Reimagining service delivery in the agentic era with Google Public Sector
cloud.google.com·23h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
PLAGUE: Plug-and-play framework for Lifelong Adaptive Generation of Multi-turn Exploits
TL;DR
Large Language Models (LLMs) are increasingly vulnerable to jailbreaking, particularly in multi-turn dialogues where harmful intent can be subtly introduced. PLAGUE is a new framework designed for the lifelong adaptive generation of multi-turn exploits, allowing for the testing of LLMs against these vulnerabilities.
✦ Why It Matters
Engineers can use PLAGUE to proactively identify and address vulnerabilities in LLMs during development.
Key Takeaways
- Utilize PLAGUE for structured multi-turn attack generation.
- Focus on context optimization to improve attack effectiveness.
- Evaluate LLMs with PLAGUE to identify vulnerabilities.
- Leverage insights from PLAGUE to enhance security protocols.
How It Works
PLAGUE operates in three phases: Primer initializes the attack context, Planner strategizes the attack sequence, and Finisher executes the final exploit. This systematic approach allows for a comprehensive exploration of potential vulnerabilities in LLMs, adapting to the dialogue's flow.