CVE-2019-16778
TensorFlow UnsortedSegmentSum Heap Buffer Overflow
Description
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.
INFO
Published Date :
Dec. 16, 2019, 9:15 p.m.
Last Modified :
Oct. 29, 2021, 3:03 p.m.
Source :
[email protected]
Remotely Exploitable :
Yes !
Impact Score :
5.9
Exploitability Score :
3.9
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-2019-16778.
| URL | Resource |
|---|---|
| https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2019-002.md | Third Party Advisory |
| https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892 | Patch |
| https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j | 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-2019-16778 vulnerability anywhere in the article.
The following table lists the changes that have been made to the
CVE-2019-16778 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.
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CVE Modified by [email protected]
May. 14, 2024
Action Type Old Value New Value -
Reanalysis by [email protected]
Oct. 29, 2021
Action Type Old Value New Value Changed Reference Type https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j Third Party Advisory https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j Patch, Third Party Advisory Removed CWE NIST CWE-787 Added CWE NIST CWE-681 -
Initial Analysis by [email protected]
Dec. 19, 2019
Action Type Old Value New Value Added CVSS V2 NIST (AV:N/AC:L/Au:N/C:P/I:P/A:P) Added CVSS V3.1 NIST AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H Changed Reference Type https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2019-002.md No Types Assigned https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2019-002.md Third Party Advisory Changed Reference Type https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892 No Types Assigned https://github.com/tensorflow/tensorflow/commit/db4f9717c41bccc3ce10099ab61996b246099892 Patch Changed Reference Type https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j No Types Assigned https://github.com/tensorflow/tensorflow/security/advisories/GHSA-844w-j86r-4x2j Third Party Advisory Added CWE NIST CWE-787 Added CPE Configuration OR *cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:* versions from (including) 1.0.0 up to (excluding) 1.15.0
CWE - Common Weakness Enumeration
While CVE identifies
specific instances of vulnerabilities, CWE categorizes the common flaws or
weaknesses that can lead to vulnerabilities. CVE-2019-16778 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-2019-16778
weaknesses.
Exploit Prediction
EPSS is a daily estimate of the probability of exploitation activity being observed over the next 30 days.
0.17 }} -0.00%
score
0.53415
percentile