CVE-2020-26269 : Exploit Details and Defense Strategies
Learn about CVE-2020-26269, a vulnerability in TensorFlow's filesystem glob matching implementation allowing a heap out of bounds read. Find out the impact, affected systems, exploitation mechanism, and mitigation steps.
In TensorFlow release candidate versions 2.4.0rc*, the implementation for matching filesystem paths to globbing pattern is vulnerable to an access out of bounds of the array holding the directories. The issue impacts the master branch and release candidates for TensorFlow version 2.4.
Understanding CVE-2020-26269
In this CVE, a vulnerability in TensorFlow's implementation for matching filesystem paths to globbing patterns could lead to a heap out of bounds read.
What is CVE-2020-26269?
The vulnerability in TensorFlow's release candidate versions 2.4.0rc* allows an attacker to access out of bounds of the array holding directories due to unverified invariants and preconditions in the implementation.
The Impact of CVE-2020-26269
The vulnerability could be exploited by an attacker to read sensitive information from the heap memory, potentially leading to information disclosure or further exploitation.
Technical Details of CVE-2020-26269
The technical details of the vulnerability in TensorFlow release candidate versions 2.4.0rc* are as follows:
Vulnerability Description
The vulnerability arises from the general implementation for matching filesystem paths to globbing patterns, allowing access out of bounds of the array holding directories.
Affected Systems and Versions
- Product: TensorFlow
- Vendor: TensorFlow
- Versions Affected: 2.4.0rc*
Exploitation Mechanism
The issue is due to unverified invariants and preconditions in the parallel implementation of GetMatchingPaths, allowing an attacker to exploit the vulnerability.
Mitigation and Prevention
To address CVE-2020-26269 and enhance security:
Immediate Steps to Take
- Update TensorFlow to version 2.4.0 to apply the patch.
- Avoid using release candidate versions in production environments.
Long-Term Security Practices
- Regularly update TensorFlow to the latest stable versions.
- Implement secure coding practices to prevent similar vulnerabilities.
Patching and Updates
- Apply patches and updates provided by TensorFlow to mitigate the vulnerability.