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Security Advisories: MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLTR2ZjItNHhjZy02NWN4
Division by 0 in `Conv2D`
Impact
An attacker can trigger a division by 0 in tf.raw_ops.Conv2D
:
import tensorflow as tf
input = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
filter = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
strides = [1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.Conv2D(input=input, filter=filter, strides=strides, padding=padding)
This is because the implementation does a division by a quantity that is controlled by the caller:
const int64 patch_depth = filter.dim_size(2);
if (in_depth % patch_depth != 0) { ... }
Patches
We have patched the issue in GitHub commit b12aa1d44352de21d1a6faaf04172d8c2508b42b.
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 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.
Permalink: https://github.com/advisories/GHSA-4vf2-4xcg-65cxJSON: https://advisories.ecosyste.ms/api/v1/advisories/MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLTR2ZjItNHhjZy02NWN4
Source: GitHub Advisory Database
Origin: Unspecified
Severity: Low
Classification: General
Published: over 2 years ago
Updated: 10 months ago
CVSS Score: 2.5
CVSS vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Identifiers: GHSA-4vf2-4xcg-65cx, CVE-2021-29526
References:
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4vf2-4xcg-65cx
- https://nvd.nist.gov/vuln/detail/CVE-2021-29526
- https://github.com/tensorflow/tensorflow/commit/b12aa1d44352de21d1a6faaf04172d8c2508b42b
- https://github.com/advisories/GHSA-4vf2-4xcg-65cx
Affected Packages
pypi:tensorflow-gpu
Versions: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3, >= 2.2.0, < 2.2.3, < 2.1.4Fixed in: 2.4.2, 2.3.3, 2.2.3, 2.1.4
pypi:tensorflow-cpu
Versions: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3, >= 2.2.0, < 2.2.3, < 2.1.4Fixed in: 2.4.2, 2.3.3, 2.2.3, 2.1.4
pypi:tensorflow
Versions: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3, >= 2.2.0, < 2.2.3, < 2.1.4Fixed in: 2.4.2, 2.3.3, 2.2.3, 2.1.4