Individual skill

ai-supply-chain

Attack the ML/LLM supply chain — poisoned models, datasets, plugins, and unsafe model deserialization. Load when an app loads third-party models/weights (HuggingFace, .pt/.pkl/.h5), installs ML deps, uses plugins/extensions, or fine-tunes on external data. Signals: torch.load, pickle model files, model hub downloads, plugin marketplace, RAG over external corpora.

skills/ai-ml/ai-supply-chain/SKILL.md

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Last checked: Nov 7, 2026

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Repository stars (not skill ratings): 27
Repository last updated: Oct 14, 2026
License: MIT

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# ML/LLM supply-chain attacks

## When it applies
The target consumes third-party ML artifacts: downloaded model weights, datasets, tokenizers,
plugins/extensions, or fine-tuning data. Each is code or data that runs with the app's trust.

## Why it works
Model files are frequently **pickle-based** (`torch.load`, `.pkl`, joblib) — loading them executes
arbitrary code (`__reduce__`), so a malicious model on a hub is RCE on whoever loads it. Datasets
and RAG corpora poison behavior; plugins/extensions run with the assistant's privileges; typosquatted
ML packages inject code at install.

## Method
1. **Unsafe model deserialization (RCE)**: if the app `torch.load`/`pickle.load`s a model you can