Shah AI Security Lab / Labs

ASL-021Planned

Malicious Model / Unsafe Serialization

Testing whether unsafe model-file formats and loading practices can lead to code execution, and how Safetensors and related safeguards change that.

Category
AI Supply Chain
Difficulty
Advanced
Last Updated
20 September 2026
  • supply-chain
  • serialization
  • safetensors

Lab detail

Objective

Some legacy model-serialization formats (notably pickle-based .bin/.pt files) can execute arbitrary code on load, which is why safer formats like Safetensors exist. This lab will test, in an isolated environment, how a malicious payload embedded in an unsafe serialization format behaves when loaded through common tooling, and how effectively safer formats and scanning tools prevent it.

Planned Scope

  • Build isolated, disposable environments for safely observing unsafe deserialization behavior.
  • Compare loading behavior across unsafe legacy formats and Safetensors.
  • Evaluate model-scanning tools for detection of known-unsafe patterns.
  • Document safe model-loading practices for a private/local LLM pipeline.

This lab specifically requires strict environment isolation — see Responsible Use below before any implementation work begins.

Framework Mapping

Referenced frameworks

Exact control-ID mappings are to be validated as this lab progresses — see the full Frameworks reference.

  • MITRE ATLAS

Responsible Use / Safety Notes

How this lab is run

This lab uses synthetic data and controlled environments. It is intended to demonstrate defensive AI-security principles and does not contain customer data, production credentials or intentionally weaponized exploit infrastructure.

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