
Ai Ml Security
This is a comprehensive attack playbook covering the full spectrum of ML security risks, from pickle deserialization RCE in PyTorch model files to adversarial examples and federated learning poisoning. It walks through concrete exploits like backdooring Hugging Face models, crafting physical-world adversarial patches, and extracting black-box models via API queries. The skill includes actual attack code, detection signals for malicious models, and comparison tables for different threat vectors (FGSM vs PGD vs C&W, safetensors vs pickle formats). Use this when you need to assess ML system security beyond basic prompt injection, especially for supply chain risks in model downloads or when evaluating robustness against data poisoning and model extraction attacks.
- skills.sh installs
- 3.8K
- GitHub stars
- 2.6K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add yaklang/hack-skills --skill ai-ml-security --agent claude-codeInstalls into .claude/skills of the current project.
Files
SKILL: AI/ML Security — Expert Attack Playbook
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.
0. RELATED ROUTING
- llm-prompt-injection for LLM-specific prompt injection, jailbreaking, and tool abuse techniques
- deserialization-insecure for deeper coverage of Python pickle and general deserialization attack patterns
- dependency-confusion when the ML pipeline has supply chain risks via pip/npm package confusion
1. MODEL SUPPLY CHAIN ATTACKS
1.1 Malicious Model Files — Pickle RCE
Sponsors
Details
- Repository
- github.com/yaklang/hack-skills
- Categories
- Security, AI & Agent Building, Data Science & ML
- First seen
- Jun 3, 2026
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