Artificial intelligence systems like large language models (LLMs) are increasingly deployed in production environments, yet their security posture remains an evolving challenge. Understanding how to probe and defend these models requires both novel offensive techniques and structured frameworks to categorize risks. The AI-penetration-testing repository by Mr-Infect offers a curated knowledge base that aggregates the latest attack vectors, payload libraries, and research papers to help security professionals navigate this complex landscape.
What the AI-penetration-testing repository offers
This repo is not a software tool or framework you install and run; rather, it’s a comprehensive curated collection of resources focused on AI, machine learning, and LLM security research and penetration testing. It covers the entire attack surface around generative AI systems, including prompt injection, jailbreaking, model and data poisoning, vector store attacks, supply chain risks, and denial-of-service via token abuse.
At its core, it organizes content around the OWASP LLM Top 10 (2024) — a framework that defines the most critical risks for LLMs in production. This provides a structured way to approach testing and research. The repository links to offensive AI testing frameworks such as MITRE ATLAS, Lakera Gandalf, and AI Goat, which are tools and platforms designed for adversarial evaluation of language models.
The stack is essentially a knowledge compilation, comprising:
- Prompt injection payload libraries illustrating various evasion and bypass techniques
- Adversarial machine learning research papers covering poisoning and evasion attacks
- Links to offensive AI testing tools and frameworks for practical red teaming
- Documentation of attack patterns, including novel vectors using Unicode, emojis, multi-modal inputs like images, audio, and PDFs
This makes the repo a centralized starting point for security engineers and AI researchers who want to understand or expand their offensive and defensive capabilities around LLMs like ChatGPT, Claude, and LLaMA.
Why the prompt injection payload library stands out
The real technical strength of this repository lies in its prompt injection payload techniques section. Prompt injection has become one of the most prominent attack vectors against LLMs, where attackers manipulate the input prompt to bypass restrictions or cause the model to perform unintended actions.
This repo aggregates payloads that showcase evasion strategies such as using Unicode homoglyphs, emoji obfuscation, and multi-modal inputs. For example, attackers may embed instructions within images or audio files that LLMs processing multi-modal inputs could interpret and execute. Another notable pattern is the “ignore previous instructions” payload, which tries to reset or override the model’s internal context or safety guardrails.
These payloads are not theoretical; they have practical implications for red teams testing AI deployments. Having a curated, categorized collection accelerates the pentesting cycle and reduces the guesswork in crafting effective inputs that probe model weaknesses.
The tradeoff here is that while the library is extensive, it requires human expertise to adapt these payloads to specific target models and deployment scenarios. There is no turnkey automation, reflecting the early stage of offensive AI tooling.
Explore the project
Since this repository is a curated knowledge base, it does not provide installation or setup commands. Instead, the best way to engage with it is:
- Start with the README, which outlines the OWASP LLM Top 10 framework and links to each risk category
- Dive into the prompt injection payloads folder to review real-world examples of attack inputs
- Explore the linked papers in adversarial ML to understand the academic foundations of model poisoning and evasion
- Check out references to offensive AI testing frameworks like MITRE ATLAS and Lakera Gandalf for practical toolsets
Navigating the repo this way helps build a mental model of the AI threat landscape and provides concrete payloads and strategies to test your own AI systems.
Verdict
This repository is a valuable resource for cybersecurity engineers, AI researchers, and red teamers focused on AI/ML security. It excels as a curated, up-to-date reference that compiles the fragmented knowledge around AI penetration testing into one place.
However, it is not a software tool or automated scanner, so expect to invest effort in adapting the payloads and research to your environment. The lack of turnkey automation reflects the nascent state of offensive AI tooling but does not diminish the practical utility of the curated content.
If you are responsible for securing LLMs or conducting adversarial testing on AI systems, this repo is worth bookmarking and diving into. Its structured approach using the OWASP LLM Top 10 framework and the detailed prompt injection payloads provide a foundation for both offensive experimentation and defensive hardening.
→ GitHub Repo: Mr-Infect/AI-penetration-testing ⭐ 233