CVE-2022-21740
📋 TL;DR
CVE-2022-21740 is a heap overflow vulnerability in TensorFlow's SparseCountSparseOutput implementation that allows attackers to write beyond allocated memory boundaries. This can lead to arbitrary code execution, denial of service, or information disclosure. Users of TensorFlow versions 2.5.0-2.7.0 are affected.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Remote code execution with the privileges of the TensorFlow process, potentially leading to complete system compromise.
Likely Case
Application crash (denial of service) or memory corruption leading to unstable behavior.
If Mitigated
Limited impact if TensorFlow runs in sandboxed environments with minimal privileges.
🎯 Exploit Status
Exploitation requires crafting specific input to trigger the heap overflow. No public exploits were available at disclosure.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.8.0, 2.7.1, 2.6.3, and 2.5.3
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-44qp-9wwf-734r
Restart Required: Yes
Instructions:
1. Identify current TensorFlow version. 2. Upgrade to patched version: pip install --upgrade tensorflow==2.8.0 (or appropriate version). 3. Restart all services using TensorFlow. 4. Test functionality after upgrade.
🔧 Temporary Workarounds
Disable SparseCountSparseOutput usage
allAvoid using the vulnerable SparseCountSparseOutput operation in your code
Review and modify code to remove calls to tf.sparse.count_sparse_output or related functions
🧯 If You Can't Patch
- Run TensorFlow in sandboxed/containerized environments with minimal privileges
- Implement input validation and sanitization for all TensorFlow operations
🔍 How to Verify
Check if Vulnerable:
Check TensorFlow version: python -c 'import tensorflow as tf; print(tf.__version__)' and compare to affected versions 2.5.0-2.7.0Check Version:
python -c 'import tensorflow as tf; print(tf.__version__)'Verify Fix Applied:
After patching, verify version is 2.8.0, 2.7.1, 2.6.3, or 2.5.3 using same command📡 Detection & Monitoring
Log Indicators:
- Segmentation faults
- Memory access violation errors
- TensorFlow process crashes
Network Indicators:
- Unusual spikes in ML inference requests
- Requests with malformed tensor data
SIEM Query:
process_name:tensorflow AND (event_type:crash OR error_message:"heap" OR error_message:"overflow")🔗 References
- https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/count_ops.cc#L168-L273
- https://github.com/tensorflow/tensorflow/commit/2b7100d6cdff36aa21010a82269bc05a6d1cc74a
- https://github.com/tensorflow/tensorflow/commit/adbbabdb0d3abb3cdeac69e38a96de1d678b24b3
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-44qp-9wwf-734r
- https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/count_ops.cc#L168-L273
- https://github.com/tensorflow/tensorflow/commit/2b7100d6cdff36aa21010a82269bc05a6d1cc74a
- https://github.com/tensorflow/tensorflow/commit/adbbabdb0d3abb3cdeac69e38a96de1d678b24b3
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-44qp-9wwf-734r
