📰 Originally published on Securityelites — AI Red Team Education — the canonical, fully-updated version of this article.
⚠️ Critical Legal Notice: Testing ChatGPT, the OpenAI API, or any OpenAI product without explicit written authorisation violates OpenAI’s Terms of Service and potentially computer fraud laws in your jurisdiction. The ONLY authorised path to researching ChatGPT security is through OpenAI’s official bug bounty programme on HackerOne. This guide covers the methodology for authorised research only. All exercises use local models or authorised practice platforms — never ChatGPT directly unless you have active HackerOne scope authorisation.
The most-searched phrase in AI security right now is some variant of “how to hack ChatGPT.” I understand the appeal — ChatGPT is the most widely deployed AI system in history, it’s on every professional’s radar, and finding a meaningful security vulnerability in it would be career-defining research. The problem is that most people asking that question haven’t thought through what it actually means to research ChatGPT security ethically and legally.
Unauthorised testing of ChatGPT is a Terms of Service violation and potentially a criminal offence under computer fraud laws. OpenAI has a dedicated trust and safety team, and researchers who probe their systems without authorisation get noticed. The right way to research ChatGPT security — the way that gets you paid, builds your reputation, and doesn’t put you at legal risk — is through their bug bounty programme, with a methodology that produces evidence that their security team can act on.
This guide is for people who want to research ChatGPT security properly. Here’s how authorised research actually works.
🎯 What This Research Guide Covers
What you can legally test on ChatGPT and through what channel
The 5-stage authorised assessment methodology for ChatGPT security research
Custom GPT security testing — the richest authorised attack surface available
Enterprise ChatGPT API deployment assessment — where the critical findings are
How to write a ChatGPT bug bounty report that gets triaged and paid
⏱ 25 min read · 3 exercises included What You Need: Local Ollama setup with llama3.1 (for practice exercises) · A HackerOne account (free) · Python + requests for API testing · Read the vulnerabilities breakdown first — knowing what you’re looking for makes the methodology much more productive ### How to Hack ChatGPT — Complete Authorised Research Guide 1. The Legal Framework — What You Can Actually Test 2. Stage 1 — Reconnaissance Without Touching ChatGPT 3. Stage 2 — Custom GPT Security Testing 4. Stage 3 — Enterprise API Deployment Assessment 5. Stage 4 — Feature-Specific Testing 6. Stage 5 — Writing a Report That Gets Paid This methodology builds directly on the vulnerability categories documented in the ChatGPT security vulnerabilities breakdown. The practical techniques connect to the 6-stage LLM hacking methodology from the LLM hacking tutorial — many of the same stages apply, adapted for ChatGPT’s specific architecture. Everything is indexed in the AI Elite Hub alongside the broader prompt injection research in the prompt injection explained guide.
The Legal Framework — What You Can Actually Test
Before anything technical: the authorisation question. ChatGPT has three categories of testing targets with different authorisation requirements:
Fully authorised without additional steps: Local models you run yourself (Ollama, LM Studio). Practice platforms explicitly built for security testing (Gandalf, HackAPrompt). Your own Custom GPTs if you built them — you own the configuration and can test it however you like.
Authorised through OpenAI’s HackerOne programme: ChatGPT.com, ChatGPT API, Custom GPTs built by others, the GPT Store ecosystem. You need to apply for HackerOne access to the OpenAI programme and stay within their published scope. The application is free and approval is typically straightforward for researchers who can demonstrate legitimate intent.
Not authorised under any circumstances: OpenAI’s internal infrastructure, employee accounts, training data systems, OpenAI corporate networks. These are explicitly out of scope and testing them is a criminal matter, not a bug bounty matter.
The Custom GPT category is where I’d point most researchers starting out. Custom GPTs built by third parties are in scope for OpenAI’s programme, and they represent the richest testing surface for several reasons: they have operator-defined system prompts (extraction research), they vary enormously in security posture (easy to find poorly configured ones), and the findings translate directly to demonstrating real-world business risk in a way that abstract model-level findings don’t.
Stage 1 — Reconnaissance Without Touching ChatGPT
The most valuable recon on ChatGPT happens before you send a single message. I spend the first session of any ChatGPT security research doing passive reconnaissance that shapes every subsequent test.
CHATGPT RECON — PASSIVE INFORMATION GATHERING Copy
OSINT on Custom GPT deployments — find exposed configs
site:chatgpt.com/g/ “system prompt” OR “instructions”
GitHub search for exposed OpenAI API keys
site:github.com “OPENAI_API_KEY” OR “sk-proj-” language:python
Search for publicly shared ChatGPT Custom GPT configurations
site:github.com “custom gpt” “system prompt” filetype:txt OR filetype:md
Find enterprise ChatGPT deployments with exposed API endpoints
site:target.com “powered by ChatGPT” OR “openai.com”
Always verify any finding before considering reporting — OSINT only at this stage
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