CVE-2021-37650
📋 TL;DR
This vulnerability in TensorFlow allows attackers to trigger heap buffer overflows and segmentation faults by passing non-string data types to dataset-to-record conversion functions. It affects TensorFlow users who process datasets with numeric types using tf.raw_ops.ExperimentalDatasetToTFRecord or tf.raw_ops.DatasetToTFRecord operations. The vulnerability stems from improper input validation that assumes all dataset records are strings.
💻 Affected Systems
- TensorFlow
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
Remote code execution leading to complete system compromise, data corruption, or denial of service through application crashes.
Likely Case
Application crashes (segmentation faults) causing denial of service and potential data loss in affected TensorFlow pipelines.
If Mitigated
Limited impact with proper input validation and sandboxing, potentially just application instability.
🎯 Exploit Status
Exploitation requires access to TensorFlow operations that process datasets, typically through application code or APIs.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: TensorFlow 2.6.0, 2.5.1, 2.4.3, 2.3.4
Vendor Advisory: https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f8h4-7rgh-q2gm
Restart Required: Yes
Instructions:
1. Identify TensorFlow version. 2. Upgrade to patched version: pip install tensorflow==2.6.0 (or appropriate version). 3. Restart all TensorFlow services and applications. 4. Verify fix with version check.
🔧 Temporary Workarounds
Input Validation Workaround
allAdd explicit type checking before calling vulnerable operations to ensure all dataset elements are strings.
# Python example: Ensure dataset contains only strings
import tensorflow as tf
# Add validation layer before vulnerable ops
def validate_string_dataset(dataset):
return dataset.map(lambda x: tf.ensure_shape(x, [])).filter(lambda x: x.dtype == tf.string)
🧯 If You Can't Patch
- Disable or remove usage of tf.raw_ops.ExperimentalDatasetToTFRecord and tf.raw_ops.DatasetToTFRecord operations
- Implement strict input validation and sanitization for all dataset processing pipelines
🔍 How to Verify
Check if Vulnerable:
Check if code uses tf.raw_ops.ExperimentalDatasetToTFRecord or tf.raw_ops.DatasetToTFRecord with potentially non-string datasets.Check Version:
python -c 'import tensorflow as tf; print(tf.__version__)'Verify Fix Applied:
Verify TensorFlow version is 2.6.0+, 2.5.1+, 2.4.3+, or 2.3.4+ using version check command.📡 Detection & Monitoring
Log Indicators:
- Segmentation fault errors
- TensorFlow core dumps
- Unexpected process termination in dataset processing
Network Indicators:
- Unusual dataset processing requests to TensorFlow-serving APIs
SIEM Query:
source="tensorflow" AND ("segmentation fault" OR "core dumped" OR "DatasetToTFRecord")🔗 References
- https://github.com/tensorflow/tensorflow/commit/e0b6e58c328059829c3eb968136f17aa72b6c876
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f8h4-7rgh-q2gm
- https://github.com/tensorflow/tensorflow/commit/e0b6e58c328059829c3eb968136f17aa72b6c876
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f8h4-7rgh-q2gm
