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SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response

First seen · 7/30/2026, 12:00 PMLatest activity · 7/30/2026, 12:00 PM

SecRespond introduces a benchmark for evaluating LLM agents in post-compromise incident response, rather than in clean pre-attack environments. Agents receive a forensic disk snapshot from a compromised host alongside security-product alerts, vulnerability scans, and baseline checks. They must produce forensic reports covering intrusions, baseline risks, and vulnerability risks, plus a remediation plan. The benchmark contains 10 cyber ranges spanning four entry-point types, 21 ATT&CK techniques, and five operating systems. Across 23 frontier models evaluated with the OpenCode harness, agents reliably found alert-exposed issues but struggled with proactive disk investigation and comprehensive, verified remediation; no model fully detected and remediated any range.

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Reporting Timeline

  1. AggregatorarXiv7/29, 07:32 PMnot independent
    SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response
  2. AggregatorHuggingFace Daily Papers7/30, 12:00 PMnot independentRepresentative
    SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response