Benchmark RadarAI BENCHMARK PROFILE
SecRespond
A benchmark for evaluating LLM agents on post-compromise incident-response workflows using forensic disk snapshots and host security reports across 10 cyber ranges.
- Released
- 2026-07-29
- Readiness
- Runnable
- Primary field
- Cybersecurity
Why it matters
Cybersecurity benchmarks typically focus on pre-compromise settings; this addresses the gap in evaluating agents for real-world incident response, where proactive investigation and remediation are critical.
Motivation
Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line interfaces (CLIs), making it critical to thoroughly assess their security capabilities.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.