Chain-of-Thought Driven Adversarial Scenario Extrapolation for Robust Language Models
This addresses safety vulnerabilities like jailbreaks and toxicity in LLMs for users and developers, representing a new paradigm rather than an incremental improvement.
The paper tackles the problem of safety risks in Large Language Models (LLMs) by introducing Adversarial Scenario Extrapolation (ASE), a framework that uses Chain-of-Thought reasoning to enhance robustness and seamlessness, achieving near-zero jailbreak attack success rates, minimal toxicity, and <4% outright rejections while outperforming existing defenses.
Large Language Models (LLMs) exhibit impressive capabilities, but remain susceptible to a growing spectrum of safety risks, including jailbreaks, toxic content, hallucinations, and bias. Existing defenses often address only a single threat type or resort to rigid outright rejection, sacrificing user experience and failing to generalize across diverse and novel attacks. This paper introduces Adversarial Scenario Extrapolation (ASE), a novel inference-time computation framework that leverages Chain-of-Thought (CoT) reasoning to simultaneously enhance LLM robustness and seamlessness. ASE guides the LLM through a self-generative process of contemplating potential adversarial scenarios and formulating defensive strategies before generating a response to the user query. Comprehensive evaluation on four adversarial benchmarks with four latest LLMs shows that ASE achieves near-zero jailbreak attack success rates and minimal toxicity, while slashing outright rejections to <4%. ASE outperforms six state-of-the-art defenses in robustness-seamlessness trade-offs, with 92-99% accuracy on adversarial Q&A and 4-10x lower bias scores. By transforming adversarial perception into an intrinsic cognitive process, ASE sets a new paradigm for secure and natural human-AI interaction.