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🔒 AI Pentesting Roadmap — LLM Security and Offensive Testing
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Overview

This roadmap provides a structured learning path for practitioners aiming to assess and attack AI/ML systems, with a focus on LLMs and related pipelines. It organizes topics into progressive phases: foundations in ML and APIs, core AI security concepts, prompt injection and LLM-specific attacks, hands-on labs, advanced exploitation techniques, and real-world research/bug bounty work.

Phased Structure

Phase 1 (Foundations) covers machine learning fundamentals and LLM internals, including model architectures and tokenization concepts. Phase 2 (AI/ML Security Concepts) anchors the curriculum on standards and frameworks such as OWASP LLM Top 10, MITRE ATLAS, and NIST AI risk guidance. Phase 3 focuses on prompt injection and LLM adversarial vectors, describing attack surfaces like context manipulation, instruction-following bypasses, and RAG pipeline poisoning. Phase 4 emphasizes hands-on practice through CTFs, sandboxed labs, and safe testing methodologies. Phase 5 explores advanced exploitation: model poisoning, data poisoning, backdoor techniques, and chaining vulnerabilities across API/authentication layers. Phase 6 targets real-world research, disclosure workflows, and bug bounty engagement.

Technical Coverage

The roadmap lists practical tooling and repositories for experiment design and testing concepts without prescribing deployment steps. It calls out necessary foundations—Python programming, HTTP/API mechanics, and web security basics (XSS, SSRF, SQLi) to support end-to-end attack scenarios against AI systems. Notable conceptual risks include RAG poisoning, adversarial ML perturbations, prompt injection, and leakage through augmented memory or external tool integrations.

Limitations & Considerations

The guide is educational and emphasizes conceptual descriptions of capabilities and use cases rather than operational recipes. It highlights standards and references rather than prescriptive mitigations. Practical exploration should respect ethical boundaries and responsible disclosure norms.

🔹 OWASP #MITRE_ATLAS #RAG #prompt_injection #adversarialML

🔗 Source: https://github.com/anmolksachan/AI-ML-Free-Resources-for-Security-and-Prompt-Injection

merve (@mervenoyann)

트윗은 'distillation-attack-as-a-service'를 언급합니다. 이는 모델 증류 기법을 악용해 모델 지식이나 동작을 모방·추출하는 공격을 서비스 형태로 제공한다는 의미로 해석될 수 있으며, 모델 저작권 침해·데이터 유출·보안 취약성을 시사합니다. AI 개발자와 보안팀은 API 사용·모델 공개 정책 및 추론 보안 방어를 점검해야 합니다.

https://x.com/mervenoyann/status/2030017555830546762

#modelextraction #distillationattack #aisecurity #adversarialml

European researchers report that poetic prompts can bypass safety guardrails in multiple LLMs, exposing gaps in classifier-based moderation.

A good reminder that safety systems must evolve alongside generative models - especially as adversarial creativity becomes easier to automate.

What direction should improvements take?

Source: https://www.wired.com/story/poems-can-trick-ai-into-helping-you-make-a-nuclear-weapon/

Follow us for more neutral and security-focused AI updates.

#AISafety #LLMSecurity #AdversarialML #CyberSecurity #MLResearch #TechNadu

Adversarial machine learning
https://en.wikipedia.org/wiki/Adversarial_machine_learning

* study of attacks on machine learning algorithms, & defenses against such attacks

#AI #ML #MachineLearning #ComputerSecurity #AIsafety #AdversarialAttacks #AdversarialML

This was another great ep following the one with Simon Willison about finding the boundaries of LLMs https://oxide-and-friends.transistor.fm/episodes/adversarial-machine-learning

#podcast #machineLearning #LLM #adversarialML

If you build and maintain a database of "fingerprints" of adversarial attacks, you can estimate which kind is being used against your model in real time. This tells you both about the technical sophistication of your adversary, and the strength of possible adversarial defenses.

Learn more at https://adversarial-designs.shop/blogs/blog/know-thy-enemy-classifying-attackers-with-adversarial-fingerprinting

#ThreatIntelligence #AdversarialML