CVE-2021-37656
📋 TL;DR
This vulnerability in TensorFlow allows an attacker to cause undefined behavior by providing malformed input to the tf.raw_ops.RaggedTensorToSparse function. Attackers can trigger null pointer dereferences leading to crashes or potential code execution. Anyone using affected TensorFlow versions is vulnerable.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Remote code execution leading to complete system compromise, data theft, or service disruption.
Likely Case
Denial of service through application crashes or instability in TensorFlow-based services.
If Mitigated
Limited impact with proper input validation and sandboxing in place.
🎯 Exploit Status
Exploitation requires ability to call the vulnerable function with crafted input. No public exploits known at advisory time.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.6.0, 2.5.1, 2.4.3, and 2.3.4
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4xfp-4pfp-89wg
Restart Required: No
Instructions:
1. Update TensorFlow to patched version: 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 affected functionality.
🔧 Temporary Workarounds
Input Validation Wrapper
allAdd validation to ensure splits arrays are strictly increasing before calling RaggedTensorToSparse
# Python code to validate splits
import numpy as np
def safe_ragged_to_sparse(splits, values):
if not np.all(np.diff(splits) > 0):
raise ValueError('Splits must be strictly increasing')
return tf.raw_ops.RaggedTensorToSparse(splits=splits, values=values)
🧯 If You Can't Patch
- Disable or restrict access to tf.raw_ops.RaggedTensorToSparse function in production environments
- Implement strict input validation and sanitization for all TensorFlow operations accepting user input
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: python -c 'import tensorflow as tf; print(tf.__version__)'. If version is 2.3.0-2.3.3, 2.4.0-2.4.2, 2.5.0, or 2.6.0-rc, you are vulnerable.Check Version:
python -c 'import tensorflow as tf; print(tf.__version__)'Verify Fix Applied:
After patching, verify version is 2.6.0, 2.5.1, 2.4.3, or 2.3.4. Test with sample code that previously triggered the issue.📡 Detection & Monitoring
Log Indicators:
- Segmentation faults or crashes in TensorFlow processes
- Error logs mentioning RaggedTensorToSparse failures
- Unexpected process terminations in ML inference services
Network Indicators:
- Unusual spikes in failed API calls to ML services
- Increased error rates in TensorFlow-serving endpoints
SIEM Query:
process_name:"python" AND (event_type:"segmentation_fault" OR event_type:"crash") AND process_cmdline:"tensorflow"🔗 References
- https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4xfp-4pfp-89wg
- https://github.com/tensorflow/tensorflow/commit/1071f554dbd09f7e101324d366eec5f4fe5a3ece
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4xfp-4pfp-89wg
