Same Request, Different Boundary: Evaluating Cybersecurity Assistance across Conversational Contexts
Quick summary
arXiv:2609.00578v1 Announce Type: new 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, Repetitio
Key takeaways
- arXiv:2609.00578v1 Announce Type: new 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.
Why it matters
“Same Request, Different Boundary: Evaluating Cybersecurity Assistance across Conversational Contexts” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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