CVE-2021-37635
📋 TL;DR
This vulnerability in TensorFlow allows attackers to trigger out-of-bounds memory accesses during sparse reduction operations, potentially leading to memory corruption, crashes, or arbitrary code execution. It affects TensorFlow users performing sparse tensor operations with untrusted input. The vulnerability is present in multiple TensorFlow versions.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Remote code execution leading to complete system compromise, data theft, or ransomware deployment.
Likely Case
Application crashes, denial of service, or memory corruption leading to unstable behavior.
If Mitigated
Limited impact with proper input validation and sandboxing; crashes contained within application boundaries.
🎯 Exploit Status
Exploitation requires control over sparse tensor input data and knowledge of TensorFlow's internal memory layout. No public exploits have been reported.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.6.0, 2.5.1, 2.4.3, 2.3.4
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cgfm-62j4-v4rf
Restart Required: No
Instructions:
1. Update TensorFlow to patched version using pip: 'pip install --upgrade tensorflow==2.6.0' (or appropriate version). 2. Verify installation with 'python -c "import tensorflow as tf; print(tf.__version__)"'. 3. Test sparse reduction operations to ensure functionality.
🔧 Temporary Workarounds
Disable sparse reduction operations
allAvoid using sparse reduction operations in production code until patched.
# Modify code to use dense tensors instead of sparse tensors for reduction operations
# Or implement custom validation for sparse tensor indices before reduction
Input validation wrapper
allImplement custom validation for sparse tensor indices to ensure they're within bounds.
# Before calling sparse reduction ops, validate: indices < tensor_shape
# Example: if not all(0 <= idx < dim for idx, dim in zip(indices, tensor_shape)): raise ValueError
🧯 If You Can't Patch
- Implement strict input validation for all sparse tensor operations, rejecting any indices outside tensor bounds.
- Isolate TensorFlow processes using containerization or sandboxing to limit potential damage from memory corruption.
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: 'python -c "import tensorflow as tf; print(tf.__version__)"'. If version is between 2.3.0 and 2.5.0 inclusive, you are vulnerable.Check Version:
python -c "import tensorflow as tf; print(tf.__version__)"Verify Fix Applied:
After updating, verify version is 2.6.0, 2.5.1, 2.4.3, or 2.3.4. Test sparse reduction operations with edge cases to ensure no crashes.📡 Detection & Monitoring
Log Indicators:
- Segmentation faults or memory access violation errors in TensorFlow logs
- Unexpected application crashes during sparse tensor operations
- Error messages containing 'out of bounds', 'index', or 'sparse_reduce'
Network Indicators:
- Unusual network traffic patterns if TensorFlow serves models via APIs (though exploit is local)
SIEM Query:
source="tensorflow" AND ("segmentation fault" OR "memory violation" OR "out of bounds")🔗 References
- https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cgfm-62j4-v4rf
- https://github.com/tensorflow/tensorflow/commit/87158f43f05f2720a374f3e6d22a7aaa3a33f750
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cgfm-62j4-v4rf
