CVE-2020-26269
📋 TL;DR
This CVE describes an out-of-bounds memory access vulnerability in TensorFlow's file path globbing implementation. Attackers could potentially read or write memory beyond allocated bounds, leading to crashes or arbitrary code execution. Only TensorFlow 2.4 release candidates and master branch development versions are affected.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Remote code execution leading to complete system compromise, data exfiltration, or ransomware deployment.
Likely Case
Application crash (denial of service) or memory corruption leading to unstable behavior.
If Mitigated
No impact if patched version is used or vulnerable versions are not deployed.
🎯 Exploit Status
Exploitation requires triggering the vulnerable path globbing functionality with specific inputs.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.4.0 (final release)
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9jjw-hf72-3mxw
Restart Required: Yes
Instructions:
1. Upgrade to TensorFlow 2.4.0 or later. 2. Run 'pip install --upgrade tensorflow' or equivalent for your package manager. 3. Restart all TensorFlow-dependent services.
🔧 Temporary Workarounds
Downgrade to stable version
allRevert to TensorFlow 2.3.x or earlier stable versions
pip install tensorflow==2.3.0
Disable parallel globbing
allAvoid using GetMatchingPaths functionality if possible
🧯 If You Can't Patch
- Isolate TensorFlow services in restricted network segments
- Implement strict input validation for path operations
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: 'python -c "import tensorflow as tf; print(tf.__version__)"' - if output starts with '2.4.0rc', you are vulnerable.Check Version:
python -c "import tensorflow as tf; print(tf.__version__)"Verify Fix Applied:
After upgrade, verify version is '2.4.0' or higher (not containing 'rc').📡 Detection & Monitoring
Log Indicators:
- TensorFlow crashes with memory access violations
- Segmentation faults in TensorFlow processes
Network Indicators:
- Unusual file path patterns being sent to TensorFlow services
SIEM Query:
process_name:tensorflow AND (event_id:1000 OR exception_code:0xc0000005)🔗 References
- https://github.com/tensorflow/tensorflow/commit/8b5b9dc96666a3a5d27fad7179ff215e3b74b67c
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9jjw-hf72-3mxw
- https://github.com/tensorflow/tensorflow/commit/8b5b9dc96666a3a5d27fad7179ff215e3b74b67c
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9jjw-hf72-3mxw
