Part of Learn LLM Prompting With Hundreds of Examples
Blueprint
Safety checkedLearn From Reverse Engineering Research
Technical articles on how GPTs work, prompt injection attacks, and LLM system prompt discovery.
What problem does this solve?
Understanding how LLMs are actually built requires access to research explaining real extraction techniques and system vulnerabilities. This collection of articles covers reverse engineering, ChatGPT sandbox capabilities, memory mechanisms, and lessons from analyzing 1,000+ GPTs.
How does it work?
- Read the flagship article 'A Tale of Reverse Engineering 1001 GPTs' (REcon 2024 talk) for high-level reverse engineering findings. 2. Study specific technical docs on ChatGPT sandbox Python/Linux packages and how the memory/bio tool works. 3. Cross-reference with ArXiv citations for peer-reviewed research on prompt robustness and instruction leakage. Output: deep understanding of how GPTs are built and what their boundaries are.
Included Skills
No skills in this group
Key Features
REcon 2024 Talk
Video and writeup on reverse engineering OpenAI's 1,000+ custom GPTs, security findings, and ethical implications.
Sandbox Analysis
Complete inventory of Python packages and Linux system packages installed in ChatGPT's code interpreter.
Memory & Bio Deep Dive
Technical explanation of how OpenAI's 'bio' tool persists memory across conversations.
Academic Citations
Links to ArXiv papers on prompt robustness, stealing attacks, and responsible prompt engineering.
About This Blueprint
- Industry
- Technology
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