CVE-2020-15197
Tensorflow Sparse Tensor Denial of Service Vulnerability
Description
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.
INFO
Published Date :
Sept. 25, 2020, 7:15 p.m.
Last Modified :
Aug. 17, 2021, 1:21 p.m.
Source :
[email protected]
Remotely Exploitable :
Yes !
Impact Score :
4.0
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-2020-15197
.
URL | Resource |
---|---|
https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02 | Patch Third Party Advisory |
https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1 | Third Party Advisory |
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qc53-44cj-vfvx | Exploit 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-2020-15197
vulnerability anywhere in the article.
The following table lists the changes that have been made to the
CVE-2020-15197
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 -
CPE Deprecation Remap by [email protected]
Aug. 17, 2021
Action Type Old Value New Value Changed CPE Configuration OR *cpe:2.3:a:tensorflow:tensorflow:2.3.0:*:*:*:-:*:*:* OR *cpe:2.3:a:google:tensorflow:2.3.0:*:*:*:-:*:*:* -
Initial Analysis by [email protected]
Oct. 01, 2020
Action Type Old Value New Value Added CVSS V2 NIST (AV:N/AC:M/Au:S/C:N/I:N/A:P) Added CVSS V3.1 NIST AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H Changed Reference Type https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02 No Types Assigned https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02 Patch, Third Party Advisory Changed Reference Type https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1 No Types Assigned https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1 Third Party Advisory Changed Reference Type https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qc53-44cj-vfvx No Types Assigned https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qc53-44cj-vfvx Exploit, Third Party Advisory Added CPE Configuration OR *cpe:2.3:a:tensorflow:tensorflow:2.3.0:*:*:*:-:*:*:*
CWE - Common Weakness Enumeration
While CVE identifies
specific instances of vulnerabilities, CWE categorizes the common flaws or
weaknesses that can lead to vulnerabilities. CVE-2020-15197
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-2020-15197
weaknesses.
Exploit Prediction
EPSS is a daily estimate of the probability of exploitation activity being observed over the next 30 days.
0.18 }} 0.05%
score
0.54514
percentile