CVE-2021-29614
📋 TL;DR
This vulnerability in TensorFlow's tf.io.decode_raw function allows out-of-bounds memory writes when combining fixed_length with wider datatypes, potentially leading to interpreter crashes or memory corruption. All TensorFlow users running affected versions are impacted, particularly those processing untrusted input data.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Remote code execution or complete system compromise through memory corruption attacks, potentially allowing attackers to execute arbitrary code in the context of the TensorFlow process.
Likely Case
Application crashes, denial of service, or memory corruption leading to unpredictable behavior and potential data corruption.
If Mitigated
Limited impact if input validation and sandboxing prevent exploitation, though crashes may still occur with malformed inputs.
🎯 Exploit Status
Proof of concept demonstrates interpreter crash. The memory corruption primitive could be weaponized for more severe attacks, though this requires additional exploitation techniques.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.5.0, 2.4.2, 2.3.3, 2.2.3, 2.1.4
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8pmx-p244-g88h
Restart Required: No
Instructions:
1. Upgrade TensorFlow to patched version: pip install --upgrade tensorflow==2.5.0 (or appropriate version). 2. Verify installation with: python -c 'import tensorflow as tf; print(tf.__version__)'. 3. Test that tf.io.decode_raw functions correctly with fixed_length parameter.
🔧 Temporary Workarounds
Avoid tf.io.decode_raw with fixed_length
allTemporarily avoid using tf.io.decode_raw with the fixed_length parameter until patched.
Input validation and sanitization
allImplement strict input validation for any data passed to tf.io.decode_raw, particularly checking fixed_length parameter values.
🧯 If You Can't Patch
- Implement strict input validation and sanitization for all data passed to tf.io.decode_raw
- Isolate TensorFlow processes in containers or sandboxes to limit potential damage from exploitation
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: python -c 'import tensorflow as tf; print(tf.__version__)'. If version is between 2.1.0-2.4.1 (excluding patched versions), you are vulnerable.Check Version:
python -c 'import tensorflow as tf; print(tf.__version__)'Verify Fix Applied:
After patching, test tf.io.decode_raw with fixed_length parameter using known problematic inputs to ensure no crashes occur.📡 Detection & Monitoring
Log Indicators:
- Python interpreter crashes
- Segmentation faults in TensorFlow processes
- Memory access violation errors
Network Indicators:
- Unusual patterns of data being sent to TensorFlow services
- Repeated connection attempts followed by service crashes
SIEM Query:
source="tensorflow" AND (event_type="crash" OR error="segmentation fault" OR error="memory access violation")🔗 References
- https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8pmx-p244-g88h
- https://github.com/tensorflow/tensorflow/commit/698e01511f62a3c185754db78ebce0eee1f0184d
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8pmx-p244-g88h
