Security vulnerabilities and automated fixes for tensorflow issues
3 posts found
TensorFlow's data service dispatcher validated dataset IDs against forward-slash traversal attacks but overlooked backslash characters on non-Windows platforms, allowing attackers to escape the root directory. A targeted fix adds explicit backslash validation across all platforms, closing a high-severity path traversal vulnerability in the snapshot management system.
A shell injection vulnerability in TensorFlow's DELF dataset download script allowed attackers who controlled the `data_dir` parameter to execute arbitrary shell commands by injecting metacharacters into `os.system()` calls. The fix replaces all four `os.system()` invocations with `subprocess.run()` using argument lists, eliminating shell interpretation entirely. This change closes a high-severity code execution path in production ML infrastructure.
A high-severity untrusted deserialization vulnerability was discovered in `TransferLearningTF.ipynb`, a transfer learning tutorial notebook that loads VGG16 model weights from the internet without verifying their integrity. Because Keras relies on Python's pickle-based serialization format under the hood, a tampered or substituted weights file could execute arbitrary code with the full privileges of the notebook user. The fix adds a SHA-256 checksum verification step immediately after the weight