CVE-2021-37663
📋 TL;DR
This vulnerability in TensorFlow's QuantizeV2 operation allows attackers to trigger undefined behavior by accessing invalid memory locations. Attackers can cause crashes or potentially execute arbitrary code by exploiting incomplete validation of input parameters. 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 service disruption.
Likely Case
Application crashes, denial of service, or memory corruption leading to unstable behavior.
If Mitigated
No impact if patched or if vulnerable code paths are not invoked.
🎯 Exploit Status
Exploitation requires ability to call QuantizeV2 with controlled parameters, typically through ML model inference APIs.
🛠️ 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-g25h-jr74-qp5j
Restart Required: No
Instructions:
1. Update TensorFlow to patched version using pip: 'pip install --upgrade tensorflow==2.6.0' 2. Verify installation with 'python -c "import tensorflow as tf; print(tf.__version__)"' 3. Test QuantizeV2 operations to ensure stability.
🔧 Temporary Workarounds
Input Validation Wrapper
allAdd validation layer before calling QuantizeV2 to ensure min_range and max_range have same non-zero length
# Python code to wrap QuantizeV2 calls
import tensorflow as tf
def safe_quantize_v2(input_tensor, min_range, max_range, axis=-1):
if axis != -1:
if not (min_range.shape == max_range.shape):
raise ValueError("min_range and max_range must have same shape")
if min_range.shape[0] != input_tensor.shape[axis]:
raise ValueError("Range dimensions must match axis dimension")
return tf.raw_ops.QuantizeV2(input=input_tensor, min_range=min_range, max_range=max_range, axis=axis)
🧯 If You Can't Patch
- Disable or restrict access to APIs that call QuantizeV2 operation
- Implement network segmentation to isolate TensorFlow services from critical systems
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: 'python -c "import tensorflow as tf; print(tf.__version__)"' - versions 2.3.0-2.5.0 are vulnerable.Check Version:
python -c "import tensorflow as tf; print('TensorFlow version:', tf.__version__)"Verify Fix Applied:
After patching, test QuantizeV2 with malformed inputs to ensure proper error handling instead of crashes.📡 Detection & Monitoring
Log Indicators:
- Segmentation faults in TensorFlow processes
- Memory access violation errors
- Unexpected TensorFlow service restarts
Network Indicators:
- Unusual spikes in ML inference API errors
- Increased failed quantization requests
SIEM Query:
source="tensorflow" AND ("segmentation fault" OR "memory violation" OR "QuantizeV2 error")🔗 References
- https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g25h-jr74-qp5j
- https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-g25h-jr74-qp5j
