LLm security? AI module hacking? ai governance? like courses and practical videos and/or labs
#What are the best resources for AI security learning? hands on labs etc? llm, prompt injection?
limber vine
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Hi @limber vine. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
The knowledge sources only partially cover what you’re asking; they mention some security-related topics (like prompt injection and guardrails) and some general learning resources, but they do not provide a comprehensive list of hands‑on AI security / LLM security courses or labs. I’ll summarize what is available in them and where it touches your topics.
1. OpenAI Academy (general AI literacy, including responsible use)
OpenAI runs OpenAI Academy, a free learning hub with workshops, on‑demand videos, and community discussions about using AI responsibly and effectively. It’s aimed at educators and a broad audience, but can be a good starting point for governance, responsible use, and general AI literacy. [OpenAI Academy]
“OpenAI Academy is a free, publicly accessible learning hub built to boost AI literacy and give anyone… the practical skills to use artificial intelligence responsibly and effectively.” [OpenAI Academy]
You can browse courses and live sessions at:
https://academy.openai.com/
2. Official docs on LLM / agent security & prompt injection
These are not “courses,” but they are practical, security‑focused docs with concrete patterns and mitigations:
- Safety best practices – covers moderation, adversarial testing, prompt injection risk, human‑in‑the‑loop, constraining inputs/outputs, etc. [Safety best practices]
- Agent Builder safety – focused on agents, prompt injection, private data leakage, structured outputs, and tool approvals. [Agent safety]
- Computer Use: risks and safety – prompt injection via web pages/screenshots, safety checks, allowlists/blocklists, human‑in‑the‑loop. [Computer use safety]
- Connectors & MCP: risks and safety – prompt injection, untrusted MCP servers, approvals, logging, and data‑sharing review. [MCP safety]
- Deep research: safety risks and mitigations – detailed prompt‑injection + data‑exfiltration example and mitigations (staging workflows, schema validation, trusted MCPs, etc.). [Deep research safety]
These are good “design docs” if you want to build your own labs or exercises around LLM security, prompt injection, and data exfiltration.
3. Guardrails & prompt‑injection–related examples
The OpenAI Cookbook has a practical notebook on LLM guardrails, including prompt‑injection considerations:
- How to implement LLM guardrails – shows input and output guardrails, including:
- Input guardrails for prompt injection and jailbreaking detection. [Guardrails overview]
- Discussion of limitations: LLM‑based guardrails can share the same vulnerabilities as the base model, including prompt injection. [Input guardrails]
This is code‑oriented and can be turned into hands‑on labs (e.g., build a topical guardrail, then try to bypass it with injection attempts).
4. Community discussion on prompt injection
There’s a community forum thread that explicitly frames prompt injection as a “hack this prompt” challenge, which you could adapt into your own lab:
- “Challenge: Hack this prompt!” – community post about designing prompts resistant to prompt injection, with a challenge to escape the prompt and leak a secret. [Prompt injection challenge]
This isn’t a structured course, but it’s directly aligned with “LLM security” and “prompt injection” from a practical, adversarial perspective.
5. Prompt‑engineering resources (not security‑specific, but useful background)
The docs link to several prompting guides and tools that can help you understand how prompts work, which is foundational for understanding prompt injection and jailbreaks:
- Prompting guides and libraries listed in the Cookbook “Related resources” section (e.g., Brex’s Prompt Engineering Guide, promptingguide.ai, etc.). [Prompting guides; Prompting tools]
These are not framed as “security” courses, but they’re useful to understand the mechanics you’re trying to defend.
6. What’s not in the sources
The provided knowledge sources do not list:
- Dedicated “LLM security” or “AI red‑teaming” courses with labs.
- Formal “AI governance” courses or certifications.
- Hands‑on “AI model hacking” platforms (CTFs, lab environments, etc.).
So I can’t point you to specific named courses or lab platforms beyond OpenAI Academy and the docs/cookbook content above.
If you want to build your own hands‑on path using only what’s in these sources, a rough progression could be:
- Use OpenAI Academy for general responsible‑use / governance context. [OpenAI Academy]
- Study Safety best practices and Agent/Computer Use/MCP/Deep Research safety docs to understand real risk patterns and mitigations. [Safety best practices; Deep research safety]
- Work through the guardrails Cookbook example and adapt it into your own experiments (e.g., try to bypass your own guardrails with prompt injection). [Guardrails overview]
- Use the community prompt‑injection challenge as inspiration for adversarial exercises. [Prompt injection challenge]