CVE-2022-23560
📋 TL;DR
This vulnerability in TensorFlow allows attackers to craft malicious TFLite models that can read and write memory outside of allocated arrays during sparse-to-dense tensor conversion. This affects all users running vulnerable TensorFlow versions who process untrusted TFLite models, potentially leading to memory corruption and arbitrary code execution.
💻 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
Memory corruption causing application crashes, denial of service, or limited information disclosure.
If Mitigated
No impact if proper input validation and model sanitization are implemented alongside patching.
🎯 Exploit Status
Exploitation requires crafting malicious TFLite models. The vulnerability is in the core conversion logic, making reliable exploitation feasible for skilled attackers.
🛠️ 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-4hvf-hxvg-f67v
Restart Required: Yes
Instructions:
1. Identify TensorFlow version: pip show tensorflow 2. Upgrade to patched version: pip install --upgrade tensorflow==2.8.0 (or appropriate version) 3. Restart all TensorFlow services and applications 4. Verify upgrade: python -c 'import tensorflow as tf; print(tf.__version__)'
🔧 Temporary Workarounds
Disable TFLite Model Processing
allTemporarily disable processing of TFLite models until patching is complete.
# Modify application code to reject .tflite files
# Implement input validation to block sparse tensor models
Sandbox Model Execution
linuxRun TensorFlow in isolated containers with minimal privileges.
docker run --read-only --cap-drop=ALL -v /safe/models:/models tensorflow/tensorflow:latest
🧯 If You Can't Patch
- Implement strict input validation: only allow trusted, verified TFLite models from known sources
- Deploy runtime protection: Use memory-safe languages for model preprocessing or employ ASLR/CFG hardening
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: python -c 'import tensorflow as tf; print(tf.__version__)'. Versions 2.5.0-2.7.0 are vulnerable.Check Version:
python -c 'import tensorflow as tf; print(tf.__version__)'Verify Fix Applied:
Confirm version is 2.8.0, 2.7.1, 2.6.3, or 2.5.3. Test with known safe TFLite models to ensure functionality.📡 Detection & Monitoring
Log Indicators:
- TensorFlow segmentation faults or memory errors
- Unexpected model processing failures
- Large number of .tflite file uploads
Network Indicators:
- Unusual uploads of TFLite models to ML endpoints
- Spikes in model processing requests
SIEM Query:
source="tensorflow" AND (error="segmentation" OR error="memory" OR "tflite" AND status="failed")🔗 References
- https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/internal/utils/sparsity_format_converter.cc#L252-L293
- https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v
- https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/internal/utils/sparsity_format_converter.cc#L252-L293
- https://github.com/tensorflow/tensorflow/commit/6364463d6f5b6254cac3d6aedf999b6a96225038
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hvf-hxvg-f67v
