Description
TensorFlow is an open source platform for machine learning. If `Conv2D` is given empty `input` and the `filter` and `padding` sizes are valid, the output is all-zeros. This causes division-by-zero floating point exceptions that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 611d80db29dd7b0cfb755772c69d60ae5bca05f9. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
Understanding This Vulnerability
This Common Vulnerabilities and Exposures (CVE) entry provides detailed information about a security vulnerability that has been publicly disclosed. CVEs are standardized identifiers assigned by MITRE Corporation to track and catalog security vulnerabilities across software and hardware products.
The severity rating (MEDIUM) indicates the potential impact of this vulnerability based on the CVSS (Common Vulnerability Scoring System) framework. Higher severity ratings typically indicate vulnerabilities that could lead to more significant security breaches if exploited. Security teams should prioritize remediation efforts based on severity, exploit availability, and the EPSS (Exploit Prediction Scoring System) score, which predicts the likelihood of exploitation in the wild.
If this vulnerability affects products or systems in your infrastructure, we recommend reviewing the affected products section, checking for available patches or updates from vendors, and implementing recommended workarounds or solutions until a permanent fix is available. Organizations should also monitor security advisories and threat intelligence feeds for updates about active exploitation of this vulnerability.