CVE-2022-23587
📋 TL;DR
This CVE describes an integer overflow vulnerability in TensorFlow's Grappler component during cost estimation for crop and resize operations. Attackers can trigger undefined behavior by manipulating cropping parameters, potentially leading to crashes or arbitrary code execution. Users of affected TensorFlow versions are vulnerable.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Remote code execution leading to complete system compromise, data theft, or denial of service.
Likely Case
Application crashes, denial of service, or memory corruption leading to instability.
If Mitigated
Limited impact with proper input validation and sandboxing, potentially just crashes.
🎯 Exploit Status
Exploitation requires specific crop and resize operations with malicious parameters.
🛠️ 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-8jj7-5vxc-pg2q
Restart Required: Yes
Instructions:
1. Check current TensorFlow version. 2. Upgrade to patched version via pip: pip install --upgrade tensorflow==2.8.0 (or appropriate version). 3. Restart all TensorFlow services and applications.
🔧 Temporary Workarounds
Input Validation
allValidate and sanitize all user-controlled cropping parameters before passing to TensorFlow.
Disable Crop and Resize
allTemporarily disable or replace crop and resize operations in ML pipelines if not critical.
🧯 If You Can't Patch
- Implement strict input validation for all crop and resize parameters
- Run TensorFlow in sandboxed/isolated environments with limited privileges
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version and verify if crop and resize operations are used with user inputs.Check Version:
python -c "import tensorflow as tf; print(tf.__version__)"Verify Fix Applied:
Verify TensorFlow version is 2.8.0, 2.7.1, 2.6.3, or 2.5.3 using version check command.📡 Detection & Monitoring
Log Indicators:
- TensorFlow crashes, segmentation faults, or abnormal termination logs related to crop/resize operations
Network Indicators:
- Unusual patterns in ML inference requests with large or malformed cropping parameters
SIEM Query:
source="tensorflow" AND ("crop" OR "resize") AND ("crash" OR "segfault" OR "error")🔗 References
- https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L2621-L2689
- https://github.com/tensorflow/tensorflow/commit/0aaaae6eca5a7175a193696383f582f53adab23f
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8jj7-5vxc-pg2q
- https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/grappler/costs/op_level_cost_estimator.cc#L2621-L2689
- https://github.com/tensorflow/tensorflow/commit/0aaaae6eca5a7175a193696383f582f53adab23f
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8jj7-5vxc-pg2q
