CVE-2022-23558
📋 TL;DR
This CVE describes an integer overflow vulnerability in TensorFlow's TFLite component where an attacker can craft a malicious TFLite model to trigger memory corruption. The vulnerability affects TensorFlow versions before the patched releases, potentially allowing denial of service or arbitrary code execution. Anyone using TensorFlow for machine learning inference with untrusted models is affected.
💻 Affected Systems
- TensorFlow
- TensorFlow Lite
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Remote code execution leading to complete system compromise, data theft, or ransomware deployment
Likely Case
Denial of service through application crashes or memory corruption leading to instability
If Mitigated
Limited impact with proper input validation and sandboxing of model execution
🎯 Exploit Status
Exploitation requires the ability to provide TFLite models to the vulnerable system
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.8.0, 2.7.1, 2.6.3, 2.5.3
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3
Restart Required: Yes
Instructions:
1. Identify TensorFlow version currently installed. 2. Upgrade to patched version: pip install tensorflow==2.8.0 (or appropriate version). 3. Restart all services using TensorFlow. 4. Verify the fix by checking version and testing with known safe models.
🔧 Temporary Workarounds
Input validation for TFLite models
allImplement strict validation of TFLite model inputs before processing
# Implement custom validation in model loading code
# Reject models with suspiciously large dimension sizes
Sandbox model execution
linuxRun TFLite model inference in isolated containers or sandboxes
# Use Docker containers with resource limits
# Implement seccomp or AppArmor profiles
🧯 If You Can't Patch
- Restrict TFLite model sources to trusted providers only
- 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__)' and compare to affected versionsCheck Version:
python -c 'import tensorflow as tf; print(tf.__version__)'Verify Fix Applied:
Verify version is 2.8.0, 2.7.1, 2.6.3, or 2.5.3 or higher. Test with known safe TFLite models to ensure stability.📡 Detection & Monitoring
Log Indicators:
- Segmentation faults in TensorFlow processes
- Memory allocation failures
- Unexpected process termination
Network Indicators:
- Unusual TFLite model uploads to ML endpoints
- Large model files from unexpected sources
SIEM Query:
process_name:"python" AND (event_type:"segmentation_fault" OR event_type:"out_of_memory") AND process_cmdline:"tensorflow"🔗 References
- https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L24-L33
- https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L53-L60
- https://github.com/tensorflow/tensorflow/commit/a1e1511dde36b3f8aa27a6ec630838e7ea40e091
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3
- https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L24-L33
- https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/c/common.c#L53-L60
- https://github.com/tensorflow/tensorflow/commit/a1e1511dde36b3f8aa27a6ec630838e7ea40e091
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9gwq-6cwj-47h3
