CVE-2022-21726
📋 TL;DR
This vulnerability in TensorFlow's Dequantize operation allows attackers to read past the end of memory arrays by providing invalid axis values, potentially exposing sensitive data or causing crashes. It affects all TensorFlow users who process quantized tensors with the vulnerable operation. The issue stems from insufficient validation of the axis parameter.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Memory corruption leading to arbitrary code execution, data exfiltration, or complete system compromise through heap-based out-of-bounds reads.
Likely Case
Application crashes, denial of service, or information disclosure through memory leakage of adjacent data structures.
If Mitigated
Limited impact with proper input validation and sandboxing, potentially just crashes in isolated environments.
🎯 Exploit Status
Exploitation requires control over the axis parameter in Dequantize operations. While no public exploits exist, the vulnerability is well-documented in the advisory.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.8.0, 2.7.1, 2.6.3, and 2.5.3
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72
Restart Required: No
Instructions:
1. Identify your TensorFlow version. 2. Upgrade to patched version: pip install --upgrade tensorflow==2.8.0 (or appropriate version). 3. Verify the update with import tensorflow; print(tensorflow.__version__).
🔧 Temporary Workarounds
Input Validation Wrapper
allImplement custom validation for axis parameter before calling Dequantize operations
# Python example:
def safe_dequantize(tensor, axis=-1):
if axis >= tensor.shape.rank:
raise ValueError('Axis out of bounds')
return tf.raw_ops.Dequantize(input=tensor, axis=axis)
🧯 If You Can't Patch
- Disable or restrict access to services using TensorFlow Dequantize operations
- Implement strict input validation and sanitization for all tensor processing pipelines
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: python -c 'import tensorflow as tf; print(tf.__version__)' and compare to affected versions (2.5.0-2.7.0).Check Version:
python -c 'import tensorflow as tf; print(tf.__version__)'Verify Fix Applied:
After patching, verify version is 2.8.0, 2.7.1, 2.6.3, or 2.5.3. Test Dequantize with edge cases to ensure proper bounds checking.📡 Detection & Monitoring
Log Indicators:
- TensorFlow segmentation faults
- Memory access violation errors
- Unexpected application crashes during tensor operations
Network Indicators:
- Unusual patterns in ML inference API calls with axis parameters
SIEM Query:
process.name:"python" AND (event.action:"segmentation_fault" OR log.message:"tensorflow.*Dequantize")🔗 References
- https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153
- https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72
- https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/dequantize_op.cc#L92-L153
- https://github.com/tensorflow/tensorflow/commit/23968a8bf65b009120c43b5ebcceaf52dbc9e943
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-23hm-7w47-xw72
