com um clique
adversarial-machine-learning
// Guide for adversarial machine learning: adversarial examples, data poisoning, model backdoors, and evasion attacks.
// Guide for adversarial machine learning: adversarial examples, data poisoning, model backdoors, and evasion attacks.
Guide for understanding and contributing to the awesome-ai-security curated resource list. Use this skill when adding resources, organizing categories, or maintaining README.md consistency (no duplicates).
Guide for AI-powered penetration testing tools, red teaming frameworks, and autonomous security agents.
Guide for LLM security attacks: prompt injection, jailbreaking, data extraction, and where to place resources in README.md.
Guide for AI security tooling (detectors, analyzers, guardrails, benchmarks) and consistent placement in README.md.
| name | adversarial-machine-learning |
| description | Guide for adversarial machine learning: adversarial examples, data poisoning, model backdoors, and evasion attacks. |
Use this skill when working on:
AI Security & Attacks → Adversarial AttacksAI Security & Attacks → Poisoning & BackdoorsAI Security & Attacks → Privacy & ExtractionAI Security Tools & Frameworks → AI Security LibrariesBenchmarks & StandardsKeep additions:
For detailed and up-to-date resources, fetch the complete list from:
https://raw.githubusercontent.com/gmh5225/awesome-ai-security/refs/heads/main/README.md
Use this URL to get the latest curated links when you need specific tools, papers, or resources not covered in this skill.