18:31:48.597637Z | 2022-02-24T13:11:51Z | MODERATE | null | {'CWE-367'} | {'https://nvd.nist.gov/vuln/detail/CVE-2022-23653', 'https://github.com/Backblaze/B2_Command_Line_Tool/security/advisories/GHSA-8wr4-2wm6-w3pr', 'https://github.com/Backblaze/B2_Command_Line_Tool/', 'https://github.com/Backblaze/B2_Command_Line_Tool/commit/c74029f9f75065e8f7e3c3ec8e0a23fb8204feeb'} | null | {'https://github.com/Backblaze/B2_Command_Line_Tool/commit/c74029f9f75065e8f7e3c3ec8e0a23fb8204feeb'} | {'https://github.com/Backblaze/B2_Command_Line_Tool/commit/c74029f9f75065e8f7e3c3ec8e0a23fb8204feeb'} |
PyPI | GHSA-m3rf-7m4w-r66q | Improper Authentication in Flask-AppBuilder | ### Impact
Improper authentication on the REST API. Allows for a malicious actor with a carefully crafted request to successfully authenticate and gain access to existing protected REST API endpoints. Only affects non database authentication types, and new REST API endpoints.
### Patches
Upgrade to Flask-AppBuilder 3.3.4
### For more information
If you have any questions or comments about this advisory:
* Open an issue in https://github.com/dpgaspar/Flask-AppBuilder
| {'CVE-2021-41265'} | 2022-03-03T05:13:53.493713Z | 2021-12-09T19:09:07Z | HIGH | null | {'CWE-287'} | {'https://github.com/dpgaspar/Flask-AppBuilder/releases/tag/v3.3.4', 'https://github.com/dpgaspar/Flask-AppBuilder', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41265', 'https://github.com/dpgaspar/Flask-AppBuilder/security/advisories/GHSA-m3rf-7m4w-r66q', 'https://github.com/dpgaspar/Flask-AppBuilder/commit/eba517aab121afa3f3f2edb011ec6bc4efd61fbc'} | null | {'https://github.com/dpgaspar/Flask-AppBuilder/commit/eba517aab121afa3f3f2edb011ec6bc4efd61fbc'} | {'https://github.com/dpgaspar/Flask-AppBuilder/commit/eba517aab121afa3f3f2edb011ec6bc4efd61fbc'} |
PyPI | PYSEC-2022-129 | null | Tensorflow is an Open Source Machine Learning Framework. An attacker can trigger denial of service via assertion failure by altering a `SavedModel` on disk such that `AttrDef`s of some operation are duplicated. 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-2022-23565', 'GHSA-4v5p-v5h9-6xjx'} | 2022-03-09T00:18:26.310749Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4v5p-v5h9-6xjx'} | null | {'https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0'} | {'https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0'} |
PyPI | GHSA-v7m9-9497-p9gr | Possible pod name collisions in jupyterhub-kubespawner | ### Impact
_What kind of vulnerability is it? Who is impacted?_
JupyterHub deployments using:
- KubeSpawner <= 0.11.1 (e.g. zero-to-jupyterhub 0.9.0) and
- enabled named_servers (not default), and
- an Authenticator that allows:
- usernames with hyphens or other characters that require escape (e.g. `user-hyphen` or `user@email`), and
- usernames which may match other usernames up to but not including the escaped character (e.g. `user` in the above cases)
In this circumstance, certain usernames will be able to craft particular server names which will grant them access to the default server of other users who have matching usernames.
### Patches
_Has the problem been patched? What versions should users upgrade to?_
Patch will be released in kubespawner 0.12 and zero-to-jupyterhub 0.9.1
### Workarounds
_Is there a way for users to fix or remediate the vulnerability without upgrading?_
#### KubeSpawner
Specify configuration:
for KubeSpawner
```python
from traitlets import default
from kubespawner import KubeSpawner
class PatchedKubeSpawner(KubeSpawner):
@default("pod_name_template")
def _default_pod_name_template(self):
if self.name:
return "jupyter-{username}-{servername}"
else:
return "jupyter-{username}"
@default("pvc_name_template")
def _default_pvc_name_template(self):
if self.name:
return "claim-{username}-{servername}"
else:
return "claim-{username}"
c.JupyterHub.spawner_class = PatchedKubeSpawner
```
**Note for KubeSpawner:** this configuration will behave differently before and after the upgrade, so will need to be removed when upgrading. Only apply this configuration while still using KubeSpawner ≤ 0.11.1 and remove it after upgrade to ensure consistent pod and pvc naming.
Changing the name template means pvcs for named servers will have different names. This will result in orphaned PVCs for named servers across Hub upgrade! This may appear as data loss for users, depending on configuration, but the orphaned PVCs will still be around and data can be migrated manually (or new PVCs created manually to reference existing PVs) before deleting the old PVCs and/or PVs.
### References
_Are there any links users can visit to find out more?_
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [kubespawner](https://github.com/jupyterhub/kubespawner)
* Email us at [security@ipython.org](mailto:security@ipython.org)
Credit: Jining Huang | {'CVE-2020-15110'} | 2022-03-03T05:14:03.632392Z | 2020-07-22T23:07:16Z | MODERATE | null | {'CWE-863'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15110', 'https://github.com/jupyterhub/kubespawner/security/advisories/GHSA-v7m9-9497-p9gr', 'https://github.com/jupyterhub/kubespawner', 'https://github.com/jupyterhub/kubespawner/commit/3dfe870a7f5e98e2e398b01996ca6b8eff4bb1d0'} | null | {'https://github.com/jupyterhub/kubespawner/commit/3dfe870a7f5e98e2e398b01996ca6b8eff4bb1d0'} | {'https://github.com/jupyterhub/kubespawner/commit/3dfe870a7f5e98e2e398b01996ca6b8eff4bb1d0'} |
PyPI | GHSA-8843-m7mw-mxqm | Buffer overflow in Pillow | In Pillow before 6.2.3 and 7.x before 7.0.1, there are two Buffer Overflows in libImaging/TiffDecode.c. | {'CVE-2020-10379'} | 2022-03-03T05:14:16.304336Z | 2020-07-27T21:52:41Z | HIGH | null | {'CWE-120'} | {'https://github.com/python-pillow/Pillow/commits/master/src/libImaging', 'https://github.com/python-pillow/Pillow/commit/46f4a349b88915787fea3fb91348bb1665831bbb#diff-9478f2787e3ae9668a15123b165c23ac', 'https://pillow.readthedocs.io/en/stable/releasenotes/6.2.3.html', 'https://nvd.nist.gov/vuln/detail/CVE-2020-10379', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/HOKHNWV2VS5GESY7IBD237E7C6T3I427/', 'https://snyk.io/vuln/SNYK-PYTHON-PILLOW-574577', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/BEBCPE4F2VHTIT6EZA2YZQZLPVDEBJGD/', 'https://usn.ubuntu.com/4430-2/', 'https://pillow.readthedocs.io/en/stable/releasenotes/7.1.0.html', 'https://github.com/python-pillow/Pillow/pull/4538', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/46f4a349b88915787fea3fb91348bb1665831bbb#diff-9478f2787e3ae9668a15123b165c23ac'} | {'https://github.com/python-pillow/Pillow/commit/46f4a349b88915787fea3fb91348bb1665831bbb#diff-9478f2787e3ae9668a15123b165c23ac'} |
PyPI | PYSEC-2021-209 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.SdcaOptimizer` triggers undefined behavior due to dereferencing a null pointer. The implementation(https://github.com/tensorflow/tensorflow/blob/60a45c8b6192a4699f2e2709a2645a751d435cc3/tensorflow/core/kernels/sdca_internal.cc) does not validate that the user supplied arguments satisfy all constraints expected by the op(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SdcaOptimizer). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-5gqf-456p-4836', 'CVE-2021-29572'} | 2021-08-27T03:22:34.191182Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5gqf-456p-4836', 'https://github.com/tensorflow/tensorflow/commit/f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb'} | null | {'https://github.com/tensorflow/tensorflow/commit/f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb'} | {'https://github.com/tensorflow/tensorflow/commit/f7cc8755ac6683131fdfa7a8a121f9d7a9dec6fb'} |
PyPI | PYSEC-2021-659 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to validate user controlled inputs. Whereas `OP_REQUIRES` allows returning an error condition back to the user, the `CHECK_*` macros result in a crash if the condition is false, similar to `assert`. In this case, `height` is 0 from the `images` input. This results in `max_box_row_clamp` being negative and the assertion being falsified, followed by aborting program execution. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-393f-2jr3-cp69', 'CVE-2021-29533'} | 2021-12-09T06:35:20.437161Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-393f-2jr3-cp69', 'https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87'} | null | {'https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87'} | {'https://github.com/tensorflow/tensorflow/commit/b432a38fe0e1b4b904a6c222cbce794c39703e87'} |
PyPI | PYSEC-2021-16 | null | httplib2 is a comprehensive HTTP client library for Python. In httplib2 before version 0.19.0, a malicious server which responds with long series of "\xa0" characters in the "www-authenticate" header may cause Denial of Service (CPU burn while parsing header) of the httplib2 client accessing said server. This is fixed in version 0.19.0 which contains a new implementation of auth headers parsing using the pyparsing library. | {'CVE-2021-21240', 'GHSA-93xj-8mrv-444m'} | 2021-02-12T14:56:00Z | 2021-02-08T20:15:00Z | null | null | null | {'https://github.com/httplib2/httplib2/security/advisories/GHSA-93xj-8mrv-444m', 'https://pypi.org/project/httplib2', 'https://github.com/httplib2/httplib2/pull/182', 'https://github.com/httplib2/httplib2/commit/bd9ee252c8f099608019709e22c0d705e98d26bc'} | null | {'https://github.com/httplib2/httplib2/commit/bd9ee252c8f099608019709e22c0d705e98d26bc'} | {'https://github.com/httplib2/httplib2/commit/bd9ee252c8f099608019709e22c0d705e98d26bc'} |
PyPI | PYSEC-2021-566 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a crash via a floating point exception in `tf.raw_ops.ResourceGather`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L725-L731) computes the value of a value, `batch_size`, and then divides by it without checking that this value is not 0. We have patched the issue in GitHub commit ac117ee8a8ea57b73d34665cdf00ef3303bc0b11. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-qjj8-32p7-h289', 'CVE-2021-37653'} | 2021-12-09T06:35:03.512666Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qjj8-32p7-h289', 'https://github.com/tensorflow/tensorflow/commit/ac117ee8a8ea57b73d34665cdf00ef3303bc0b11'} | null | {'https://github.com/tensorflow/tensorflow/commit/ac117ee8a8ea57b73d34665cdf00ef3303bc0b11'} | {'https://github.com/tensorflow/tensorflow/commit/ac117ee8a8ea57b73d34665cdf00ef3303bc0b11'} |
PyPI | GHSA-pgww-xf46-h92r | XSS in lxml | A XSS vulnerability was discovered in python-lxml's clean module. The module's parser didn't properly imitate browsers, which caused different behaviors between the sanitizer and the user's page. A remote attacker could exploit this flaw to run arbitrary HTML/JS code. | {'CVE-2020-27783'} | 2022-03-03T05:14:14.706013Z | 2021-01-07T21:54:01Z | MODERATE | null | {'CWE-79'} | {'https://www.debian.org/security/2020/dsa-4810', 'https://advisory.checkmarx.net/advisory/CX-2020-4286', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TMHVKRUT22LVWNL3TB7HPSDHJT74Q3JK/', 'https://lists.debian.org/debian-lts-announce/2020/12/msg00028.html', 'https://nvd.nist.gov/vuln/detail/CVE-2020-27783', 'https://github.com/lxml/lxml/commit/a105ab8dc262ec6735977c25c13f0bdfcdec72a7', 'https://security.netapp.com/advisory/ntap-20210521-0003/', 'https://bugzilla.redhat.com/show_bug.cgi?id=1901633', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/JKG67GPGTV23KADT4D4GK4RMHSO4CIQL/', 'https://pypi.org/project/lxml/', 'https://snyk.io/vuln/SNYK-PYTHON-LXML-1047473'} | null | {'https://github.com/lxml/lxml/commit/a105ab8dc262ec6735977c25c13f0bdfcdec72a7'} | {'https://github.com/lxml/lxml/commit/a105ab8dc262ec6735977c25c13f0bdfcdec72a7'} |
PyPI | PYSEC-2020-136 | null | In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code. | {'CVE-2020-15213', 'GHSA-hjmq-236j-8m87'} | 2020-10-01T23:15:00Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87', 'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} | {'https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a'} |
PyPI | PYSEC-2021-835 | null | TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's `saved_model_cli` tool is vulnerable to a code injection as it calls `eval` on user supplied strings. This can be used by attackers to run arbitrary code on the plaform where the CLI tool runs. However, given that the tool is always run manually, the impact of this is not severe. We have patched this by adding a `safe` flag which defaults to `True` and an explicit warning for users. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-3rcw-9p9x-582v', 'CVE-2021-41228'} | 2021-12-09T06:35:45.436106Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3rcw-9p9x-582v'} | null | {'https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7'} | {'https://github.com/tensorflow/tensorflow/commit/8b202f08d52e8206af2bdb2112a62fafbc546ec7'} |
PyPI | GHSA-xrr4-74mc-rpjc | Pyro mishandles pid files in temporary directory locations and opening the pid file as root | pyro before 3.15 unsafely handles pid files in temporary directory locations and opening the pid file as root. An attacker can use this flaw to overwrite arbitrary files via symlinks. | {'CVE-2011-2765'} | 2022-04-26T18:32:54.526435Z | 2018-08-21T17:01:29Z | HIGH | null | {'CWE-59'} | {'https://github.com/irmen/Pyro3', 'https://bugs.debian.org/631912', 'https://github.com/advisories/GHSA-xrr4-74mc-rpjc', 'https://pythonhosted.org/Pyro/12-changes.html', 'https://github.com/irmen/Pyro3/commit/554e095a62c4412c91f981e72fd34a936ac2bf1e', 'https://nvd.nist.gov/vuln/detail/CVE-2011-2765'} | null | {'https://github.com/irmen/Pyro3/commit/554e095a62c4412c91f981e72fd34a936ac2bf1e'} | {'https://github.com/irmen/Pyro3/commit/554e095a62c4412c91f981e72fd34a936ac2bf1e'} |
PyPI | PYSEC-2021-589 | 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'} | 2021-12-09T06:35:05.480275Z | 2021-08-12T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v768-w7m9-2vmm', 'https://github.com/tensorflow/tensorflow/commit/578e634b4f1c1c684d4b4294f9e5281b2133b3ed'} | null | {'https://github.com/tensorflow/tensorflow/commit/578e634b4f1c1c684d4b4294f9e5281b2133b3ed'} | {'https://github.com/tensorflow/tensorflow/commit/578e634b4f1c1c684d4b4294f9e5281b2133b3ed'} |
PyPI | GHSA-v936-j8gp-9q3p | Open redirects on some federation and push requests | ### Impact
Requests to user provided domains were not restricted to external IP addresses when calculating the key validity for third-party invite events and sending push notifications. This could cause Synapse to make requests to internal infrastructure. The type of request was not controlled by the user, although limited modification of request bodies was possible.
For the most thorough protection server administrators should remove the deprecated `federation_ip_range_blacklist` from their settings after upgrading to Synapse v1.25.0 which will result in Synapse using the improved default IP address restrictions. See the new `ip_range_blacklist` and `ip_range_whitelist` settings if more specific control is necessary.
### Patches
Issue is resolved by #8821. Further improvements to protect homeservers by default were made in #8870 and #8954.
### Workarounds
Requests to internal IP addresses could be blocked at the system or network level. | {'CVE-2021-21273'} | 2022-03-03T05:14:08.012098Z | 2021-02-26T17:28:34Z | LOW | null | {'CWE-601'} | {'https://github.com/matrix-org/synapse/releases/tag/v1.25.0', 'https://github.com/matrix-org/synapse', 'https://github.com/matrix-org/synapse/security/advisories/GHSA-v936-j8gp-9q3p', 'https://github.com/matrix-org/synapse/commit/30fba6210834a4ecd91badf0c8f3eb278b72e746', 'https://github.com/matrix-org/synapse/pull/8821', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TNNAJOZNMVMXM6AS7RFFKB4QLUJ4IFEY/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-21273'} | null | {'https://github.com/matrix-org/synapse/commit/30fba6210834a4ecd91badf0c8f3eb278b72e746'} | {'https://github.com/matrix-org/synapse/commit/30fba6210834a4ecd91badf0c8f3eb278b72e746'} |
PyPI | GHSA-9jhm-8m8c-c3f4 | SSRF in Sydent due to missing validation of hostnames | ### Impact
Sydent can be induced to send HTTP GET requests to internal systems, due to lack of parameter validation or IP address blacklisting.
It is not possible to exfiltrate data or control request headers, but it might be possible to use the attack to perform an internal port enumeration.
### Patches
Fixed in 9e57334, 8936925, 3d531ed, 0f00412
### Workarounds
A potential workaround would be to use a firewall to ensure that Sydent cannot reach internal HTTP resources.
### For more information
If you have any questions or comments about this advisory, email us at security@matrix.org. | {'CVE-2021-29431'} | 2022-03-03T05:14:17.907013Z | 2021-04-19T14:54:15Z | HIGH | null | {'CWE-20', 'CWE-918'} | {'https://github.com/matrix-org/sydent/security/advisories/GHSA-9jhm-8m8c-c3f4', 'https://github.com/matrix-org/sydent/commit/9e573348d81df8191bbe8c266c01999c9d57cd5f', 'https://github.com/matrix-org/sydent/releases/tag/v2.3.0', 'https://github.com/matrix-org/sydent/commit/8936925f561b0c352c2fa922d5097d7245aad00a', 'https://github.com/matrix-org/sydent/commit/0f00412017f25619bc36c264b29ea96808bf310a', 'https://pypi.org/project/matrix-sydent/', 'https://github.com/matrix-org/sydent/commit/3d531ed50d2fd41ac387f36d44d3fb2c62dd22d3', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29431'} | null | {'https://github.com/matrix-org/sydent/commit/0f00412017f25619bc36c264b29ea96808bf310a', 'https://github.com/matrix-org/sydent/commit/8936925f561b0c352c2fa922d5097d7245aad00a', 'https://github.com/matrix-org/sydent/commit/3d531ed50d2fd41ac387f36d44d3fb2c62dd22d3', 'https://github.com/matrix-org/sydent/commit/9e573348d81df8191bbe8c266c01999c9d57cd5f'} | {'https://github.com/matrix-org/sydent/commit/0f00412017f25619bc36c264b29ea96808bf310a', 'https://github.com/matrix-org/sydent/commit/9e573348d81df8191bbe8c266c01999c9d57cd5f', 'https://github.com/matrix-org/sydent/commit/3d531ed50d2fd41ac387f36d44d3fb2c62dd22d3', 'https://github.com/matrix-org/sydent/commit/8936925f561b0c352c2fa922d5097d7245aad00a'} |
PyPI | GHSA-5jqp-qgf6-3pvh | Use of "infinity" as an input to datetime and date fields causes infinite loop in pydantic |
Impact
Passing either 'infinity', 'inf' or float('inf') (or their negatives) to datetime or date fields causes validation to run forever with 100% CPU usage (on one CPU).
Patches
Pydantic is be patched with fixes available in the following versions:
v1.8.2
v1.7.4
v1.6.2
All these versions are available on pypi, and will be available on conda-forge soon.
See the changelog for details.
Workarounds
If you absolutely can't upgrade, you can work around this risk using a validator to catch these values, brief demo:
from datetime import date
from pydantic import BaseModel, validator
class DemoModel(BaseModel):
date_of_birth: date
@validator('date_of_birth', pre=True)
def skip_infinite_values(cls, v):
try:
seconds = float(v)
except (ValueError, TypeError):
return v
else:
if seconds == float('inf'):
return date.max
elif seconds == float('-inf'):
return date.min
else:
return seconds
Note: this is not an ideal solution (in particular you'll need a slightly different function for datetimes), instead of a hack like this you should upgrade pydantic.
If you are not using v1.8.x, v1.7.x or v1.6.x and are unable to upgrade to a fixed version of pydantic, please create an issue requesting a back-port, and we will endeavour to release a patch for earlier versions of pydantic.
References
This was fixed in commit 7e83fdd.
| {'CVE-2021-29510'} | 2022-03-03T05:13:24.568205Z | 2021-05-13T20:23:17Z | LOW | null | {'CWE-835'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/S2HT266L6Q7H6ICP7DFGXOGBJHNNKMKB/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/UMKAJX4O6IGBBCE32CO2G7PZQCCQSBLV/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/UEFWM7DYKD2ZHE7R5YT5EQWJPV4ZKYRB/', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29510', 'https://github.com/samuelcolvin/pydantic/security/advisories/GHSA-5jqp-qgf6-3pvh', 'https://github.com/samuelcolvin/pydantic/commit/7e83fdd2563ffac081db7ecdf1affa65ef38c468'} | null | {'https://github.com/samuelcolvin/pydantic/commit/7e83fdd2563ffac081db7ecdf1affa65ef38c468'} | {'https://github.com/samuelcolvin/pydantic/commit/7e83fdd2563ffac081db7ecdf1affa65ef38c468'} |
PyPI | GHSA-rcrv-228c-gprj | Invalid URL generation in bitlyshortener | ### Impact
Due to a sudden upstream breaking change by Bitly, versions of `bitlyshortener` <0.6.0 generate invalid short URLs. All users are affected and must update immediately.
### Patches
Upgrading `bitlyshortener` to 0.6.0 or newer will prevent the generation such invalid short URLs.
### Workarounds
A workaround is to replace "https://j.mp/" in each generated short URL with "https://bit.ly/".
### References
* [Release notes](https://github.com/impredicative/bitlyshortener/releases) | null | 2022-03-03T05:12:58.970600Z | 2022-01-21T18:39:40Z | MODERATE | null | null | {'https://github.com/impredicative/bitlyshortener/security/advisories/GHSA-rcrv-228c-gprj', 'https://github.com/impredicative/bitlyshortener', 'https://github.com/impredicative/bitlyshortener/releases/tag/0.6.0', 'https://github.com/impredicative/bitlyshortener/commit/b307d70bedf745305fa0dd3c5c600d8cb88d09b5'} | null | {'https://github.com/impredicative/bitlyshortener/commit/b307d70bedf745305fa0dd3c5c600d8cb88d09b5'} | {'https://github.com/impredicative/bitlyshortener/commit/b307d70bedf745305fa0dd3c5c600d8cb88d09b5'} |
PyPI | GHSA-x83m-p7pv-ch8v | Division by 0 in `QuantizedAdd` | ### Impact
An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedAdd`:
```python
import tensorflow as tf
x = tf.constant([68, 228], shape=[2, 1], dtype=tf.quint8)
y = tf.constant([], shape=[2, 0], dtype=tf.quint8)
min_x = tf.constant(10.723421015884028)
max_x = tf.constant(15.19578006631113)
min_y = tf.constant(-5.539003866682977)
max_y = tf.constant(42.18819949559947)
tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero.
```cc
void VectorTensorAddition(const T* vector_data, float min_vector,
float max_vector, int64 vector_num_elements,
const T* tensor_data, float min_tensor,
float max_tensor, int64 tensor_num_elements,
float output_min, float output_max, Toutput* output) {
for (int i = 0; i < tensor_num_elements; ++i) {
const int64 vector_i = i % vector_num_elements;
...
}
}
```
Since `vector_num_elements` is [determined based on input shapes](https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0.
### Patches
We have patched the issue in GitHub commit [744009c9e5cc5d0447f0dc39d055f917e1fd9e16](https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16).
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-2021-29549'} | 2022-03-03T05:12:52.895242Z | 2021-05-21T14:23:38Z | LOW | null | {'CWE-369'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29549', 'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x83m-p7pv-ch8v'} | null | {'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16'} | {'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16'} |
PyPI | PYSEC-2021-531 | null | TensorFlow is an end-to-end open source platform for machine learning. A specially crafted TFLite model could trigger an OOB write on heap in the TFLite implementation of `ArgMin`/`ArgMax`(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/arg_min_max.cc#L52-L59). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the condition in the `if` is never true, so code writes past the last valid element of `output_dims->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. | {'GHSA-crch-j389-5f84', 'CVE-2021-29603'} | 2021-12-09T06:34:59.386976Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/c59c37e7b2d563967da813fa50fe20b21f4da683', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-crch-j389-5f84'} | null | {'https://github.com/tensorflow/tensorflow/commit/c59c37e7b2d563967da813fa50fe20b21f4da683'} | {'https://github.com/tensorflow/tensorflow/commit/c59c37e7b2d563967da813fa50fe20b21f4da683'} |
PyPI | PYSEC-2021-161 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/496c2630e51c1a478f095b084329acedb253db6b/tensorflow/core/kernels/conv_grad_shape_utils.cc#L130) does a modulus operation where the divisor is controlled by the caller. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-r4pj-74mg-8868', 'CVE-2021-29524'} | 2021-08-27T03:22:25.604287Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r4pj-74mg-8868'} | null | {'https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a'} | {'https://github.com/tensorflow/tensorflow/commit/fca9874a9b42a2134f907d2fb46ab774a831404a'} |
PyPI | PYSEC-2021-474 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger an integer division by zero undefined behavior in `tf.raw_ops.QuantizedBiasAdd`. This is because the implementation of the Eigen kernel(https://github.com/tensorflow/tensorflow/blob/61bca8bd5ba8a68b2d97435ddfafcdf2b85672cd/tensorflow/core/kernels/quantization_utils.h#L812-L849) does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29546', 'GHSA-m34j-p8rj-wjxq'} | 2021-12-09T06:34:50.495115Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m34j-p8rj-wjxq', 'https://github.com/tensorflow/tensorflow/commit/67784700869470d65d5f2ef20aeb5e97c31673cb'} | null | {'https://github.com/tensorflow/tensorflow/commit/67784700869470d65d5f2ef20aeb5e97c31673cb'} | {'https://github.com/tensorflow/tensorflow/commit/67784700869470d65d5f2ef20aeb5e97c31673cb'} |
PyPI | GHSA-8rm6-75mf-7r7r | Division by zero in TFLite's implementation of hashtable lookup | ### Impact
The TFLite implementation of hashtable lookup is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/hashtable_lookup.cc#L114-L115):
```cc
const int num_rows = SizeOfDimension(value, 0);
const int row_bytes = value->bytes / num_rows;
```
An attacker can craft a model such that `values`'s first dimension would be 0.
### Patches
We have patched the issue in GitHub commit [5117e0851348065ed59c991562c0ec80d9193db2](https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2).
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-2021-29604'} | 2022-03-03T05:12:57.748379Z | 2021-05-21T14:28:19Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rm6-75mf-7r7r', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29604', 'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2'} | null | {'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2'} | {'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2'} |
PyPI | GHSA-gvm4-h8j3-rjrq | CHECK-fail in `LoadAndRemapMatrix` | ### Impact
An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from `tf.raw_ops.LoadAndRemapMatrix`:
```python
import tensorflow as tf
ckpt_path = tf.constant([], shape=[0], dtype=tf.string)
old_tensor_name = tf.constant("")
row_remapping = tf.constant([], shape=[0], dtype=tf.int64)
col_remapping = tf.constant([1], shape=[1], dtype=tf.int64)
initializing_values = tf.constant(1.0)
tf.raw_ops.LoadAndRemapMatrix(
ckpt_path=ckpt_path, old_tensor_name=old_tensor_name,
row_remapping=row_remapping, col_remapping=col_remapping,
initializing_values=initializing_values, num_rows=0, num_cols=1)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) assumes that the `ckpt_path` is always a valid scalar.
```cc
const string& ckpt_path = ckpt_path_t->scalar<tstring>()();
```
However, an attacker can send any other tensor as the first argument of `LoadAndRemapMatrix`. This would cause the rank `CHECK` in `scalar<T>()()` to trigger and terminate the process.
### Patches
We have patched the issue in GitHub commit [77dd114513d7796e1e2b8aece214a380af26fbf4](https://github.com/tensorflow/tensorflow/commit/77dd114513d7796e1e2b8aece214a380af26fbf4).
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-2021-29561'} | 2022-03-03T05:13:13.590105Z | 2021-05-21T14:24:59Z | LOW | null | {'CWE-617'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gvm4-h8j3-rjrq', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29561', 'https://github.com/tensorflow/tensorflow/commit/77dd114513d7796e1e2b8aece214a380af26fbf4'} | null | {'https://github.com/tensorflow/tensorflow/commit/77dd114513d7796e1e2b8aece214a380af26fbf4'} | {'https://github.com/tensorflow/tensorflow/commit/77dd114513d7796e1e2b8aece214a380af26fbf4'} |
PyPI | GHSA-wh37-37xw-54hr | Improper Authentication in requests-kerberos | python-requests-Kerberos through 0.5 does not handle mutual authentication | {'CVE-2014-8650'} | 2022-03-23T20:45:05.632995Z | 2020-03-10T18:02:31Z | CRITICAL | null | {'CWE-287'} | {'http://www.securityfocus.com/bid/70909', 'https://nvd.nist.gov/vuln/detail/CVE-2014-8650', 'http://www.openwall.com/lists/oss-security/2014/11/07/1', 'https://github.com/requests/requests-kerberos/issues/35', 'https://github.com/requests/requests-kerberos/blob/0.6/HISTORY.rst', 'https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2014-8650', 'https://github.com/mkomitee/requests-kerberos/commit/9c1e08cc17bb6950455a85d33d391ecd2bce6eb6', 'https://github.com/requests/requests-kerberos/pull/36', 'https://github.com/mkomitee/requests-kerberos', 'https://security-tracker.debian.org/tracker/CVE-2014-8650'} | null | {'https://github.com/mkomitee/requests-kerberos/commit/9c1e08cc17bb6950455a85d33d391ecd2bce6eb6'} | {'https://github.com/mkomitee/requests-kerberos/commit/9c1e08cc17bb6950455a85d33d391ecd2bce6eb6'} |
PyPI | PYSEC-2021-248 | null | TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseReshape` results in a denial of service based on a `CHECK`-failure. The implementation(https://github.com/tensorflow/tensorflow/blob/e87b51ce05c3eb172065a6ea5f48415854223285/tensorflow/core/kernels/sparse_reshape_op.cc#L40) has no validation that the input arguments specify a valid sparse tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are the only affected versions. | {'GHSA-9rpc-5v9q-5r7f', 'CVE-2021-29611'} | 2021-08-27T03:22:41.176381Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9rpc-5v9q-5r7f', 'https://github.com/tensorflow/tensorflow/commit/1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6'} | null | {'https://github.com/tensorflow/tensorflow/commit/1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6'} | {'https://github.com/tensorflow/tensorflow/commit/1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6'} |
PyPI | PYSEC-2021-618 | 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'} | 2021-12-09T06:35:08.821435Z | 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 | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} |
PyPI | PYSEC-2022-113 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `MapStage` is vulnerable a `CHECK`-fail if the key tensor is not a scalar. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-gcvh-66ff-4mwm', 'CVE-2022-21734'} | 2022-03-09T00:18:24.222206Z | 2022-02-03T13:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gcvh-66ff-4mwm', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/map_stage_op.cc#L519-L550', 'https://github.com/tensorflow/tensorflow/commit/f57315566d7094f322b784947093406c2aea0d7d'} | null | {'https://github.com/tensorflow/tensorflow/commit/f57315566d7094f322b784947093406c2aea0d7d'} | {'https://github.com/tensorflow/tensorflow/commit/f57315566d7094f322b784947093406c2aea0d7d'} |
PyPI | GHSA-p737-p57g-4cpr | Insertion of Sensitive Information into Log File in Jupyter notebook | ### Impact
_What kind of vulnerability is it?_
Anytime a 5xx error is triggered, the auth cookie and other header values are recorded in Jupyter Server logs by default. Considering these logs do not require root access, an attacker can monitor these logs, steal sensitive auth/cookie information, and gain access to the Jupyter server.
### Patches
_Has the problem been patched? What versions should users upgrade to?_
Upgrade to Jupyter Server version 1.15.4
### For more information
If you have any questions or comments about this advisory, or vulnerabilities to report, please email our security list [security@ipython.org](mailto:security@ipython.org).
Credit: @3coins for reporting. Thank you!
| {'CVE-2022-24757'} | 2022-03-29T22:02:00.697152Z | 2022-03-25T19:20:39Z | HIGH | null | {'CWE-532'} | {'https://github.com/jupyter-server/jupyter_server/security/advisories/GHSA-p737-p57g-4cpr', 'https://github.com/jupyter-server/jupyter_server', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24757', 'https://github.com/jupyter-server/jupyter_server/commit/a5683aca0b0e412672ac6218d09f74d44ca0de5a'} | null | {'https://github.com/jupyter-server/jupyter_server/commit/a5683aca0b0e412672ac6218d09f74d44ca0de5a'} | {'https://github.com/jupyter-server/jupyter_server/commit/a5683aca0b0e412672ac6218d09f74d44ca0de5a'} |
PyPI | PYSEC-2018-68 | null | An issue was discovered in Project Jupyter JupyterHub OAuthenticator 0.6.x before 0.6.2 and 0.7.x before 0.7.3. When using JupyterHub with GitLab group whitelisting for access control, group membership was not checked correctly, allowing members not in the whitelisted groups to create accounts on the Hub. (Users were not allowed to access other users' accounts, but could create their own accounts on the Hub linked to their GitLab account. GitLab authentication not using gitlab_group_whitelist is unaffected. No other Authenticators are affected.) | {'CVE-2018-7206'} | 2021-08-25T04:30:14.910557Z | 2018-02-18T03:29:00Z | null | null | null | {'https://blog.jupyter.org/security-fix-for-jupyterhub-gitlab-oauthenticator-7b14571d1f76', 'https://github.com/jupyterhub/oauthenticator/blob/8499dc2/CHANGELOG.md#073---2018-02-16', 'https://github.com/jupyterhub/oauthenticator/commit/1845c0e4b1bff3462c91c3108c85205acd3c75a2'} | null | {'https://github.com/jupyterhub/oauthenticator/commit/1845c0e4b1bff3462c91c3108c85205acd3c75a2'} | {'https://github.com/jupyterhub/oauthenticator/commit/1845c0e4b1bff3462c91c3108c85205acd3c75a2'} |
PyPI | PYSEC-2021-819 | null | TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` can trigger a read outside of bounds of heap allocated array. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-fr77-rrx3-cp7g', 'CVE-2021-41212'} | 2021-12-09T06:35:42.944198Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g', 'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} | null | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} | {'https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8'} |
PyPI | PYSEC-2020-233 | null | In freewvs before 0.1.1, a directory structure of more than 1000 nested directories can interrupt a freewvs scan due to Python's recursion limit and os.walk(). This can be problematic in a case where an administrator scans the dirs of potentially untrusted users. This has been patched in 0.1.1. | {'GHSA-7pmh-vrww-25xx', 'CVE-2020-15101'} | 2021-08-27T03:22:04.227798Z | 2020-07-14T22:15:00Z | null | null | null | {'https://github.com/schokokeksorg/freewvs/security/advisories/GHSA-7pmh-vrww-25xx', 'https://github.com/schokokeksorg/freewvs/commit/83a6b55c0435c69f447488b791555e6078803143'} | null | {'https://github.com/schokokeksorg/freewvs/commit/83a6b55c0435c69f447488b791555e6078803143'} | {'https://github.com/schokokeksorg/freewvs/commit/83a6b55c0435c69f447488b791555e6078803143'} |
PyPI | PYSEC-2021-233 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `EmbeddingLookup` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e4b29809543b250bc9b19678ec4776299dd569ba/tensorflow/lite/kernels/embedding_lookup.cc#L73-L74). An attacker can craft a model such that the first dimension of the `value` input is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-4vrf-ff7v-hpgr', 'CVE-2021-29596'} | 2021-08-27T03:22:38.479573Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4vrf-ff7v-hpgr'} | null | {'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e'} | {'https://github.com/tensorflow/tensorflow/commit/f61c57bd425878be108ec787f4d96390579fb83e'} |
PyPI | PYSEC-2021-663 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-8c89-2vwr-chcq', 'CVE-2021-29537'} | 2021-12-09T06:35:21.193841Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8c89-2vwr-chcq'} | null | {'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694'} | {'https://github.com/tensorflow/tensorflow/commit/f6c40f0c6cbf00d46c7717a26419f2062f2f8694'} |
PyPI | PYSEC-2021-399 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `ParallelConcat` misses some input validation and can produce a division by 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-7v94-64hj-m82h', 'CVE-2021-41207'} | 2021-11-13T06:52:43.264871Z | 2021-11-05T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7v94-64hj-m82h', 'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} | null | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} | {'https://github.com/tensorflow/tensorflow/commit/f2c3931113eaafe9ef558faaddd48e00a6606235'} |
PyPI | PYSEC-2021-376 | null | python-tuf is a Python reference implementation of The Update Framework (TUF). In both clients (`tuf/client` and `tuf/ngclient`), there is a path traversal vulnerability that in the worst case can overwrite files ending in `.json` anywhere on the client system on a call to `get_one_valid_targetinfo()`. It occurs because the rolename is used to form the filename, and may contain path traversal characters (ie `../../name.json`). The impact is mitigated by a few facts: It only affects implementations that allow arbitrary rolename selection for delegated targets metadata, The attack requires the ability to A) insert new metadata for the path-traversing role and B) get the role delegated by an existing targets metadata, The written file content is heavily restricted since it needs to be a valid, signed targets file. The file extension is always .json. A fix is available in version 0.19 or newer. There are no workarounds that do not require code changes. Clients can restrict the allowed character set for rolenames, or they can store metadata in files named in a way that is not vulnerable: neither of these approaches is possible without modifying python-tuf. | {'CVE-2021-41131', 'GHSA-wjw6-2cqr-j4qr'} | 2021-10-22T22:29:45.339771Z | 2021-10-19T18:15:00Z | null | null | null | {'https://github.com/theupdateframework/python-tuf/commit/4ad7ae48fda594b640139c3b7eae21ed5155a102', 'https://github.com/theupdateframework/python-tuf/issues/1527', 'https://github.com/theupdateframework/python-tuf/security/advisories/GHSA-wjw6-2cqr-j4qr'} | null | {'https://github.com/theupdateframework/python-tuf/commit/4ad7ae48fda594b640139c3b7eae21ed5155a102'} | {'https://github.com/theupdateframework/python-tuf/commit/4ad7ae48fda594b640139c3b7eae21ed5155a102'} |
PyPI | PYSEC-2021-726 | null | TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `OneHot` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/f61c57bd425878be108ec787f4d96390579fb83e/tensorflow/lite/kernels/one_hot.cc#L68-L72). An attacker can craft a model such that at least one of the dimensions of `indices` would be 0. In turn, the `prefix_dim_size` value would become 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-j8qh-3xrq-c825', 'CVE-2021-29600'} | 2021-12-09T06:35:32.041975Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3ebedd7e345453d68e279cfc3e4072648e5e12e5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8qh-3xrq-c825'} | null | {'https://github.com/tensorflow/tensorflow/commit/3ebedd7e345453d68e279cfc3e4072648e5e12e5'} | {'https://github.com/tensorflow/tensorflow/commit/3ebedd7e345453d68e279cfc3e4072648e5e12e5'} |
PyPI | GHSA-4rcq-jv2f-898j | Incorrect Provision of Specified Functionality in qutebrowser | # Description
After a certificate error was overridden by the user, qutebrowser displays the URL as yellow (`colors.statusbar.url.warn.fg`). However, when the affected website was subsequently loaded again, the URL was mistakenly displayed as green (`colors.statusbar.url.success_https`). While the user already has seen a certificate error prompt at this point (or set `content.ssl_strict` to `false` which is not recommended), this could still provide a false sense of security.
# Affected versions and patches
All versions of qutebrowser are believed to be affected, though versions before v0.11.x couldn't be tested.
The issue is fixed in qutebrowser v1.11.1 (pending release) and v1.12.0 (unreleased). Backported patches for older versions are available, but no further releases are planned.
# Mitigation
If you are unable to upgrade:
- Treat any host with a certificate exception as insecure, ignoring the URL color
- Or set `content.ssl_strict` to `True` (instead of `'ask'`), preventing certificate exceptions
# References
- qutebrowser issue: https://github.com/qutebrowser/qutebrowser/issues/5403
- Fix (master branch): https://github.com/qutebrowser/qutebrowser/commit/021ab572a319ca3db5907a33a59774f502b3b975
- Related issue for KDE Falkon: https://bugs.kde.org/show_bug.cgi?id=420902
- Related issue for eric6 Web Browser: https://tracker.die-offenbachs.homelinux.org/eric/issue328 (fixed in eric6 20.6) | {'CVE-2020-11054'} | 2022-03-03T05:13:10.491189Z | 2020-05-08T19:05:05Z | LOW | null | {'CWE-684'} | {'https://github.com/qutebrowser/qutebrowser/security/advisories/GHSA-4rcq-jv2f-898j', 'https://github.com/qutebrowser/qutebrowser/commit/19f01bb42d02da539446a52a25bb0c1232b86327', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/7YWJ5QNHXKTGG5NLV7EGEOKPBVZBA5GS/', 'https://nvd.nist.gov/vuln/detail/CVE-2020-11054', 'https://bugs.kde.org/show_bug.cgi?id=420902', 'https://github.com/qutebrowser/qutebrowser/commit/f5d801251aa5436aff44660c87d7013e29ac5864', 'https://github.com/qutebrowser/qutebrowser/commit/021ab572a319ca3db5907a33a59774f502b3b975', 'https://github.com/qutebrowser/qutebrowser/commit/1b7946ed14b386a24db050f2d6dba81ba6518755', 'https://github.com/qutebrowser/qutebrowser/commit/4020210b193f77cf1785b21717f6ef7c5de5f0f8', 'https://github.com/qutebrowser/qutebrowser/commit/2281a205c3e70ec20f35ec8fafecee0d5c4f3478', 'https://github.com/qutebrowser/qutebrowser/issues/5403', 'https://github.com/qutebrowser/qutebrowser/commit/a45ca9c788f648d10cccce2af41405bf25ee2948', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/MKAZOOTJ2MBHTYVYQQ52NL53F5CB2XAP/', 'https://github.com/qutebrowser/qutebrowser/commit/d28ed758d077a5bf19ddac4da468f7224114df23', 'https://github.com/qutebrowser/qutebrowser/commit/9bd1cf585fccdfe8318fff7af793730e74a04db3', 'https://tracker.die-offenbachs.homelinux.org/eric/issue328', 'https://github.com/qutebrowser/qutebrowser/commit/6821c236f9ae23adf21d46ce0d56768ac8d0c467'} | null | {'https://github.com/qutebrowser/qutebrowser/commit/4020210b193f77cf1785b21717f6ef7c5de5f0f8', 'https://github.com/qutebrowser/qutebrowser/commit/f5d801251aa5436aff44660c87d7013e29ac5864', 'https://github.com/qutebrowser/qutebrowser/commit/021ab572a319ca3db5907a33a59774f502b3b975', 'https://github.com/qutebrowser/qutebrowser/commit/9bd1cf585fccdfe8318fff7af793730e74a04db3', 'https://github.com/qutebrowser/qutebrowser/commit/d28ed758d077a5bf19ddac4da468f7224114df23', 'https://github.com/qutebrowser/qutebrowser/commit/6821c236f9ae23adf21d46ce0d56768ac8d0c467', 'https://github.com/qutebrowser/qutebrowser/commit/a45ca9c788f648d10cccce2af41405bf25ee2948', 'https://github.com/qutebrowser/qutebrowser/commit/2281a205c3e70ec20f35ec8fafecee0d5c4f3478', 'https://github.com/qutebrowser/qutebrowser/commit/1b7946ed14b386a24db050f2d6dba81ba6518755', 'https://github.com/qutebrowser/qutebrowser/commit/19f01bb42d02da539446a52a25bb0c1232b86327'} | {'https://github.com/qutebrowser/qutebrowser/commit/6821c236f9ae23adf21d46ce0d56768ac8d0c467', 'https://github.com/qutebrowser/qutebrowser/commit/2281a205c3e70ec20f35ec8fafecee0d5c4f3478', 'https://github.com/qutebrowser/qutebrowser/commit/d28ed758d077a5bf19ddac4da468f7224114df23', 'https://github.com/qutebrowser/qutebrowser/commit/19f01bb42d02da539446a52a25bb0c1232b86327', 'https://github.com/qutebrowser/qutebrowser/commit/1b7946ed14b386a24db050f2d6dba81ba6518755', 'https://github.com/qutebrowser/qutebrowser/commit/4020210b193f77cf1785b21717f6ef7c5de5f0f8', 'https://github.com/qutebrowser/qutebrowser/commit/a45ca9c788f648d10cccce2af41405bf25ee2948', 'https://github.com/qutebrowser/qutebrowser/commit/f5d801251aa5436aff44660c87d7013e29ac5864', 'https://github.com/qutebrowser/qutebrowser/commit/9bd1cf585fccdfe8318fff7af793730e74a04db3', 'https://github.com/qutebrowser/qutebrowser/commit/021ab572a319ca3db5907a33a59774f502b3b975'} |
PyPI | PYSEC-2022-144 | null | Tensorflow is an Open Source Machine Learning Framework. During shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'CVE-2022-23580', 'GHSA-627q-g293-49q7'} | 2022-03-09T00:18:28.435695Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-627q-g293-49q7', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/shape_inference.cc#L788-L790', 'https://github.com/tensorflow/tensorflow/commit/1361fb7e29449629e1df94d44e0427ebec8c83c7'} | null | {'https://github.com/tensorflow/tensorflow/commit/1361fb7e29449629e1df94d44e0427ebec8c83c7'} | {'https://github.com/tensorflow/tensorflow/commit/1361fb7e29449629e1df94d44e0427ebec8c83c7'} |
PyPI | PYSEC-2021-458 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments. Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since `OP_REQUIRES`(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function. Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`. 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-xcwj-wfcm-m23c', 'CVE-2021-29530'} | 2021-12-09T06:34:48.047849Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xcwj-wfcm-m23c', 'https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd'} | null | {'https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd'} | {'https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd'} |
PyPI | GHSA-mp9m-g7qj-6vqr | Unauthorized privilege escalation in Mod module | ### Impact
An unauthorized privilege escalation exploit has been discovered in the Mod module: this exploit allows Discord users with a high privilege level within the guild to bypass hierarchy checks when the application is in a specific condition that is beyond that user's control. By abusing this exploit, it's possible to perform destructive actions within the guild the user has high privileges in.
### Patches
This exploit has been fixed on version & ``3.4.1``.
### Workarounds
Unloading the Mod module with ``unload mod`` __or__, disabling the ``massban`` command with ``command disable global massban`` can render this exploit not accessible. We still highly recommend updating to ``3.4.1`` to completely patch this issue.
### References
* https://github.com/Cog-Creators/Red-DiscordBot/commit/726bfd38adfdfaef760412a68e01447b470f438b
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [Cog-Creators/Red-DiscordBot](https://github.com/Cog-Creators/Red-DiscordBot)
* Over on our [Discord server](https://discord.gg/red) | null | 2022-03-03T05:14:17.655531Z | 2020-10-27T20:30:48Z | MODERATE | null | {'CWE-285'} | {'https://github.com/Cog-Creators/Red-DiscordBot/', 'https://github.com/Cog-Creators/Red-DiscordBot/security/advisories/GHSA-mp9m-g7qj-6vqr', 'https://pypi.org/project/Red-DiscordBot/', 'https://github.com/Cog-Creators/Red-DiscordBot/releases/tag/3.4.1', 'https://github.com/Cog-Creators/Red-DiscordBot/commit/726bfd38adfdfaef760412a68e01447b470f438b'} | null | {'https://github.com/Cog-Creators/Red-DiscordBot/commit/726bfd38adfdfaef760412a68e01447b470f438b'} | {'https://github.com/Cog-Creators/Red-DiscordBot/commit/726bfd38adfdfaef760412a68e01447b470f438b'} |
PyPI | PYSEC-2022-84 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `OpLevelCostEstimator::CalculateTensorSize` is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements. 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-2022-23575', 'GHSA-c94w-c95p-phf8'} | 2022-03-09T00:17:34.290009Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/fcd18ce3101f245b083b30655c27b239dc72221e', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c94w-c95p-phf8', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L1552-L1558'} | null | {'https://github.com/tensorflow/tensorflow/commit/fcd18ce3101f245b083b30655c27b239dc72221e'} | {'https://github.com/tensorflow/tensorflow/commit/fcd18ce3101f245b083b30655c27b239dc72221e'} |
PyPI | GHSA-4q96-6xhq-ff43 | malicious SVG attachment causing stored XSS vulnerability | ### Impact
An attacker with `write` permissions can upload an SVG file that contains malicious javascript. This javascript will be executed in a user's browser when the user is viewing that SVG file on the wiki.
### Patches
Users are strongly advised to upgrade to a patched version.
MoinMoin Wiki 1.9.11 has the necessary fixes and also contains other important fixes.
### Workarounds
It is not advised to work around this, but to upgrade MoinMoin to a patched version.
That said, a work around via a Content Security Policy in the web server might be possible.
Also, it is of course helpful if you give `write` permissions (which include uploading attachments) only to trusted users.
### For more information
If you have any questions or comments about this advisory, email me at [twaldmann@thinkmo.de](mailto:twaldmann@thinkmo.de).
### Credits
This vulnerability was discovered by:
Catarina Leite from the Checkmarx SCA AppSec team | {'CVE-2020-15275'} | 2022-03-03T05:12:43.570455Z | 2020-11-11T15:54:41Z | LOW | null | {'CWE-79'} | {'https://nvd.nist.gov/vuln/detail/CVE-2020-15275', 'https://github.com/moinwiki/moin-1.9/commit/31de9139d0aabc171e94032168399b4a0b2a88a2', 'https://pypi.org/project/moin/', 'https://advisory.checkmarx.net/advisory/CX-2020-4285', 'https://github.com/moinwiki/moin-1.9/security/advisories/GHSA-4q96-6xhq-ff43', 'https://github.com/moinwiki/moin-1.9/releases/tag/1.9.11'} | null | {'https://github.com/moinwiki/moin-1.9/commit/31de9139d0aabc171e94032168399b4a0b2a88a2'} | {'https://github.com/moinwiki/moin-1.9/commit/31de9139d0aabc171e94032168399b4a0b2a88a2'} |
PyPI | PYSEC-2021-634 | null | TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's Grappler optimizer has a use of unitialized variable. If the `train_nodes` vector (obtained from the saved model that gets optimized) does not contain a `Dequeue` node, then `dequeue_node` is left unitialized. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41225', 'GHSA-7r94-xv9v-63jw'} | 2021-12-09T06:35:11.117498Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-7r94-xv9v-63jw'} | null | {'https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3'} | {'https://github.com/tensorflow/tensorflow/commit/68867bf01239d9e1048f98cbad185bf4761bedd3'} |
PyPI | PYSEC-2021-264 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.ResourceScatterDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/resource_variable_ops.cc#L865) uses a common class for all binary operations but fails to treat the division by 0 case separately. We have patched the issue in GitHub commit 4aacb30888638da75023e6601149415b39763d76. 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-2021-37642', 'GHSA-ch4f-829c-v5pw'} | 2021-08-27T03:22:43.277267Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/4aacb30888638da75023e6601149415b39763d76', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-ch4f-829c-v5pw'} | null | {'https://github.com/tensorflow/tensorflow/commit/4aacb30888638da75023e6601149415b39763d76'} | {'https://github.com/tensorflow/tensorflow/commit/4aacb30888638da75023e6601149415b39763d76'} |
PyPI | PYSEC-2020-264 | null | In "I hate money" before version 4.1.5, an authenticated member of one project can modify and delete members of another project, without knowledge of this other project's private code. This can be further exploited to access all bills of another project without knowledge of this other project's private code. With the default configuration, anybody is allowed to create a new project. An attacker can create a new project and then use it to become authenticated and exploit this flaw. As such, the exposure is similar to an unauthenticated attack, because it is trivial to become authenticated. This is fixed in version 4.1.5. | {'CVE-2020-15120', 'GHSA-67j9-c52g-w2q9'} | 2021-11-16T03:58:44.236145Z | 2020-07-27T18:15:00Z | null | null | null | {'https://github.com/spiral-project/ihatemoney/security/advisories/GHSA-67j9-c52g-w2q9', 'https://github.com/spiral-project/ihatemoney/commit/8d77cf5d5646e1d2d8ded13f0660638f57e98471'} | null | {'https://github.com/spiral-project/ihatemoney/commit/8d77cf5d5646e1d2d8ded13f0660638f57e98471'} | {'https://github.com/spiral-project/ihatemoney/commit/8d77cf5d5646e1d2d8ded13f0660638f57e98471'} |
PyPI | GHSA-rwr3-c2q8-gm56 | Moderate severity vulnerability that affects Pillow | Pillow before 3.3.2 allows context-dependent attackers to obtain sensitive information by using the "crafted image file" approach, related to an "Integer Overflow" issue affecting the Image.core.map_buffer in map.c component. | {'CVE-2016-9189'} | 2022-03-03T05:13:34.860527Z | 2018-07-24T20:08:27Z | MODERATE | null | {'CWE-190'} | {'http://pillow.readthedocs.io/en/3.4.x/releasenotes/3.3.2.html', 'https://nvd.nist.gov/vuln/detail/CVE-2016-9189', 'https://github.com/python-pillow/Pillow/issues/2105', 'http://www.debian.org/security/2016/dsa-3710', 'https://github.com/python-pillow/Pillow/', 'http://www.securityfocus.com/bid/94234', 'https://security.gentoo.org/glsa/201612-52', 'https://github.com/advisories/GHSA-rwr3-c2q8-gm56', 'https://github.com/python-pillow/Pillow/pull/2146/commits/c50ebe6459a131a1ea8ca531f10da616d3ceaa0f'} | null | {'https://github.com/python-pillow/Pillow/pull/2146/commits/c50ebe6459a131a1ea8ca531f10da616d3ceaa0f'} | {'https://github.com/python-pillow/Pillow/pull/2146/commits/c50ebe6459a131a1ea8ca531f10da616d3ceaa0f'} |
PyPI | PYSEC-2020-321 | null | In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. | {'CVE-2020-15206', 'GHSA-w5gh-2wr2-pm6g'} | 2021-12-09T06:35:14.366685Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-w5gh-2wr2-pm6g', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6'} | null | {'https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6'} | {'https://github.com/tensorflow/tensorflow/commit/adf095206f25471e864a8e63a0f1caef53a0e3a6'} |
PyPI | PYSEC-2021-771 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if `x` and `v` are empty but the code uses `||` instead of `&&`. We have patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-cm5x-837x-jf3c', 'CVE-2021-37660'} | 2021-12-09T06:35:37.526889Z | 2021-08-12T18:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cm5x-837x-jf3c', 'https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618'} | null | {'https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618'} | {'https://github.com/tensorflow/tensorflow/commit/e86605c0a336c088b638da02135ea6f9f6753618'} |
PyPI | PYSEC-2021-321 | null | Wasmtime is an open source runtime for WebAssembly & WASI. In Wasmtime from version 0.26.0 and before version 0.30.0 is affected by a memory unsoundness vulnerability. There was an invalid free and out-of-bounds read and write bug when running Wasm that uses `externref`s in Wasmtime. To trigger this bug, Wasmtime needs to be running Wasm that uses `externref`s, the host creates non-null `externrefs`, Wasmtime performs a garbage collection (GC), and there has to be a Wasm frame on the stack that is at a GC safepoint where there are no live references at this safepoint, and there is a safepoint with live references earlier in this frame's function. Under this scenario, Wasmtime would incorrectly use the GC stack map for the safepoint from earlier in the function instead of the empty safepoint. This would result in Wasmtime treating arbitrary stack slots as `externref`s that needed to be rooted for GC. At the *next* GC, it would be determined that nothing was referencing these bogus `externref`s (because nothing could ever reference them, because they are not really `externref`s) and then Wasmtime would deallocate them and run `<ExternRef as Drop>::drop` on them. This results in a free of memory that is not necessarily on the heap (and shouldn't be freed at this moment even if it was), as well as potential out-of-bounds reads and writes. Even though support for `externref`s (via the reference types proposal) is enabled by default, unless you are creating non-null `externref`s in your host code or explicitly triggering GCs, you cannot be affected by this bug. We have reason to believe that the effective impact of this bug is relatively small because usage of `externref` is currently quite rare. This bug has been patched and users should upgrade to Wasmtime version 0.30.0. If you cannot upgrade Wasmtime at this time, you can avoid this bug by disabling the reference types proposal by passing `false` to `wasmtime::Config::wasm_reference_types`. | {'GHSA-4873-36h9-wv49', 'CVE-2021-39218'} | 2021-09-17T22:30:49.898970Z | 2021-09-17T21:15:00Z | null | null | null | {'https://github.com/bytecodealliance/wasmtime/commit/398a73f0dd862dbe703212ebae8e34036a18c11c', 'https://github.com/bytecodealliance/wasmtime/security/advisories/GHSA-4873-36h9-wv49', 'https://crates.io/crates/wasmtime'} | null | {'https://github.com/bytecodealliance/wasmtime/commit/398a73f0dd862dbe703212ebae8e34036a18c11c'} | {'https://github.com/bytecodealliance/wasmtime/commit/398a73f0dd862dbe703212ebae8e34036a18c11c'} |
PyPI | PYSEC-2022-152 | null | Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that Grappler optimizer would attempt to build a tensor using a reference `dtype`. This would result in a crash due to a `CHECK`-fail in the `Tensor` constructor as reference types are not allowed. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-fx5c-h9f6-rv7c', 'CVE-2022-23588'} | 2022-03-09T00:18:29.595866Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/optimizers/constant_folding.cc#L1328-L1402', 'https://github.com/tensorflow/tensorflow/commit/6b5adc0877de832b2a7c189532dbbbc64622eeb6', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fx5c-h9f6-rv7c', 'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/framework/tensor.cc#L733-L781'} | null | {'https://github.com/tensorflow/tensorflow/commit/6b5adc0877de832b2a7c189532dbbbc64622eeb6'} | {'https://github.com/tensorflow/tensorflow/commit/6b5adc0877de832b2a7c189532dbbbc64622eeb6'} |
PyPI | GHSA-gjqc-q9g6-q2j3 | `CHECK`-failures in binary ops in Tensorflow | ### Impact
A malicious user can cause a denial of service by altering a `SavedModel` such that [any binary op](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/cwise_ops_common.h#L88-L137) would trigger `CHECK` failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the `dtype` no longer matches the `dtype` expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved:
```cc
functor::BinaryFunctor<Device, Functor, 1>()(
eigen_device, out->template flat<Tout>(),
input_0.template flat<Tin>(), input_1.template flat<Tin>(),
error_ptr);
```
If `Tin` and `Tout` don't match the type of data in `out` and `input_*` tensors then `flat<*>` would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a `CHECK` crash, hence a denial of service.
### Patches
We have patched the issue in GitHub commit [a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9](https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9).
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-2022-23583'} | 2022-03-03T05:13:44.768574Z | 2022-02-10T00:34:13Z | MODERATE | null | {'CWE-617'} | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/cwise_ops_common.h#L88-L137', 'https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9', 'https://github.com/tensorflow/tensorflow/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23583', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gjqc-q9g6-q2j3'} | null | {'https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9'} | {'https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9'} |
PyPI | GHSA-mq5p-2mcr-m52j | Code injection in nbgitpuller | ### Impact
Due to an unsanitized input, visiting maliciously crafted links could result in arbitrary code execution in the user environment.
### Patches
0.10.2
### Workarounds
None, other than upgrade to 0.10.2 or downgrade to 0.8.x.
### For more information
If you have any questions or comments about this advisory:
* Open an issue in [nbgitpuller](https://github.com/jupyterhub/nbgitpuller/issues)
* Email our security team at [security@ipython.org](mailto:security@ipython.org)
| {'CVE-2021-39160'} | 2022-03-22T21:32:03.613306Z | 2021-08-30T16:17:06Z | CRITICAL | null | {'CWE-94'} | {'https://github.com/jupyterhub/nbgitpuller/security/advisories/GHSA-mq5p-2mcr-m52j', 'https://github.com/jupyterhub/nbgitpuller/blob/main/CHANGELOG.md#0102---2021-08-25', 'https://github.com/jupyterhub/nbgitpuller/commit/07690644f29a566011dd0d7ba14cae3eb0490481', 'https://github.com/jupyterhub/nbgitpuller', 'https://nvd.nist.gov/vuln/detail/CVE-2021-39160'} | null | {'https://github.com/jupyterhub/nbgitpuller/commit/07690644f29a566011dd0d7ba14cae3eb0490481'} | {'https://github.com/jupyterhub/nbgitpuller/commit/07690644f29a566011dd0d7ba14cae3eb0490481'} |
PyPI | GHSA-rg3m-hqc5-344v | `SparseFillEmptyRows` heap OOB | ### Impact
The [implementation](https://github.com/tensorflow/tensorflow/blob/e71b86d47f8bc1816bf54d7bddc4170e47670b97/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L194-L241) of `SparseFillEmptyRows` can be made to trigger a heap OOB access:
```python
import tensorflow as tf
data=tf.raw_ops.SparseFillEmptyRows(
indices=[[0,0],[0,0],[0,0]],
values=['sssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss'],
dense_shape=[5,3],
default_value='o')
```
This occurs whenever the size of `indices` does not match the size of `values`.
### Patches
We have patched the issue in GitHub commit [67bfd9feeecfb3c61d80f0e46d89c170fbee682b](https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b).
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-2021-41224'} | 2022-03-03T05:12:57.990913Z | 2021-11-10T18:45:55Z | HIGH | null | {'CWE-125'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b', 'https://nvd.nist.gov/vuln/detail/CVE-2021-41224'} | null | {'https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b'} | {'https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b'} |
PyPI | PYSEC-2022-92 | null | Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that any binary op would trigger `CHECK` failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the `dtype` no longer matches the `dtype` expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If `Tin` and `Tout` don't match the type of data in `out` and `input_*` tensors then `flat<*>` would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a `CHECK` crash, hence a denial of service. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'CVE-2022-23583', 'GHSA-gjqc-q9g6-q2j3'} | 2022-03-09T00:17:35.311751Z | 2022-02-04T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/cwise_ops_common.h#L88-L137', 'https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gjqc-q9g6-q2j3'} | null | {'https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9'} | {'https://github.com/tensorflow/tensorflow/commit/a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9'} |
PyPI | PYSEC-2016-37 | null | Radicale before 1.1 allows remote authenticated users to bypass owner_write and owner_only limitations via regex metacharacters in the user name, as demonstrated by ".*". | {'CVE-2015-8748'} | 2021-12-14T08:18:58.669643Z | 2016-02-03T18:59:00Z | null | null | null | {'https://pypi.org/project/radicale', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-January/175738.html', 'http://lists.fedoraproject.org/pipermail/package-announce/2016-January/175776.html', 'https://nvd.nist.gov/vuln/detail/CVE-2015-8748', 'http://www.securityfocus.com/bid/80255', 'http://www.openwall.com/lists/oss-security/2016/01/05/7', 'http://www.debian.org/security/2016/dsa-3462', 'https://github.com/Unrud/Radicale/commit/4bfe7c9f7991d534c8b9fbe153af9d341f925f98', 'https://github.com/Kozea/Radicale/pull/341', 'http://www.openwall.com/lists/oss-security/2016/01/06/4'} | null | {'https://github.com/Unrud/Radicale/commit/4bfe7c9f7991d534c8b9fbe153af9d341f925f98'} | {'https://github.com/Unrud/Radicale/commit/4bfe7c9f7991d534c8b9fbe153af9d341f925f98'} |
PyPI | GHSA-gq9m-qvpx-68hc | Insufficient Entropy in werkzeug | Pallets Werkzeug before 0.15.3, when used with Docker, has insufficient debugger PIN randomness because Docker containers share the same machine id. | {'CVE-2019-14806'} | 2022-03-03T05:12:59.845405Z | 2019-08-21T16:15:24Z | HIGH | null | {'CWE-331'} | {'https://github.com/pallets/werkzeug/blob/7fef41b120327d3912fbe12fb64f1951496fcf3e/src/werkzeug/debug/__init__.py#L168', 'https://nvd.nist.gov/vuln/detail/CVE-2019-14806', 'https://palletsprojects.com/blog/werkzeug-0-15-3-released/', 'https://github.com/pallets/werkzeug/commit/00bc43b1672e662e5e3b8cecd79e67fc968fa246'} | null | {'https://github.com/pallets/werkzeug/commit/00bc43b1672e662e5e3b8cecd79e67fc968fa246'} | {'https://github.com/pallets/werkzeug/commit/00bc43b1672e662e5e3b8cecd79e67fc968fa246'} |
PyPI | PYSEC-2020-272 | 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'} | 2021-12-09T06:34:40.896350Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fxw-76px-3rxv', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} |
PyPI | GHSA-9w4w-cpc8-h2fq | Exposure of Sensitive Information to an Unauthorized Actor in httpie | ### Impact
HTTPie have the practical concept of [sessions](https://httpie.io/docs/cli/sessions), which help users to persistently store some of the state that belongs to the outgoing requests and incoming responses on the disk for further usage. As an example, we can make an authenticated request and save it to a [named session](https://httpie.io/docs/cli/named-sessions) called `api`:
```bash
$ http --session api -a user:pass pie.dev/basic-auth/user/pass
```
```json
{
"authenticated": true,
"user": "user"
}
```
Since we have now saved the authentication data to that session, we won‘t have to enter it again and again on every invocation. We can simply reference the session, and HTTPie will use the saved state directly from it:
```bash
$ http --session api pie.dev/basic-auth/user/pass
```
```json
{
"authenticated": true,
"user": "user"
}
```
One particular use case of these sessions is storing cookies (commonly referred to as a `Cookie Jar`). If a response has a `Set-Cookie`
header, HTTPie will parse it and store the actual cookie in the session. And from that point on, all outgoing requests will attach that cookie (in the form of a `Cookie` header).
This is extremely useful, especially when you are dealing with websites which manage their own state on the client-side through cookies.
```bash
$ http -F --session jar pie.dev/cookies/set/x/y
```
```json
{
"cookies": {
"x": "y"
}
}
```
Before `3.1.0`, HTTPie didn‘t distinguish between cookies and hosts they belonged. This behavior resulted in the exposure of some cookies when there are redirects originating from the actual host to a third party website, e.g:
```bash
$ http -F --session jar pie.dev/redirect-to url==https://httpbin.org/cookies
```
(Pre 3.1.0)
```json
{
"cookies": {
"x": "y"
}
}
```
(Post 3.1.0)
```json
{
"cookies": {}
}
```
This behavior has been corrected in this release (with taking [RFC 6265 — HTTP State Management Mechanism](https://datatracker.ietf.org/doc/html/rfc6265) into the consideration).
A huge credit goes to [@Glyph](https://github.com/glyph) for disclosing the original vulnerability to us (through [huntr.dev](http://huntr.dev/)).
### Patches
We suggest users to upgrade their HTTPie version to `3.1.0` or higher, and run `httpie cli sessions upgrade` command on their sessions.
### For more information
If you have any questions or comments about this advisory:
* Email us: [`security@httpie.io`](mailto:security@httpie.io)
> Please note that this entry is covered by both [CVE-2022-24737](https://www.cvedetails.com/cve/CVE-2022-24737) and [CVE-2022-0430](https://nvd.nist.gov/vuln/detail/CVE-2022-0430).
| {'CVE-2022-24737'} | 2022-03-29T22:31:56.406062Z | 2022-03-07T23:44:28Z | MODERATE | null | {'CWE-200'} | {'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/4QZD2AZOL7XLNZVAV6GDNXYU6MFRU5RS/', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/R5VYSYKEKVZEVEBIWAADGDXG4Y3EWCQ3/', 'https://github.com/httpie/httpie/releases/tag/3.1.0', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24737', 'https://github.com/httpie/httpie', 'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/TXFCHGTW3V32GD6GXXJZE5QAOSDT3RTY/', 'https://github.com/httpie/httpie/security/advisories/GHSA-9w4w-cpc8-h2fq'} | null | {'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b'} | {'https://github.com/httpie/httpie/commit/65ab7d5caaaf2f95e61f9dd65441801c2ddee38b'} |
PyPI | PYSEC-2021-272 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.ExperimentalDatasetToTFRecord` and `tf.raw_ops.DatasetToTFRecord` can trigger heap buffer overflow and segmentation fault. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc#L93-L102) assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types. We have patched the issue in GitHub commit e0b6e58c328059829c3eb968136f17aa72b6c876. 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-2021-37650', 'GHSA-f8h4-7rgh-q2gm'} | 2021-08-27T03:22:43.967494Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f8h4-7rgh-q2gm', 'https://github.com/tensorflow/tensorflow/commit/e0b6e58c328059829c3eb968136f17aa72b6c876'} | null | {'https://github.com/tensorflow/tensorflow/commit/e0b6e58c328059829c3eb968136f17aa72b6c876'} | {'https://github.com/tensorflow/tensorflow/commit/e0b6e58c328059829c3eb968136f17aa72b6c876'} |
PyPI | PYSEC-2021-788 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'CVE-2021-37677', 'GHSA-qfpc-5pjr-mh26'} | 2021-12-09T06:35:39.087428Z | 2021-08-12T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qfpc-5pjr-mh26', 'https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764'} | null | {'https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764'} | {'https://github.com/tensorflow/tensorflow/commit/da857cfa0fde8f79ad0afdbc94e88b5d4bbec764'} |
PyPI | PYSEC-2021-622 | null | TensorFlow is an open source platform for machine learning. In affected versions the code behind `tf.function` API can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive. This occurs due to using a non-reentrant `Lock` Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'GHSA-h67m-xg8f-fxcf', 'CVE-2021-41213'} | 2021-12-09T06:35:09.356832Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf', 'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7'} | null | {'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7'} | {'https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7'} |
PyPI | PYSEC-2021-767 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToSparse`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/ragged_tensor_to_sparse_kernel.cc#L30) has an incomplete validation of the splits values: it does not check that they are in increasing order. We have patched the issue in GitHub commit 1071f554dbd09f7e101324d366eec5f4fe5a3ece. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-4xfp-4pfp-89wg', 'CVE-2021-37656'} | 2021-12-09T06:35:37.172867Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4xfp-4pfp-89wg'} | null | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece'} | {'https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece'} |
PyPI | PYSEC-2015-27 | null | The editor in IPython Notebook before 3.2.2 and Jupyter Notebook 4.0.x before 4.0.5 allows remote attackers to execute arbitrary JavaScript code via a crafted file, which triggers a redirect to files/, related to MIME types. | {'CVE-2015-7337'} | 2021-07-15T02:22:16.210618Z | 2015-09-29T19:59:00Z | null | null | null | {'http://seclists.org/oss-sec/2015/q3/558', 'http://seclists.org/oss-sec/2015/q3/634', 'http://lists.fedoraproject.org/pipermail/package-announce/2015-September/167670.html', 'https://github.com/ipython/ipython/commit/0a8096adf165e2465550bd5893d7e352544e5967', 'https://bugzilla.redhat.com/show_bug.cgi?id=1264067', 'https://github.com/jupyter/notebook/commit/9e63dd89b603dfbe3a7e774d8a962ee0fa30c0b5', 'https://security.gentoo.org/glsa/201512-02'} | null | {'https://github.com/jupyter/notebook/commit/9e63dd89b603dfbe3a7e774d8a962ee0fa30c0b5', 'https://github.com/ipython/ipython/commit/0a8096adf165e2465550bd5893d7e352544e5967'} | {'https://github.com/ipython/ipython/commit/0a8096adf165e2465550bd5893d7e352544e5967', 'https://github.com/jupyter/notebook/commit/9e63dd89b603dfbe3a7e774d8a962ee0fa30c0b5'} |
PyPI | GHSA-5hj3-vjjf-f5m7 | Heap OOB in `SdcaOptimizerV2` | ### Impact
An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `tf.raw_ops.SdcaOptimizerV2`:
```python
import tensorflow as tf
tf.raw_ops.SdcaOptimizerV2(
sparse_example_indices=[[1]],
sparse_feature_indices=[[1]],
sparse_feature_values=[[1.0,2.0]],
dense_features=[[1.0]],
example_weights=[1.0],
example_labels=[],
sparse_indices=[1],
sparse_weights=[1.0],
dense_weights=[[1.0]],
example_state_data=[[100.0,100.0,100.0,100.0]],
loss_type='logistic_loss',
l1=100.0,
l2=100.0,
num_loss_partitions=1,
num_inner_iterations=1,
adaptive=True)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/sdca_internal.cc#L320-L353) does not check that the length of `example_labels` is the same as the number of examples.
### Patches
We have patched the issue in GitHub commit [a4e138660270e7599793fa438cd7b2fc2ce215a6](https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### 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-2021-37672'} | 2022-03-03T05:13:53.533223Z | 2021-08-25T14:41:39Z | MODERATE | null | {'CWE-125'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37672', 'https://github.com/tensorflow/tensorflow', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-5hj3-vjjf-f5m7', 'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} | null | {'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} | {'https://github.com/tensorflow/tensorflow/commit/a4e138660270e7599793fa438cd7b2fc2ce215a6'} |
PyPI | PYSEC-2020-337 | null | In affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0. | {'CVE-2020-26271', 'GHSA-q263-fvxm-m5mw'} | 2021-12-09T06:35:16.854014Z | 2020-12-10T22:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q263-fvxm-m5mw', 'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b'} | null | {'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b'} | {'https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b'} |
PyPI | PYSEC-2019-234 | null | In TensorFlow before 1.15, a heap buffer overflow in UnsortedSegmentSum can be produced when the Index template argument is int32. In this case data_size and num_segments fields are truncated from int64 to int32 and can produce negative numbers, resulting in accessing out of bounds heap memory. This is unlikely to be exploitable and was detected and fixed internally in TensorFlow 1.15 and 2.0. | {'CVE-2019-16778', 'GHSA-844w-j86r-4x2j'} | 2021-12-09T06:35:11.891064Z | 2019-12-16T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892', 'https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2019-002.md', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j'} | null | {'https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892'} | {'https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892'} |
PyPI | PYSEC-2022-105 | null | Tensorflow is an Open Source Machine Learning Framework. The implementation of `Dequantize` does not fully validate the value of `axis` and can result in heap OOB accesses. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked and this results in reading past the end of the array containing the dimensions of the input tensor. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-23hm-7w47-xw72', 'CVE-2022-21726'} | 2022-03-09T00:18:23.133344Z | 2022-02-03T11:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72', 'https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153', 'https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943'} | null | {'https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943'} | {'https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943'} |
PyPI | PYSEC-2021-419 | null | TensorFlow is an open source platform for machine learning. In affected versions the `ImmutableConst` operation in TensorFlow can be tricked into reading arbitrary memory contents. This is because the `tstring` TensorFlow string class has a special case for memory mapped strings but the operation itself does not offer any support for this datatype. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41227', 'GHSA-j8c8-67vp-6mx7'} | 2021-11-13T06:52:46.221231Z | 2021-11-05T23:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585', 'https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j8c8-67vp-6mx7'} | null | {'https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b', 'https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585'} | {'https://github.com/tensorflow/tensorflow/commit/3712a2d3455e6ccb924daa5724a3652a86f6b585', 'https://github.com/tensorflow/tensorflow/commit/1cb6bb6c2a6019417c9adaf9e6843ba75ee2580b'} |
PyPI | PYSEC-2021-675 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-x83m-p7pv-ch8v', 'CVE-2021-29549'} | 2021-12-09T06:35:23.303837Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x83m-p7pv-ch8v', 'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16'} | null | {'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16'} | {'https://github.com/tensorflow/tensorflow/commit/744009c9e5cc5d0447f0dc39d055f917e1fd9e16'} |
PyPI | PYSEC-2021-225 | null | TensorFlow is an end-to-end open source platform for machine learning. The optimized implementation of the `TransposeConv` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L5221-L5222). An attacker can craft a model such that `stride_{h,w}` values are 0. Code calling this function must validate these 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. | {'GHSA-vfr4-x8j2-3rf9', 'CVE-2021-29588'} | 2021-08-27T03:22:37.053061Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-vfr4-x8j2-3rf9'} | null | {'https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d'} | {'https://github.com/tensorflow/tensorflow/commit/801c1c6be5324219689c98e1bd3e0ca365ee834d'} |
PyPI | GHSA-g2xc-35jw-c63p | HTTP Request Smuggling: Invalid Transfer-Encoding in Waitress | ### Impact
Waitress would parse the `Transfer-Encoding` header and only look for a single string value, if that value was not `chunked` it would fall through and use the `Content-Length` header instead.
According to the HTTP standard `Transfer-Encoding` should be a comma separated list, with the inner-most encoding first, followed by any further transfer codings, ending with `chunked`.
Requests sent with:
```
Transfer-Encoding: gzip, chunked
```
Would incorrectly get ignored, and the request would use a `Content-Length` header instead to determine the body size of the HTTP message.
This could allow for Waitress to treat a single request as multiple requests in the case of HTTP pipelining.
### Patches
This issue is fixed in Waitress 1.4.0. This brings a range of changes to harden Waitress against potential HTTP request confusions, and may change the behaviour of Waitress behind non-conformist proxies.
Waitress will now return a 501 Not Implemented error if the `Transfer-Encoding` is not `chunked` or contains multiple elements. Waitress does not support any transfer codings such as `gzip` or `deflate`.
The Pylons Project recommends upgrading as soon as possible, while validating that the changes in Waitress don't cause any changes in behavior.
### Workarounds
Various reverse proxies may have protections against sending potentially bad HTTP requests to the backend, and or hardening against potential issues like this. If the reverse proxy doesn't use HTTP/1.1 for connecting to the backend issues are also somewhat mitigated, as HTTP pipelining does not exist in HTTP/1.0 and Waitress will close the connection after every single request (unless the Keep Alive header is explicitly sent... so this is not a fool proof security method).
### Issues/more security issues:
* open an issue at https://github.com/Pylons/waitress/issues (if not sensitive or security related)
* email the Pylons Security mailing list: pylons-project-security@googlegroups.com (if security related) | {'CVE-2019-16786'} | 2022-04-25T23:17:07.559471Z | 2019-12-20T23:04:18Z | HIGH | null | {'CWE-444'} | {'https://github.com/Pylons/waitress', 'https://nvd.nist.gov/vuln/detail/CVE-2019-16786', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/LYEOTGWJZVKPRXX2HBNVIYWCX73QYPM5/', 'https://github.com/Pylons/waitress/commit/f11093a6b3240fc26830b6111e826128af7771c3', 'https://access.redhat.com/errata/RHSA-2020:0720', 'https://www.oracle.com/security-alerts/cpuapr2022.html', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/GVDHR2DNKCNQ7YQXISJ45NT4IQDX3LJ7/', 'https://github.com/Pylons/waitress/security/advisories/GHSA-g2xc-35jw-c63p', 'https://docs.pylonsproject.org/projects/waitress/en/latest/#security-fixes'} | null | {'https://github.com/Pylons/waitress/commit/f11093a6b3240fc26830b6111e826128af7771c3'} | {'https://github.com/Pylons/waitress/commit/f11093a6b3240fc26830b6111e826128af7771c3'} |
PyPI | PYSEC-2021-730 | null | TensorFlow is an end-to-end open source platform for machine learning. The TFLite implementation of hashtable lookup is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/hashtable_lookup.cc#L114-L115) An attacker can craft a model such that `values`'s first dimension would be 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-8rm6-75mf-7r7r', 'CVE-2021-29604'} | 2021-12-09T06:35:32.707618Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8rm6-75mf-7r7r', 'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2'} | null | {'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2'} | {'https://github.com/tensorflow/tensorflow/commit/5117e0851348065ed59c991562c0ec80d9193db2'} |
PyPI | GHSA-9j59-75qj-795w | Path traversal in Pillow | If the path to the temporary directory on Linux or macOS contained a space, this would break removal of the temporary image file after im.show() (and related actions), and potentially remove an unrelated file. This been present since PIL. | {'CVE-2022-24303'} | 2022-04-07T15:17:03.062945Z | 2022-03-11T23:10:32Z | CRITICAL | null | null | {'https://github.com/python-pillow/Pillow/pull/3450', 'https://pillow.readthedocs.io/en/stable/releasenotes/9.0.1.html#security', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/XR6UP2XONXOVXI4446VY72R63YRO2YTP/', 'https://github.com/python-pillow/Pillow/commit/427221ef5f19157001bf8b1ad7cfe0b905ca8c26', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/W4ZUXPKEX72O3E5IHBPVY5ZCPMJ4GHHV/', 'https://nvd.nist.gov/vuln/detail/CVE-2022-24303', 'https://github.com/python-pillow/Pillow'} | null | {'https://github.com/python-pillow/Pillow/commit/427221ef5f19157001bf8b1ad7cfe0b905ca8c26'} | {'https://github.com/python-pillow/Pillow/commit/427221ef5f19157001bf8b1ad7cfe0b905ca8c26'} |
PyPI | GHSA-4v5p-v5h9-6xjx | `CHECK`-failures in Tensorflow | ### Impact
An attacker can trigger denial of service via assertion failure by altering a `SavedModel` on disk such that `AttrDef`s of some operation are duplicated.
### Patches
We have patched the issue in GitHub commit [c2b31ff2d3151acb230edc3f5b1832d2c713a9e0](https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0).
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-2022-23565'} | 2022-03-03T05:13:16.682166Z | 2022-02-09T23:49:01Z | MODERATE | null | {'CWE-617'} | {'https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4v5p-v5h9-6xjx', 'https://nvd.nist.gov/vuln/detail/CVE-2022-23565', 'https://github.com/tensorflow/tensorflow/'} | null | {'https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0'} | {'https://github.com/tensorflow/tensorflow/commit/c2b31ff2d3151acb230edc3f5b1832d2c713a9e0'} |
PyPI | PYSEC-2021-674 | null | TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc) does not validate all constraints specified in the op's contract(https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedBatchNormWithGlobalNormalization). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29548', 'GHSA-p45v-v4pw-77jr'} | 2021-12-09T06:35:23.143235Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p45v-v4pw-77jr'} | null | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} | {'https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b'} |
PyPI | PYSEC-2021-224 | null | TensorFlow is an end-to-end open source platform for machine learning. The `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division(https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be zero. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'CVE-2021-29587', 'GHSA-j7rm-8ww4-xx2g'} | 2021-08-27T03:22:36.876924Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j7rm-8ww4-xx2g', 'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7'} | null | {'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7'} | {'https://github.com/tensorflow/tensorflow/commit/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7'} |
PyPI | GHSA-qr82-2c78-4m8h | Reference binding to nullptr in map operations | ### Impact
An attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.Map*` and `tf.raw_ops.OrderedMap*` operations:
```python
import tensorflow as tf
tf.raw_ops.MapPeek(
key=tf.constant([8],dtype=tf.int64),
indices=[],
dtypes=[tf.int32],
capacity=8,
memory_limit=128)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L222-L248) has a check in place to ensure that `indices` is in ascending order, but does not check that `indices` is not empty.
### Patches
We have patched the issue in GitHub commit [532f5c5a547126c634fefd43bbad1dc6417678ac](https://github.com/tensorflow/tensorflow/commit/532f5c5a547126c634fefd43bbad1dc6417678ac).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### 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-2021-37671'} | 2022-03-03T05:13:59.171617Z | 2021-08-25T14:41:42Z | HIGH | null | {'CWE-824'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qr82-2c78-4m8h', 'https://github.com/tensorflow/tensorflow/commit/532f5c5a547126c634fefd43bbad1dc6417678ac', 'https://nvd.nist.gov/vuln/detail/CVE-2021-37671', 'https://github.com/tensorflow/tensorflow'} | null | {'https://github.com/tensorflow/tensorflow/commit/532f5c5a547126c634fefd43bbad1dc6417678ac'} | {'https://github.com/tensorflow/tensorflow/commit/532f5c5a547126c634fefd43bbad1dc6417678ac'} |
PyPI | PYSEC-2021-731 | null | TensorFlow is an end-to-end open source platform for machine learning. The TFLite code for allocating `TFLiteIntArray`s is vulnerable to an integer overflow issue(https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L24-L27). An attacker can craft a model such that the `size` multiplier is so large that the return value overflows the `int` datatype and becomes negative. In turn, this results in invalid value being given to `malloc`(https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L47-L52). In this case, `ret->size` would dereference an invalid pointer. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range. | {'GHSA-jf7h-7m85-w2v2', 'CVE-2021-29605'} | 2021-12-09T06:35:32.887706Z | 2021-05-14T20:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jf7h-7m85-w2v2'} | null | {'https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5'} | {'https://github.com/tensorflow/tensorflow/commit/7c8cc4ec69cd348e44ad6a2699057ca88faad3e5'} |
PyPI | GHSA-hwvq-6gjx-j797 | Special Element Injection in notebook | ### Impact
Untrusted notebook can execute code on load. This is a remote code execution, but requires user action to open a notebook.
### Patches
5.7.11, 6.4.1
### References
[OWASP Page on Injection Prevention](https://cheatsheetseries.owasp.org/cheatsheets/Injection_Prevention_Cheat_Sheet.html#injection-prevention-rules)
### For more information
If you have any questions or comments about this advisory, or vulnerabilities to report, please email our security list security@ipython.org.
Credit: Guillaume Jeanne from Google
### Example:
A notebook with the following content in a cell and it would display an alert when opened for the first time in Notebook (in an untrusted state):
```
{ "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<select><iframe></select><img src=x: onerror=alert('xss')>\n"], "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "" ] }
```` | {'CVE-2021-32798'} | 2022-03-03T05:13:42.124259Z | 2021-08-23T19:40:38Z | CRITICAL | null | {'CWE-79'} | {'https://github.com/jupyter/notebook/security/advisories/GHSA-hwvq-6gjx-j797', 'https://github.com/jupyter/notebook/commit/79fc76e890a8ec42f73a3d009e44ef84c14ef0d5', 'https://nvd.nist.gov/vuln/detail/CVE-2021-32798'} | null | {'https://github.com/jupyter/notebook/commit/79fc76e890a8ec42f73a3d009e44ef84c14ef0d5'} | {'https://github.com/jupyter/notebook/commit/79fc76e890a8ec42f73a3d009e44ef84c14ef0d5'} |
PyPI | GHSA-6gv8-p3vj-pxvr | Null pointer dereference in `UncompressElement` | ### Impact
The code for `tf.raw_ops.UncompressElement` can be made to trigger a null pointer dereference:
```python
import tensorflow as tf
data = tf.data.Dataset.from_tensors([0.0])
tf.raw_ops.UncompressElement(
compressed=tf.data.experimental.to_variant(data),
output_types=[tf.int64],
output_shapes=[2])
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/compression_ops.cc#L50-L53) obtains a pointer to a `CompressedElement` from a `Variant` tensor and then proceeds to dereference it for decompressing. There is no check that the `Variant` tensor contained a `CompressedElement`, so the pointer is actually `nullptr`.
### Patches
We have patched the issue in GitHub commit [7bdf50bb4f5c54a4997c379092888546c97c3ebd](https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd).
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360. | {'CVE-2021-37649'} | 2022-03-03T05:13:59.627726Z | 2021-08-25T14:43:27Z | HIGH | null | {'CWE-476'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-37649', 'https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6gv8-p3vj-pxvr'} | null | {'https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd'} | {'https://github.com/tensorflow/tensorflow/commit/7bdf50bb4f5c54a4997c379092888546c97c3ebd'} |
PyPI | GHSA-x8h6-xgqx-jqgp | Undefined behavior and `CHECK`-fail in `FractionalMaxPoolGrad` | ### Impact
The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty:
```python
import tensorflow as tf
orig_input = tf.constant([2, 3], shape=[1, 1, 1, 2], dtype=tf.int64)
orig_output = tf.constant([], dtype=tf.int64)
out_backprop = tf.zeros([2, 3, 6, 6], dtype=tf.int64)
row_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64)
col_pooling_sequence = tf.constant([0], shape=[1], dtype=tf.int64)
tf.raw_ops.FractionalMaxPoolGrad(
orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop,
row_pooling_sequence=row_pooling_sequence,
col_pooling_sequence=col_pooling_sequence, overlapping=False)
```
The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process
```python
import tensorflow as tf
orig_input = tf.constant([1], shape=[1], dtype=tf.int64)
orig_output = tf.constant([1], shape=[1], dtype=tf.int64)
out_backprop = tf.constant([1, 1], shape=[2, 1, 1, 1], dtype=tf.int64)
row_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
col_pooling_sequence = tf.constant([1], shape=[1], dtype=tf.int64)
tf.raw_ops.FractionalMaxPoolGrad(
orig_input=orig_input, orig_output=orig_output, out_backprop=out_backprop,
row_pooling_sequence=row_pooling_sequence,
col_pooling_sequence=col_pooling_sequence, overlapping=False)
```
The [implementation](https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues.
### Patches
We have patched the issue in GitHub commit [32fdcbff9d06d010d908fcc4bd4b36eb3ce15925](https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925).
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-2021-29580'} | 2022-03-03T05:13:55.417003Z | 2021-05-21T14:26:26Z | LOW | null | {'CWE-908'} | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29580', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp'} | null | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925'} | {'https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925'} |
PyPI | PYSEC-2022-104 | null | Tensorflow is an Open Source Machine Learning Framework. The estimator for the cost of some convolution operations can be made to execute a division by 0. The function fails to check that the stride argument is strictly positive. Hence, the fix is to add a check for the stride argument to ensure it is valid. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. | {'GHSA-v3f7-j968-4h5f', 'CVE-2022-21725'} | 2022-03-09T00:18:22.994300Z | 2022-02-03T13:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-v3f7-j968-4h5f', 'https://github.com/tensorflow/tensorflow/blob/ffa202a17ab7a4a10182b746d230ea66f021fe16/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L189-L198', 'https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a'} | null | {'https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a'} | {'https://github.com/tensorflow/tensorflow/commit/3218043d6d3a019756607643cf65574fbfef5d7a'} |
PyPI | PYSEC-2021-418 | null | TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseBinCount` is vulnerable to a heap OOB access. This is because of missing validation between the elements of the `values` argument and the shape of the sparse output. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range. | {'CVE-2021-41226', 'GHSA-374m-jm66-3vj8'} | 2021-11-13T06:52:46.070716Z | 2021-11-05T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/f410212e373eb2aec4c9e60bf3702eba99a38aba', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-374m-jm66-3vj8'} | null | {'https://github.com/tensorflow/tensorflow/commit/f410212e373eb2aec4c9e60bf3702eba99a38aba'} | {'https://github.com/tensorflow/tensorflow/commit/f410212e373eb2aec4c9e60bf3702eba99a38aba'} |
PyPI | GHSA-mh74-4m5g-fcjx | Malicious users could control the content of invitation emails | ### Impact
A malicious user could abuse Sydent to send out arbitrary emails from the Sydent email address. This could be used to construct plausible phishing emails, for example.
### Patches
Fixed in 4469d1d, 6b405a8, 65a6e91.
Note that these patches include changes to the *default* email templates. If these templates have been locally modified, they must also be updated.
### For more information
If you have any questions or comments about this advisory, email us at security@matrix.org. | {'CVE-2021-29432'} | 2022-03-03T05:12:55.741149Z | 2021-04-19T14:54:24Z | MODERATE | null | {'CWE-20'} | {'https://nvd.nist.gov/vuln/detail/CVE-2021-29432', 'https://github.com/matrix-org/sydent/security/advisories/GHSA-mh74-4m5g-fcjx', 'https://github.com/matrix-org/sydent/commit/4469d1d42b2b1612b70638224c07e19623039c42', 'https://github.com/matrix-org/sydent/releases/tag/v2.3.0', 'https://pypi.org/project/matrix-sydent/'} | null | {'https://github.com/matrix-org/sydent/commit/4469d1d42b2b1612b70638224c07e19623039c42'} | {'https://github.com/matrix-org/sydent/commit/4469d1d42b2b1612b70638224c07e19623039c42'} |
PyPI | GHSA-cfx7-2xpc-8w4h | Division by zero in TFLite's implementation of `BatchToSpaceNd` | ### Impact
The implementation of the `BatchToSpaceNd` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/b5ed552fe55895aee8bd8b191f744a069957d18d/tensorflow/lite/kernels/batch_to_space_nd.cc#L81-L82):
```cc
TF_LITE_ENSURE_EQ(context, output_batch_size % block_shape[dim], 0);
output_batch_size = output_batch_size / block_shape[dim];
```
An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` is 0.
### Patches
We have patched the issue in GitHub commit [2c74674348a4708ced58ad6eb1b23354df8ee044](https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044).
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-2021-29593'} | 2022-03-03T05:12:44.546198Z | 2021-05-21T14:27:01Z | LOW | null | {'CWE-369'} | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cfx7-2xpc-8w4h', 'https://nvd.nist.gov/vuln/detail/CVE-2021-29593', 'https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044'} | null | {'https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044'} | {'https://github.com/tensorflow/tensorflow/commit/2c74674348a4708ced58ad6eb1b23354df8ee044'} |
PyPI | PYSEC-2020-273 | null | In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a `reinterpret_cast` Since the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1. | {'CVE-2020-15193', 'GHSA-rjjg-hgv6-h69v'} | 2021-12-09T06:34:40.985674Z | 2020-09-25T19:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v', 'http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html', 'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8', 'https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1'} | null | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} | {'https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8'} |
PyPI | GHSA-mj63-64x7-57xf | Path traversal in impacket | Multiple path traversal vulnerabilities exist in smbserver.py in Impacket before 0.9.23. An attacker that connects to a running smbserver instance can list and write to arbitrary files via ../ directory traversal. This could potentially be abused to achieve arbitrary code execution by replacing /etc/shadow or an SSH authorized key. | {'CVE-2021-31800'} | 2022-03-03T05:12:59.880283Z | 2021-06-18T18:43:14Z | CRITICAL | null | {'CWE-22'} | {'https://github.com/SecureAuthCorp/impacket/releases', 'https://github.com/SecureAuthCorp/impacket/blob/cb6d43a677c338db930bc4e9161620832c1ec624/impacket/smbserver.py#L2958', 'https://nvd.nist.gov/vuln/detail/CVE-2021-31800', 'https://github.com/SecureAuthCorp/impacket/blob/cb6d43a677c338db930bc4e9161620832c1ec624/impacket/smbserver.py#L2008', 'https://github.com/SecureAuthCorp/impacket/commit/99bd29e3995c254e2d6f6c2e3454e4271665955a', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/IPXDPWCAPVX3UWYZ3N2T5OLBSBBUHJP6/', 'https://github.com/SecureAuthCorp/impacket/blob/cb6d43a677c338db930bc4e9161620832c1ec624/impacket/smbserver.py#L3485', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/KRV2C5DATXBHG6TF6CEEX54KZ75THQS3/', 'https://github.com/SecureAuthCorp/impacket/blob/cb6d43a677c338db930bc4e9161620832c1ec624/impacket/smbserver.py#L876', 'https://github.com/SecureAuthCorp/impacket/releases/tag/impacket_0_9_23', 'https://github.com/SecureAuthCorp/impacket/commit/49c643bf66620646884ed141c94e5fdd85bcdd2f', 'https://lists.fedoraproject.org/archives/list/package-announce@lists.fedoraproject.org/message/UF56LYB27LHEIFJTFHU3M75NMNNK2SCG/'} | null | {'https://github.com/SecureAuthCorp/impacket/commit/49c643bf66620646884ed141c94e5fdd85bcdd2f', 'https://github.com/SecureAuthCorp/impacket/commit/99bd29e3995c254e2d6f6c2e3454e4271665955a'} | {'https://github.com/SecureAuthCorp/impacket/commit/99bd29e3995c254e2d6f6c2e3454e4271665955a', 'https://github.com/SecureAuthCorp/impacket/commit/49c643bf66620646884ed141c94e5fdd85bcdd2f'} |
PyPI | PYSEC-2021-273 | null | TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. | {'GHSA-hpv4-7p9c-mvfr', 'CVE-2021-37651'} | 2021-08-27T03:22:44.051773Z | 2021-08-12T21:15:00Z | null | null | null | {'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30', 'https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hpv4-7p9c-mvfr'} | null | {'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30'} | {'https://github.com/tensorflow/tensorflow/commit/0f931751fb20f565c4e94aa6df58d54a003cdb30'} |
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