CVE-2021-41205
📋 TL;DR
This CVE describes an out-of-bounds read vulnerability in TensorFlow's QuantizeAndDequantizeV* operations that could allow attackers to read sensitive memory contents. It affects TensorFlow users running affected versions who process untrusted models or inputs. The vulnerability could lead to information disclosure or potentially facilitate further attacks.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Memory disclosure leading to sensitive data exposure, potential information leak that could facilitate other attacks, or denial of service through application crashes.
Likely Case
Information disclosure from heap memory, potentially exposing model parameters, training data remnants, or other sensitive information stored in adjacent memory.
If Mitigated
Limited impact with proper input validation and sandboxing; potential denial of service if application crashes.
🎯 Exploit Status
Exploitation requires control over model inputs or operations; no public exploits known as of advisory publication.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.4.4, 2.5.2, 2.6.1, or 2.7.0
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f
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 TensorFlow services and applications. 4. Test functionality with existing models.
🔧 Temporary Workarounds
Input Validation and Sanitization
allValidate and sanitize all model inputs to prevent malicious QuantizeAndDequantizeV* operations
Model Source Verification
allOnly load models from trusted sources and verify model integrity before processing
🧯 If You Can't Patch
- Implement strict input validation for all TensorFlow operations
- Isolate TensorFlow processing in sandboxed environments with limited memory access
🔍 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 2.4.4, 2.5.2, 2.6.1, or 2.7.0+: python -c 'import tensorflow as tf; print(tf.__version__)'📡 Detection & Monitoring
Log Indicators:
- TensorFlow segmentation faults
- Memory access violation errors
- Unexpected application crashes during model processing
Network Indicators:
- Unusual model uploads to ML services
- Suspicious input patterns to QuantizeAndDequantize operations
SIEM Query:
source="tensorflow" AND (error="segmentation fault" OR error="memory violation" OR error="out of bounds")🔗 References
- https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f
- https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f
