CVE-2022-29208
📋 TL;DR
This CVE allows attackers to cause a segmentation fault and denial of service in TensorFlow by passing negative values to the tf.raw_ops.EditDistance function. The vulnerability affects users running TensorFlow versions before 2.6.4, 2.7.2, 2.8.1, or 2.9.0 who process untrusted input through this function.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Complete service disruption through segmentation fault crashes, potentially leading to availability issues in machine learning inference services.
Likely Case
Denial of service through application crashes when processing malicious input, disrupting ML workflows.
If Mitigated
No impact if patched versions are used or if untrusted input is properly validated before processing.
🎯 Exploit Status
Exploitation requires passing specially crafted input to the vulnerable function.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: 2.6.4, 2.7.2, 2.8.1, or 2.9.0
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c78-pq5v-5wq2
Restart Required: Yes
Instructions:
1. Identify current TensorFlow version. 2. Upgrade to 2.6.4, 2.7.2, 2.8.1, or 2.9.0 using pip: 'pip install --upgrade tensorflow==2.9.0'. 3. Restart all TensorFlow services and applications.
🔧 Temporary Workarounds
Input Validation
allValidate all input to tf.raw_ops.EditDistance to ensure no negative values are passed.
Function Restriction
allDisable or restrict usage of tf.raw_ops.EditDistance in production environments.
🧯 If You Can't Patch
- Implement strict input validation for all parameters passed to tf.raw_ops.EditDistance
- Isolate TensorFlow services in containers with resource limits to contain crash impacts
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: python -c 'import tensorflow as tf; print(tf.__version__)' and compare to affected versions.Check Version:
python -c 'import tensorflow as tf; print(tf.__version__)'Verify Fix Applied:
After patching, verify version is 2.6.4, 2.7.2, 2.8.1, or 2.9.0+ and test EditDistance with edge cases.📡 Detection & Monitoring
Log Indicators:
- Segmentation fault errors
- TensorFlow crash logs
- Application termination with memory access violations
Network Indicators:
- Unusual input patterns to ML endpoints
- Repeated requests with negative parameter values
SIEM Query:
source="tensorflow" AND ("segmentation fault" OR "SIGSEGV" OR "memory access violation")🔗 References
- https://github.com/tensorflow/tensorflow/commit/30721cf564cb029d34535446d6a5a6357bebc8e7
- https://github.com/tensorflow/tensorflow/releases/tag/v2.6.4
- https://github.com/tensorflow/tensorflow/releases/tag/v2.7.2
- https://github.com/tensorflow/tensorflow/releases/tag/v2.8.1
- https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2r2f-g8mw-9gvr
- https://github.com/tensorflow/tensorflow/commit/30721cf564cb029d34535446d6a5a6357bebc8e7
- https://github.com/tensorflow/tensorflow/releases/tag/v2.6.4
- https://github.com/tensorflow/tensorflow/releases/tag/v2.7.2
- https://github.com/tensorflow/tensorflow/releases/tag/v2.8.1
- https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2r2f-g8mw-9gvr
