AI/ML/LLM Penetration Testing Toolkit Overview
The AI/ML/LLM Penetration Testing Toolkit by Mr-Infect is a specialized resource for offensive and defensive security in AI, machine learning, and large language models. It targets vulnerabilities in systems like ChatGPT, Claude, and LLaMA, providing tools and techniques for cybersecurity engineers, red teamers, AI researchers, and ethical hackers. The repository is structured to address critical threats in AI security, including prompt injection, data leaks, model poisoning, and supply chain attacks.
Developed since 2025, this toolkit emphasizes practical applications for AI penetration testing, aligning with the evolving risks in sectors such as finance, healthcare, legal, defense, and national infrastructure. Its focus on adversarial ML techniques ensures that users can simulate real-world attack scenarios to identify and mitigate weaknesses in AI-driven systems.
How It Works
Users can explore AI, ML, and LLM fundamentals, including distinctions between AI, ML, and LLMs, as well as the lifecycle of LLMs from problem definition to deployment. Key technical concepts such as tokenization and vectorization are also covered, providing a foundation for understanding how LLMs process input.
- Jailbreaking & Output Overwriting: Methods to manipulate LLM responses, such as overriding system prompts or exploiting plugin vulnerabilities.
- Sensitive Information Leakage: Exploits targeting the extraction of personally identifiable information (PII), trade secrets, or source code.
- Model Weight Poisoning: Adversarial training methods to corrupt model behavior during or after deployment.
Use Cases
The toolkit is primarily used for offensive security testing, enabling red teamers and security researchers to identify vulnerabilities in AI systems before malicious actors exploit them.
- Prompt Injection Testing: Evaluating an LLM’s resilience to jailbreaking attempts, such as bypassing safety filters or extracting unauthorized data through crafted prompts.
- Adversarial ML Assessments: Simulating data poisoning attacks to test model robustness during training or inference phases.
- Plugin and API Abuse Detection: Assessing risks associated with third-party integrations, including unauthorized API calls or malicious plugin execution.
- Hallucination and Bias Analysis: Testing LLMs for generating misleading or biased outputs, which can have ethical and operational implications.
It also references curated payload libraries, including collections of jailbreak prompts and adversarial examples, to facilitate practical testing scenarios.
Key Features & Capabilities
The repository provides a comprehensive collection of offensive AI pentesting tools and frameworks, including:
- PIPE (Prompt Injection Primer for Engineers): A resource for understanding and executing prompt injection attacks.
- ChatGPT Red Team Ally: Tools and scripts designed to assist red teamers in testing ChatGPT’s security posture.
- Lakera Gandalf: An interactive playground for testing prompt injection attacks in real time.
- L1B3RT45: A collection of jailbreak prompts for testing LLM security controls.
Additionally, the toolkit includes references to external research and case studies, such as:
- Prompt Injection & Jailbreaking: Technical guides and exploit demonstrations, including PDF-based injection techniques and Unicode evasion methods.
- Model Poisoning & Supply Chain Attacks: Analyses of data corruption attacks and compromised dependencies in ML pipelines.
- Hallucination and Bias: Studies on mitigating misleading or biased LLM responses, including ethical considerations for user communication.
The toolkit also encourages community contributions, allowing users to fork the repository, develop new features, and submit pull requests to enhance its capabilities. This collaborative approach ensures that the toolkit remains up-to-date with emerging threats and defensive strategies in AI security.