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ProtoArena is a new black-box benchmark that tests AI agents on 80 cross‑component vulnerability‑exploitation environments across 11 protocol and application domains and 7 attack categories. It evaluates agents at three information levels—guided reproduction, exploitation with context, and effect‑directed black‑box exploration—using a hybrid verifier that combines rule‑based checks with LLM review of traffic and logs. In experiments, Claude Opus 4.8 achieved the highest success rates, reaching 48.8% under guided conditions and 12.5% in effect‑directed black‑box scenarios, while also demonstrating significant DNSBomb optimization performance.
Large Language Models (LLMs) have shown promise for automated penetration testing, yet existing end-to-end black-box evaluations are highly susceptible to error cascading: failures in early reconnaissance can mask an agent's actual ability to exploit vulnerabilities. To more accurately characterize these capabilities, we propose a two-stage decoupled evaluation framework that separates exploit execution from reconnaissance.
arXiv:2606. 13079v1 Announce Type: cross Abstract: Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines that frontier AI systems must not cross.
arXiv:2606. 14295v1 Announce Type: cross Abstract: Frontier AI systems are increasingly capable of cybersecurity tasks, including codebase inspection, vulnerability detection, and exploitation.
arXiv:2606. 30555v1 Announce Type: new Abstract: The rapid integration of Large Language Models (LLMs) has driven the evolution of Multi-Agent Systems (MAS), where specialized agents collaborate to execute complex workflows.
arXiv:2508.14925v2 Announce Type: replace-cross Abstract: By providing a standardized interface for LLM agents to interact with external tools, the Model Context Protocol (MCP) is quickly becoming a...