arXiv:2606.02644v1 Announce Type: cross Abstract: Agentic scaffolds have dramatically improved LLM performance on complex, long-horizon tasks, yielding both broad benefits and amplified risks in domains like cybersecurity. Existing benchmarks for AI agents in cybersecurity focus mainly on measuring proficiency--how effectively agents can complete offensive security tasks--but neglect a critical question: when and how should agents refuse harmful requests? We present the first framework for establishing refusal boundaries in offensive security contexts. Our framework defines (1) principled crit
The rapid advancement of AI agents, particularly in domains like cybersecurity, urgently necessitates frameworks to manage their ethical boundaries and refusal capabilities.
Establishing clear refusal boundaries for AI agents in sensitive areas like cybersecurity is critical for preventing misuse, managing risks, and fostering responsible AI development.
This framework shifts the focus from merely measuring AI agent proficiency to actively defining and implementing ethical guardrails for their behavior in potentially harmful contexts.
- · AI ethicists
- · Cybersecurity defense organizations
- · Regulatory bodies
- · Responsible AI developers
- · Malicious actors
- · Unregulated AI developers
- · Organizations with weak AI governance
- · Generative AI companies without ethical frameworks
The framework will guide the development of safer and more controllable AI agents for cybersecurity applications.
Increased public and institutional trust in AI agents deployed in critical infrastructure and sensitive operations.
Potential for early regulatory intervention or industry self-regulation based on the principles outlined in such frameworks, shaping the future of AI agent development standards globally.
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Read at arXiv cs.AI