CVE-2021-41212
📋 TL;DR
This vulnerability in TensorFlow allows attackers to trigger an out-of-bounds read in the tf.ragged.cross function, potentially leading to memory disclosure or application crashes. It affects TensorFlow versions before the patched releases, impacting any system using vulnerable TensorFlow code for machine learning tasks.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Memory corruption leading to remote code execution or sensitive data disclosure from TensorFlow process memory
Likely Case
Application crash (denial of service) or limited memory disclosure
If Mitigated
No impact if patched or workarounds applied; isolated crashes if unpatched but with proper sandboxing
🎯 Exploit Status
Exploitation requires crafting specific inputs to tf.ragged.cross; no public exploits known
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.7.0, 2.6.1, 2.5.2, or 2.4.4
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g
Restart Required: No
Instructions:
1. Update TensorFlow using pip: pip install --upgrade tensorflow==2.7.0 (or appropriate patched version). 2. Verify installation with: python -c 'import tensorflow as tf; print(tf.__version__)'
🔧 Temporary Workarounds
Avoid tf.ragged.cross usage
allTemporarily disable or avoid using the vulnerable tf.ragged.cross function
# Review code for tf.ragged.cross usage and replace with alternative functions
🧯 If You Can't Patch
- Implement input validation/sanitization for tf.ragged.cross inputs
- Run TensorFlow in sandboxed/isolated environments to limit potential impact
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version and if tf.ragged.cross is used in your codebaseCheck Version:
python -c 'import tensorflow as tf; print(tf.__version__)'Verify Fix Applied:
Verify TensorFlow version is 2.7.0, 2.6.1, 2.5.2, or 2.4.4+ and test tf.ragged.cross functionality📡 Detection & Monitoring
Log Indicators:
- TensorFlow segmentation faults
- Memory access violation errors in application logs
Network Indicators:
- Unusual ML model inference requests targeting tf.ragged.cross endpoints
SIEM Query:
source="tensorflow" AND ("segmentation fault" OR "memory violation" OR "out of bounds")🔗 References
- https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g
- https://github.com/tensorflow/tensorflow/commit/fa6b7782fbb14aa08d767bc799c531f5e1fb3bb8
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fr77-rrx3-cp7g
