CVE-2021-41224
📋 TL;DR
This vulnerability in TensorFlow allows attackers to trigger a heap out-of-bounds memory access by providing mismatched sizes for indices and values arrays to the SparseFillEmptyRows function. This could potentially lead to memory corruption, crashes, or arbitrary code execution. All TensorFlow users running affected versions are at risk.
💻 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 machine learning models.
If Mitigated
Application crashes with no data loss if proper sandboxing and privilege separation are implemented.
🎯 Exploit Status
Exploitation requires control over the inputs to SparseFillEmptyRows function, which typically requires some level of application access.
🛠️ 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-rg3m-hqc5-344v
Restart Required: No
Instructions:
1. Update TensorFlow using pip: 'pip install --upgrade tensorflow==2.7.0' (or appropriate patched version). 2. Verify the update completed successfully. 3. Test your ML models to ensure compatibility.
🔧 Temporary Workarounds
Input validation wrapper
allAdd validation to ensure indices and values arrays have matching sizes before calling SparseFillEmptyRows
# Python code to wrap the vulnerable function
import tensorflow as tf
def safe_sparse_fill_empty_rows(indices, values, dense_shape, default_value):
if indices.shape[0] != values.shape[0]:
raise ValueError('indices and values must have matching first dimension')
return tf.raw_ops.SparseFillEmptyRows(indices=indices, values=values, dense_shape=dense_shape, default_value=default_value)
🧯 If You Can't Patch
- Implement strict input validation for all user-provided data passed to TensorFlow operations
- Isolate TensorFlow execution in containers or sandboxes with minimal privileges
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: 'python -c "import tensorflow as tf; print(tf.__version__)"' - if version is 2.4.0-2.4.3, 2.5.0-2.5.1, or 2.6.0, you are vulnerable.Check Version:
python -c "import tensorflow as tf; print('TensorFlow version:', tf.__version__)"Verify Fix Applied:
After updating, verify version is 2.7.0, 2.6.1, 2.5.2, or 2.4.4 using the same command.📡 Detection & Monitoring
Log Indicators:
- TensorFlow process crashes with segmentation faults
- Memory access violation errors in application logs
- Unexpected termination of ML inference services
Network Indicators:
- Sudden drops in ML service availability
- Increased error rates in API responses from ML endpoints
SIEM Query:
source="tensorflow" AND ("segmentation fault" OR "memory violation" OR "SparseFillEmptyRows")🔗 References
- https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v
- https://github.com/tensorflow/tensorflow/commit/67bfd9feeecfb3c61d80f0e46d89c170fbee682b
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rg3m-hqc5-344v
