SecRespond: Benchmarking AI Agents for Post-Compromise Incident Response
Researchers have unveiled SecRespond, the inaugural benchmark designed to assess Large Language Model (LLM) agents in the context of post-compromise incident-response workflows. Current cybersecurity benchmarks primarily target pre-compromise situations, leaving a significant gap in post-compromise analysis. SecRespond tasks agents with examining forensic disk snapshots from compromised hosts, alongside alerts, vulnerability scans, and baseline checks from security products. Agents are expected to generate forensic reports detailing intrusions, baseline risks, and vulnerability threats, in addition to proposing a remediation strategy. This benchmark is applied across 10 cyber ranges, each engineered to replicate authentic post-compromise scenarios. This initiative fills a crucial void in evaluating LLM agents for practical security operations involving host artifacts and command-line interfaces.
Key facts
- SecRespond is the first benchmark for post-compromise incident response using LLM agents.
- Existing benchmarks focus on pre-compromise settings.
- Agents analyze forensic disk snapshots, alerts, vulnerability scans, and baseline checks.
- Agents must produce forensic reports and a remediation plan.
- The benchmark is instantiated across 10 cyber ranges.
- LLM agents are increasingly used in real-world security operations.
- The benchmark assesses agents' ability to handle host artifacts and CLIs.
- The work was published on arXiv (2607.26791).
Entities
Institutions
- arXiv