CVE-2022-23573
📋 TL;DR
This vulnerability in TensorFlow's AssignOp implementation allows copying uninitialized data to new tensors, leading to undefined behavior. It affects users of TensorFlow who perform tensor assignment operations. The issue occurs because the code checks if the left-hand side tensor is initialized but fails to verify the right-hand side.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Memory corruption leading to arbitrary code execution, data leakage, or application crashes.
Likely Case
Application instability, crashes, or unpredictable behavior during tensor operations.
If Mitigated
Limited impact if proper input validation and sandboxing are implemented.
🎯 Exploit Status
Exploitation requires crafting specific tensor operations to trigger the uninitialized data copy.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.8.0, 2.7.1, 2.6.3, 2.5.3
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q85f-69q7-55h2
Restart Required: No
Instructions:
1. Update TensorFlow to patched version using pip: pip install --upgrade tensorflow==2.8.0 2. Verify installation with: python -c 'import tensorflow as tf; print(tf.__version__)'
🔧 Temporary Workarounds
Input Validation
allImplement custom checks to ensure all tensors in assignment operations are properly initialized before use.
🧯 If You Can't Patch
- Restrict tensor operations to trusted, validated inputs only
- Implement runtime monitoring for tensor initialization errors
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: python -c 'import tensorflow as tf; print(tf.__version__)' - versions 2.5.0-2.7.0 are vulnerable.Check Version:
python -c 'import tensorflow as tf; print(tf.__version__)'Verify Fix Applied:
Confirm version is 2.8.0, 2.7.1, 2.6.3, or 2.5.3 using same command.📡 Detection & Monitoring
Log Indicators:
- TensorFlow error logs containing 'uninitialized' or 'undefined behavior' messages
- Application crashes during tensor assignment operations
Network Indicators:
- Unusual patterns in ML inference request failures
SIEM Query:
source="tensorflow" AND ("uninitialized" OR "assignop" OR "undefined behavior")🔗 References
- https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/assign_op.h#L30-L143
- https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q85f-69q7-55h2
- https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/assign_op.h#L30-L143
- https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q85f-69q7-55h2
