CVE-2022-21732 – tensorflow-gpu

Package

Manager: pip
Name: tensorflow-gpu
Vulnerable Version: >=0 <2.5.3 || >=2.6.0 <2.6.3 || =2.7.0 || >=2.7.0 <2.7.1

Severity

Level: Medium

CVSS v3.1: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L

CVSS v4.0: CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N

EPSS: 0.0022 pctl0.44566

Details

Memory exhaustion in Tensorflow ### Impact The [implementation of `ThreadPoolHandle`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/data/experimental/threadpool_dataset_op.cc#L79-L135) can be used to trigger a denial of service attack by allocating too much memory: ```python import tensorflow as tf y = tf.raw_ops.ThreadPoolHandle(num_threads=0x60000000,display_name='tf') ``` This is because the `num_threads` argument is only checked to not be negative, but there is no upper bound on its value. ### Patches We have patched the issue in GitHub commit [e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e](https://github.com/tensorflow/tensorflow/commit/e3749a6d5d1e8d11806d4a2e9cc3123d1a90b75e). The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team.

Metadata

Created: 2022-02-10T00:20:29Z
Modified: 2024-11-13T22:12:45Z
Source: https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2022/02/GHSA-c582-c96p-r5cq/GHSA-c582-c96p-r5cq.json
CWE IDs: ["CWE-400", "CWE-770"]
Alternative ID: GHSA-c582-c96p-r5cq
Finding: F067
Auto approve: 1