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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2605.21956 |
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| _version_ | 1866916035139993600 |
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| author | Mittelsteadt, Matt Kraprayoon, Jam Staes-Polet, Robin Galeev, Oskar Wehner, Jan Covino, Christopher Ee, Shaun |
| author_facet | Mittelsteadt, Matt Kraprayoon, Jam Staes-Polet, Robin Galeev, Oskar Wehner, Jan Covino, Christopher Ee, Shaun |
| contents | Artificial Intelligence (AI) agents can now orchestrate cyberattacks. This development is already increasing the speed and scale of cyber attacks, decreasing attack costs, and improving the operational autonomy of cyber capabilities. To defend against these emerging threats, actors must first develop the capability to detect them. This report frames the offensive cyber agent detection challenge by outlining the coming detection gap between offensive cyber agents and traditional cyber capabilities; introducing detection-in-depth, a strategic framework to guide policymakers and defenders responding to this detection gap; and presents five actionable detection mechanisms to support policymakers, industry, and defenders when putting this strategic framework into practice. These include (1) Agent Identifiers for Critical Infrastructure,(2) Agent Honeypots; (3) AI-Automated Alert Analysis and Triage: systems that use AI to filter, prioritize, and interpret the growing volume of detection signals expected from autonomous cyber operations; (4) An Agentic Security Alert Standard: A reporting standard model that providers can use to communicate agentic threats, improving the speed, consistency, and actionability of reports; (5) An Agentic Cybersecurity Exchange (ACE): an institution modeled on the Global Signal Exchange that brings together model and cloud providers to detect offensive cyber agent threats at their origin point and coordinate ecosystem-wide agentic threat disruption. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21956 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Detecting Offensive Cyber Agents: A Detection-in-Depth Approach Mittelsteadt, Matt Kraprayoon, Jam Staes-Polet, Robin Galeev, Oskar Wehner, Jan Covino, Christopher Ee, Shaun Computers and Society Artificial Intelligence (AI) agents can now orchestrate cyberattacks. This development is already increasing the speed and scale of cyber attacks, decreasing attack costs, and improving the operational autonomy of cyber capabilities. To defend against these emerging threats, actors must first develop the capability to detect them. This report frames the offensive cyber agent detection challenge by outlining the coming detection gap between offensive cyber agents and traditional cyber capabilities; introducing detection-in-depth, a strategic framework to guide policymakers and defenders responding to this detection gap; and presents five actionable detection mechanisms to support policymakers, industry, and defenders when putting this strategic framework into practice. These include (1) Agent Identifiers for Critical Infrastructure,(2) Agent Honeypots; (3) AI-Automated Alert Analysis and Triage: systems that use AI to filter, prioritize, and interpret the growing volume of detection signals expected from autonomous cyber operations; (4) An Agentic Security Alert Standard: A reporting standard model that providers can use to communicate agentic threats, improving the speed, consistency, and actionability of reports; (5) An Agentic Cybersecurity Exchange (ACE): an institution modeled on the Global Signal Exchange that brings together model and cloud providers to detect offensive cyber agent threats at their origin point and coordinate ecosystem-wide agentic threat disruption. |
| title | Detecting Offensive Cyber Agents: A Detection-in-Depth Approach |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2605.21956 |