CVE-2021-41219
📋 TL;DR
This vulnerability in TensorFlow's sparse matrix multiplication allows attackers to trigger undefined behavior and potential heap out-of-bounds access by providing zero or negative dimensions. This affects all users running vulnerable TensorFlow versions for machine learning workloads. Successful exploitation could lead to crashes, memory corruption, or arbitrary code execution.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Remote code execution leading to complete system compromise, data theft, or denial of service in ML inference services.
Likely Case
Application crashes, denial of service in ML pipelines, or memory corruption leading to unstable behavior.
If Mitigated
Minimal impact if proper input validation and sandboxing are implemented, though crashes may still occur.
🎯 Exploit Status
Exploitation requires control over input dimensions to sparse matrix operations, which may be accessible via user inputs in ML applications.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.7.0, 2.6.1, 2.5.2, 2.4.4
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x
Restart Required: Yes
Instructions:
1. Identify current TensorFlow version. 2. Upgrade to patched version: pip install tensorflow==2.7.0 (or appropriate version). 3. Restart all services using TensorFlow. 4. Test ML applications for compatibility.
🔧 Temporary Workarounds
Input validation wrapper
allAdd validation to reject zero or negative dimensions in sparse matrix operations
Implement input validation in application code before calling sparse matrix multiplication functions
🧯 If You Can't Patch
- Implement strict input validation for all user-provided matrix dimensions
- Isolate TensorFlow processes with sandboxing/containerization to limit potential damage
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: python -c 'import tensorflow as tf; print(tf.__version__)' and compare with affected versionsCheck Version:
python -c 'import tensorflow as tf; print(tf.__version__)'Verify Fix Applied:
Verify version is 2.7.0, 2.6.1, 2.5.2, or 2.4.4 or higher📡 Detection & Monitoring
Log Indicators:
- TensorFlow crashes with segmentation faults
- Memory access violation errors in application logs
- Unexpected process termination during matrix operations
Network Indicators:
- Sudden service unavailability of ML inference endpoints
- Increased error rates in ML API responses
SIEM Query:
source="tensorflow" AND ("segmentation fault" OR "memory violation" OR "heap corruption")🔗 References
- https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x
- https://github.com/tensorflow/tensorflow/commit/e6cf28c72ba2eb949ca950d834dd6d66bb01cfae
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4f99-p9c2-3j8x
