Scanning AI Models for Security Risks
As AI adoption increases, machine learning (ML) models have become a potential attack vector for malware. Certain model formats, particularly those using Python’s serialization, can execute arbitrary code when loaded, making them susceptible to remote code execution (RCE) attacks. MetaDefender Sandbox supports scanning AI model files, including **.pkl** and **.pt**, providing robust detection for the most critical attack vectors.
Common AI Model Attack Vectors
Several ML model formats can introduce security risks:
- Pickle Files (
**.pkl**,**.pt**) – Used in PyTorch and other ML frameworks, these files allow deserialization of arbitrary objects, potentially executing embedded malicious code. - PyTorch Checkpoints (
**pytorch_model.bin**) – Relies on Python'spicklemechanism, making it vulnerable to similar RCE risks. - TensorFlow Models (
**.h5**,**.pb**) – Can be manipulated to trigger vulnerabilities in TensorFlow’s parsing logic. - ONNX Models (
**.onnx**) – While structured, certain model operators may still be exploited. - ZIP/TAR Archives (
**.zip**,**.tar.gz**) – Often used to package multiple files, potentially concealing malicious scripts or payloads.
How MetaDefender Sandbox Enhances AI Model Security
MetaDefender Sandbox provides comprehensive security analysis for ML models, identifying potential threats before they can impact AI workflows.
Key Capabilities:
- Deep Static Analysis: Extracts and analyzes serialized objects inside
.pkland.ptfiles. - Heuristic and Behavioral Detection: Identifies suspicious execution patterns and potential exploitation attempts.
By scanning AI model files before deployment, organizations can mitigate the risk of malicious payloads compromising their AI infrastructure.