Security vulnerabilities and automated fixes for machine learning issues
4 posts found
A high-severity arbitrary code execution vulnerability was discovered in `tools/ardy-engine/retarget.py` where `np.load()` was called with `allow_pickle=True`, enabling attackers to embed malicious pickle payloads in `.npz` files. The fix was a single-character change—switching `allow_pickle=True` to `allow_pickle=False`—that eliminates the deserialization attack vector while preserving the file's legitimate array data loading functionality.
A critical path traversal vulnerability in the TRL backend allowed attackers to read arbitrary system files like `/etc/passwd` and `/proc/self/environ` through the gRPC fine-tuning API. The `_do_training` method passed user-controlled `dataset_source` directly to `os.path.exists()` and `load_dataset()` without validation. The fix implements strict directory containment checks using `os.path.realpath()` to ensure all file operations stay within allowed directories.
A medium-severity integer overflow vulnerability was discovered in the OpenVINO noise suppression plugin where model input tensor shapes were loaded without dimension validation. An attacker could supply a crafted `.xml/.bin` model file with extremely large or zero-sized dimensions, causing integer overflow during memory allocation or zero-size allocations followed by out-of-bounds writes. The fix introduces a `NS_MAX_SHAPE_DIM` constant that validates each dimension against a safe upper bound b
A critical Server-Side Request Forgery (SSRF) vulnerability was discovered in `models/common.py` where `requests.get()` fetched images from arbitrary URLs without validating whether the target resolved to internal infrastructure. An attacker could supply URLs targeting AWS metadata endpoints (169.254.169.254), private networks, or localhost services through the Flask REST API. The fix introduces DNS-resolution-based validation using Python's `socket.getaddrinfo()` and `ipaddress` module to block