Same Request, Different Boundary: Evaluating Cybersecurity Assistance across Conversational Contexts

2026-08-31

AI Summary

研究通过3R-Bench评估了对话上下文对LLM处理网络安全请求的影响，旨在防止恶意使用同时不拒绝合法帮助。

Abstract

Large Language Models (LLMs) can solve complex problems, but their misuse in high-risk domains can lead to severe consequences. Model providers therefore restrict assistance for potentially harmful requests. Refusing all cybersecurity requests would therefore harm legitimate users. Providers need a mechanism to block malicious use without denying legitimate assistance to defenders. Existing cybersecurity-specific datasets evaluate this mechanism, but none considers the conversational context of a request. We introduce 3R-Bench (Refusal, Repetition, and Revision), a benchmark of 150 real-world cybersecurity requests augmented with two adversarial conversational settings, and evaluate eight LLMs on it. Prior assistant behavior strongly changes responses to an unchanged request: among 376 available pairs from a 400-pair panel, compliance rises from 62.0% after refused history to 85.1% after accepted history. The opposite pattern appears under dialogue decomposition. In comparison, compliance falls from 501/800 direct responses to 172/800 after dialogue; among 738 pairs returning model-authored text in both conditions, the decrease is 45.1 points. Failure feedback recovers only a small fraction of this loss.

Problem

• cybersecurity
• large language models
• conversational context
• malicious use
• legitimate assistance

Innovation

• 3R-Bench
• conversational context
• cybersecurity requests