Building Trustworthy Cyber Agent Evaluations

Recent LLM-based agents are moving from isolated cybersecurity reasoning toward interactive offensive workflows. They can inspect codebases, reproduce vulnerabilities, generate working exploits, and operate inside tool-rich environments. This makes cyber capability evaluation a central AI safety question.

A realistic cyber attack is not a single exploit attempt. It involves discovering web-facing attack surfaces, obtaining an initial foothold, gathering internal information, and expanding compromise across internal systems. Benchmarks that only measure CTF solving or vulnerability reproduction are useful, but they do not fully capture this end-to-end workflow.

My current work focuses on evaluation pipelines that make this behavior measurable and auditable: realistic cyber ranges, reproducible harnesses, automatic result verification, and careful analysis of how prompts, hints, tools, and self-evolution alter measured capability.

This blog will collect short notes on cybersecurity agents, frontier AI safety, benchmark design, and trustworthy evaluation.