analiza aiqbench
This study establishes the first evaluation framework for autonomous defense agents configuring commercial EDR systems, revealing a critical sim-to-real gap where telemetry is optimized for SOC workflows rather than scientific benchmarking. The research underscores the necessity of per-policy attribution to distinguish between agent actions and the EDR's inherent autonomous behavior, which can fluctuate during evaluation windows. These findings provide essential methodological guidance for accurately benchmarking black-box security tools in enterprise environments.
Closing the Sim-to-Real Gap: An Evaluation Framework for Autonomous Cyber Defense Configuration of Commercial EDR
arXiv:2606.08168v1 Announce Type: cross Abstract: Leading commercial endpoint detection and response (EDR) products have shifted from operator-configured rule sets to multi-component systems where autonomous AI components operate alongside, and increasingly in place of, operator-deployed policies. Autonomous defense agents using commercial EDR as their hardening tool are no longer tuning a passive tool, but a black-box autonomous system capable of making vendor-specific decisions. We present the first evaluation framework for autonomous defense agents hardening commercial EDR. We instantiate it in a Game of Active Directory (GOAD) lab with Horizon3.ai's NodeZero as the autonomous pentester and Microsoft Defender XDR as the EDR. We run a sample benchmark of defense agents with two large language model (LLM) backbones (Claude Sonnet 4.6 and Cisco Foundation-Sec-8B). We report three lessons learned that neither simulation nor open-source-EDR evaluation can surface: (i) commercial EDR telemetry is engineered for Security Operations Center (SOC) analyst workflows rather than scientific benchmarking; (ii) the importance of per-policy attribution to separate defense agent actions from autonomous EDR actions; and (iii) the EDR's autonomous behavior varies during the evaluation window. Together, these findings highlight a sim-to-real gap for enterprise defense and motivate evaluation methodology for benchmarking autonomous defense agents in environments with black-box, autonomous tools.
Abstract:Leading commercial endpoint detection and response (EDR) products have shifted from operator-configured rule sets to multi-component systems where autonomous AI components operate alongside, and increasingly in place of, operator-deployed policies. Autonomous defense agents using commercial EDR as their hardening tool are no longer tuning a passive tool, but a black-box autonomous system capable of making vendor-specific decisions. We present the first evaluation framework for autonomous defense agents hardening commercial EDR. We instantiate it in a Game of Active Directory (GOAD) lab with this http URL's NodeZero as the autonomous pentester and Microsoft Defender XDR as the EDR. We run a sample benchmark of defense agents with two large language model (LLM) backbones (Claude Sonnet 4.6 and Cisco Foundation-Sec-8B). We report three lessons learned that neither simulation nor open-source-EDR evaluation can surface: (i) commercial EDR telemetry is engineered for Security Operations Center (SOC) analyst workflows rather than scientific benchmarking; (ii) the importance of per-policy attribution to separate defense agent actions from autonomous EDR actions; and (iii) the EDR's autonomous behavior varies during the evaluation window. Together, these findings highlight a sim-to-real gap for enterprise defense and motivate evaluation methodology for benchmarking autonomous defense agents in environments with black-box, autonomous tools.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2606.08168 [cs.CR]
(or arXiv:2606.08168v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2606.08168 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 297 cuvinte
Subiecte conexe
Cost-Aware Multi-Objective Bandits: Theory and Application to Budgeted LLM Configuration Evaluation
arXiv · 6 aug. 2026

Build agentic retrieval on a Managed Amazon Bedrock Knowledge Base with CDK
AWS · 26 oct. 2026

The Ability of Frontier Models to Control Robots is Rapidly Improving
LessWrong · 26 oct. 2026
MDArena: Evaluating Coding Agents on Realistic Molecular Dynamics Workflows
arXiv · 5 aug. 2026