Individual skill
ai-ml-security
AI/ML security playbook. Use when assessing model supply chain attacks (pickle RCE, poisoned weights), adversarial examples, model poisoning, model stealing, data privacy attacks (membership inference, model inversion), and autonomous agent security risks.
knowledge/hack-skills/skills/ai-ml-security/SKILL.md
Source commit 961a0dd3d2d2f66a07a416ade3b1d4473b64802e
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 4, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,008
- Repository last updated
- Nov 5, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-agent Repository topic
- appsec Repository topic
- bug-bounty Repository topic
- cybersecurity Repository topic
- devsecops Repository topic
- exploit-validation Repository topic
- hermes-agent Repository topic
- infosec Repository topic
- llm-security Repository topic
- nuclei Repository topic
- operations Derived category
- data Derived category
- security Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
Read excerpt
# 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](../llm-prompt-injection/SKILL.md) for LLM-specific prompt injection, jailbreaking, and tool abuse techniques
- [deserialization-insecure](../deserialization-insecure/SKILL.md) for deeper coverage of Python pickle and general deserialization attack patterns
- [dependency-confusion](../dependency-confusion/SKILL.md) when the ML pipeline has supply chain risks via pip/npm package confusion
---
## 1. MODEL SUPPLY CHAIN ATTACKS
