PLAGUE Framework Pushes Multi-Turn Attack Success on LLMs

Large Language Models (LLMs) like OpenAI's o3 and Claude's Opus 4.1 have become central to executing complex tasks via multi-turn dialogues. However, their susceptibility to multi-turn attacks remains a pressing concern. Enter PLAGUE, the latest framework promising to elevate attack efficiency to unprecedented levels.

Rethinking Multi-Turn Attacks

The paper, published in Japanese, reveals PLAGUE's strategic approach. By dissecting multi-turn attacks into three distinct phases, Primer, Planner, and Finisher, PLAGUE systematically navigates the intricacies of LLM vulnerabilities. Notably, its design is inspired by lifelong-learning agents, offering a dynamic adaptation to evolving contexts.

What the English-language press missed: the benchmark results speak for themselves. PLAGUE showcases an attack success rate (ASR) improvement by more than 30% compared to existing methods. Specifically, it achieves an ASR of 81.4% on OpenAI's elusive o3 model and 67.3% on Claude's Opus 4.1. These results are remarkable, considering these models' reputations for resisting jailbreaking attempts.

Why PLAGUE Matters

So why should readers care about a framework for attacking LLMs? For starters, the rise of agentic workflows means that LLMs are playing larger roles in automating complex tasks. The implications of a successful attack could ripple across industries reliant on these models for efficiency and productivity.

Compare these numbers side by side, and it's clear that PLAGUE isn't just about breaking systems for sport. It's a critical tool for stress-testing the very systems we trust with sensitive information. As models become more ingrained in decision-making processes, understanding their vulnerabilities isn't merely academic, it's essential for securing technological progress.

The Road Ahead for LLM Security

Western coverage has largely overlooked this shift in attack methodology. The data shows that while single-turn attack vulnerabilities have been extensively studied, the multi-turn landscape remains ripe for exploration. PLAGUE's structured approach might just be the key to unraveling these complex vulnerabilities.

In a world where AI systems are increasingly entrusted with sensitive tasks, ensuring their security is non-negotiable. PLAGUE sets a new standard for understanding and addressing LLM vulnerabilities. The question now isn't whether these models can be cracked, but how quickly security measures can evolve to keep pace.