> For the complete documentation index, see [llms.txt](https://dev-angelist.gitbook.io/ai-ml-pentest/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dev-angelist.gitbook.io/ai-ml-pentest/introduction.md).

# Introduction

## Topics <a href="#topics" id="topics"></a>

> 1. [OWASP](/ai-ml-pentest/owasp-and-llm.md)[ and LLM](/ai-ml-pentest/owasp-and-llm.md)
> 2. [OWASP Top 10 for LLM App - 2025](/ai-ml-pentest/owasp-top-10-for-llm-app-2025.md)
> 3. [Labs](/ai-ml-pentest/labs.md)

***

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{% embed url="<https://cloud.google.com/learn/artificial-intelligence-vs-machine-learning?hl=en>" %}

## Certifications

* [Certified AI/ML Pentester (C-AI/MLPen) - TheSecOps Cert](https://secops.group/product/certified-ai-ml-pentester/)

## Other Resources

* [Certified AI/ML Pentester (C-AI/MLPen) - TheSecOps Cert](https://secops.group/product/certified-ai-ml-pentester/)
* [GenAI OWASP](https://genai.owasp.org/)
* [OWASP Top 10 for Large Language Model Applications ](https://owasp.org/www-project-top-10-for-large-language-model-applications/)
* [OWASP Top 10 for LLM Applications 2025](https://genai.owasp.org/resource/owasp-top-10-for-llm-applications-2025/)
* [LLM-Pentesting-Resources](https://github.com/R3DLB/LLM-Pentesting-Resources/tree/main)
* [Offensive ML Playbook](https://wiki.offsecml.com/Welcome+to+the+Offensive+ML+Playbook)
* [Prompt Engineering](https://learnprompting.org/docs/introduction)
* [Prompt Injection - IBM](https://www.ibm.com/topics/prompt-injection)
* [LLM Security](https://llmsecurity.net/)
* [Threat Modeling LLM Applications - AI Village](https://aivillage.org/large%20language%20models/threat-modeling-llm/)
* [Payloads for Attacking Large Language Models (PALLMs)](https://github.com/mik0w/pallms)
* [Awesome LLM Security](https://github.com/corca-ai/awesome-llm-security)
* [Adversarial Prompting in LLMs](https://www.promptingguide.ai/risks/adversarial)
* [Prompt Injection Attacks - Cobalt](https://www.cobalt.io/blog/prompt-injection-attacks)
* [AI vulnerability deep dive - Bugcrowd](https://www.bugcrowd.com/blog/ai-vulnerability-deep-dive-prompt-injection/)
* [Prompt Hacking and Misuse of LLMs - Unite AI](https://www.unite.ai/prompt-hacking-and-misuse-of-llm/?trk=article-ssr-frontend-pulse_little-text-block)
* [MITRE Atlas](https://atlas.mitre.org/matrices/ATLAS/)
* [Planning red teaming for LLMs and their app - Microsoft](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/red-teaming)
* [NVIDIA AI Red Team: An Introduction](https://developer.nvidia.com/blog/nvidia-ai-red-team-an-introduction/)
* [Awesome Machine Learning for Cyber Security](https://github.com/jivoi/awesome-ml-for-cybersecurity)

### Video Resources

* [Proof of Inference: Verifying the Integrity of Machine Learning Model Predictions](https://www.youtube.com/watch?v=o_ftHUCMSsE) 🇮🇹
* [Hackerare un Large Language Model: un tentativo di Explainable AI (XAI) / Zimuel e Gianfagna](https://www.youtube.com/watch?v=pxtGLvnHQdM) 🇮🇹

## [Labs](/ai-ml-pentest/labs.md) 🔬

## Software

* [BITE](https://github.com/INK-USC/BITE) Textual Backdoor Attacks with Iterative Trigger Injection
* [garak](https://github.com/leondz/garak/) LLM vulnerability scanner
* [HouYi](https://github.com/LLMSecurity/HouYi) successful prompt injection framework
* [dropbox/llm-security](https://github.com/dropbox/llm-security) demo scripts & docs for LLM attacks
* [promptmap](https://github.com/utkusen/promptmap) bulk testing of prompt injection on openai LLMs
* [rebuff](https://github.com/protectai/rebuff) LLM Prompt Injection Detector


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