CVE-2021-29517
TensorFlow Division by Zero Vulnerability (Input Validation)
Description
TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
INFO
Published Date :
May 14, 2021, 8:15 p.m.
Last Modified :
May 20, 2021, 4:27 p.m.
Source :
[email protected]
Remotely Exploitable :
No
Impact Score :
3.6
Exploitability Score :
1.8
References to Advisories, Solutions, and Tools
Here, you will find a curated list of external links that provide in-depth
information, practical solutions, and valuable tools related to
CVE-2021-29517.
| URL | Resource |
|---|---|
| https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f | Patch Third Party Advisory |
| https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv | Exploit Patch Third Party Advisory |
We scan GitHub repositories to detect new proof-of-concept exploits. Following list is a collection of public exploits and proof-of-concepts, which have been published on GitHub (sorted by the most recently updated).
Results are limited to the first 15 repositories due to potential performance issues.
The following list is the news that have been mention
CVE-2021-29517 vulnerability anywhere in the article.
The following table lists the changes that have been made to the
CVE-2021-29517 vulnerability over time.
Vulnerability history details can be useful for understanding the evolution of a vulnerability, and for identifying the most recent changes that may impact the vulnerability's severity, exploitability, or other characteristics.
-
CVE Modified by [email protected]
May. 14, 2024
Action Type Old Value New Value -
Initial Analysis by [email protected]
May. 20, 2021
Action Type Old Value New Value Added CVSS V2 NIST (AV:L/AC:L/Au:N/C:N/I:N/A:P) Added CVSS V3.1 NIST AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H Changed Reference Type https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f No Types Assigned https://github.com/tensorflow/tensorflow/commit/799f835a3dfa00a4d852defa29b15841eea9d64f Patch, Third Party Advisory Changed Reference Type https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv No Types Assigned https://github.com/tensorflow/tensorflow/security/advisories/GHSA-772p-x54p-hjrv Exploit, Patch, Third Party Advisory Added CPE Configuration OR *cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:* versions up to (excluding) 2.1.4 *cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:* versions from (including) 2.2.0 up to (excluding) 2.2.3 *cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:* versions from (including) 2.3.0 up to (excluding) 2.3.3 *cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:* versions from (including) 2.4.0 up to (excluding) 2.4.2
CWE - Common Weakness Enumeration
While CVE identifies
specific instances of vulnerabilities, CWE categorizes the common flaws or
weaknesses that can lead to vulnerabilities. CVE-2021-29517 is
associated with the following CWEs:
Common Attack Pattern Enumeration and Classification (CAPEC)
Common Attack Pattern Enumeration and Classification
(CAPEC)
stores attack patterns, which are descriptions of the common attributes and
approaches employed by adversaries to exploit the CVE-2021-29517
weaknesses.
Exploit Prediction
EPSS is a daily estimate of the probability of exploitation activity being observed over the next 30 days.
0.04 }} 0.00%
score
0.10669
percentile