awesome-ai-security

A curated list of AI Security materials and resources for Pentesters, Bug Hunters, and Security Researchers.

If you find that some links are not working, you can simply replace the username with gmh5225.
Or you can send an issue for me.

Show respect to all the projects below, perfect works of art 🫡

How to contribute?

• https://github.com/HyunCafe/contribute-practice
• https://docs.github.com/en/get-started/quickstart/contributing-to-projects

Skills for AI Agents

This repository provides skills that can be used with AI agents and coding assistants such as Cursor, OpenClaw, Claude Code, Codex CLI, and other compatible tools. Install skills to get specialized knowledge about game security topics.

• https://github.com/vercel-labs/skills [The open agent skills tool - npx skills]

View on learn-skills.dev

Installation:

npx skills add https://github.com/gmh5225/awesome-ai-security --skill <skill-name>

Available Skills:

Skill	Description
adversarial-machine-learning	Adversarial machine learning: adversarial examples, data poisoning, model backdoors, and evasion attacks
ai-powered-pentesting	AI-powered penetration testing tools, red teaming frameworks, and autonomous security agents
llm-attacks-security	LLM security attacks: prompt injection, jailbreaking, and data extraction
awesome-ai-security-overview	Overview of this repository and contribution guidelines
ai-security-tooling	AI security tooling: detectors, analyzers, guardrails, and benchmarks

Example:

# Install LLM attacks skill
npx skills add https://github.com/gmh5225/awesome-ai-security --skill llm-attacks-security

# Install multiple skills
npx skills add https://github.com/gmh5225/awesome-ai-security --skill adversarial-machine-learning
npx skills add https://github.com/gmh5225/awesome-ai-security --skill ai-powered-pentesting

AI Security Starter Pack

• CTFs / Practice

  • https://github.com/verialabs/ctf-agent [ctf-agent - autonomous CTFd solver: coordinator LLM + parallel model swarms in Docker; BSidesSF 2026 1st]
  • https://aivillage.org/ [AI Village @ DEF CON - LLM Jailbreak Challenges]
  • https://doublespeak.chat/#/handbook [Doublespeak - AI Security Challenges]
  • https://github.com/EasyJailbreak/EasyJailbreak [Framework for adversarial jailbreak prompts]
  • https://github.com/microsoft/AI-Red-Teaming-Playground-Labs [Microsoft AI Red Teaming Playground Labs]
  • https://github.com/schwartz1375/genai-security-training [GenAI Red Teaming Training]

• Blogs / Resources

  • https://genai.owasp.org/ [OWASP GenAI Security Project]
  • https://llm-stats.com [LLM Leaderboard]
  • https://www.aidaily.win [AI Daily News]
  • https://baoyu.io/blog/how-to-write-good-prompt [How to Write Good Prompts]
  • https://rootissh.in/ [LLM Pentesting Series Blog]
  • https://github.com/Abdowaer098/Wa3r-OffSec-Kit [Wa3r OffSec Kit - offensive-security knowledge base with practical workflows, payload patterns, case studies, and forensics notes]

• Newsletters / Collections

  • https://mlsecops.com/podcast [MLSecOps Podcast]
  • https://podcasts.apple.com/ph/podcast/the-genai-security-podcast/id1782916580 [GenAI Security Podcast]
  • https://avidml.org/ [AI Vulnerability Database (AVID)]

• Certifications / Courses

  • https://cs229.stanford.edu/ [Stanford CS229: Machine Learning]
  • https://course.fast.ai/ [fast.ai Practical Deep Learning]
  • https://www.coursera.org/specializations/deep-learning [Deep Learning Specialization by Andrew Ng]
  • https://huggingface.co/reasoning-course [Build DeepSeek-R1 like Reasoning Model]

AI/LLM Guide

• Foundations

  • https://d2l.ai/ [Dive into Deep Learning - Interactive book with PyTorch/JAX/TensorFlow]
  • http://neuralnetworksanddeeplearning.com/ [Neural Networks and Deep Learning by Michael Nielsen]
  • https://www.deeplearningbook.org/ [Deep Learning by Goodfellow, Bengio, Courville]
  • https://github.com/karminski/one-small-step [AI/LLM Tutorial]
  • https://github.com/datawhalechina/happy-llm [LLM Principles and Practice Tutorial]
  • https://github.com/rasbt/LLMs-from-scratch [Build LLM from Scratch]
  • https://github.com/naklecha/llama3-from-scratch [LLaMA3 from Scratch]
  • https://github.com/ZJU-LLMs/Foundations-of-LLMs [Foundations of LLMs]

• Awesome Lists

  • https://github.com/WangRongsheng/awesome-LLM-resourses [Comprehensive LLM Resources]
  • https://github.com/mahseema/awesome-ai-tools [Awesome AI Tools]
  • https://github.com/Shubhamsaboo/awesome-llm-apps [Awesome LLM Apps]
  • https://github.com/mahonzhan/awesome-agent-harness [Curated list of agent harnesses, agent frameworks, workflow frameworks, and emerging agent protocols]
  • https://github.com/punkpeye/awesome-mcp-servers [Awesome MCP Servers]
  • https://github.com/wong2/awesome-mcp-servers [Awesome MCP Servers]
  • https://github.com/deepseek-ai/awesome-deepseek-integration [Awesome DeepSeek Integration]
  • https://github.com/lmmlzn/Awesome-LLMs-Datasets [Awesome LLMs Datasets]

• From-scratch LLMs / Reasoning

  • https://github.com/rasbt/LLMs-from-scratch/tree/main/ch05/11_qwen3 [Qwen3 From Scratch - Chinese walkthrough]
  • https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/11_qwen3/standalone-qwen3-moe-plus-kvcache.ipynb [Implement Qwen3 MoE with KV cache from scratch]
  • https://github.com/rasbt/LLMs-from-scratch/tree/main/ch05/12_gemma3 [Build Gemma 3 270M from scratch]
  • https://github.com/rasbt/reasoning-from-scratch [Reasoning models from scratch]
  • https://github.com/mingyin0312/RLFromScratch [Reinforcement learning from scratch (Chinese tutorial)]
  • https://github.com/karpathy/nanochat [End-to-end nanochat training loop in ~8K lines]
  • https://github.com/kyegomez/OpenMythos [OpenMythos - first-principles theoretical reconstruction of Claude Mythos architecture based on public research literature]
  • https://github.com/vixhal-baraiya/microgpt-c [MicroGPT-C — train and infer a tiny GPT in pure dependency-free C (single file); fp32/AVX2 style CPU path; MIT]

AI Security & Attacks

Prompt Injection

• https://www.lakera.ai/blog/guide-to-prompt-injection [Prompt Injection Guide]
• https://genai.owasp.org/llmrisk/llm01-prompt-injection/ [OWASP LLM01:2025 Prompt Injection]
• https://redbotsecurity.com/prompt-injection-attacks-ai-security-2025/ [Prompt Injection Attacks 2025]
• https://github.com/protectai/rebuff [Self-hardening Prompt Injection Detector]
• https://github.com/NVIDIA/garak [NVIDIA LLM Vulnerability Scanner]
• https://github.com/deadbits/vigil-llm [Detects Prompt Injections and Risky Inputs]
• https://github.com/alphasecio/prompt-guard [Prompt Defense for LLM]
• https://github.com/tml-epfl/llm-adaptive-attacks [Adaptive Attacks on LLMs]
• https://github.com/RomiconEZ/llamator [LLM Vulnerability Testing Framework]
• https://github.com/gh0stOo/claude-md-vorlagen-de/blob/main/guides/prompt-hardening.md [German-language prompt-injection hardening guide with 10 concrete before/after code patterns (system/user separation, delimiters, output validation, RAG source distrust)]
• https://github.com/Vick606/subcanopy-guard [Context-Aware Indirect Prompt Injection Scanner]