Researchers have introduced SecRespond, a novel benchmark designed to evaluate the efficacy of Large Language Model (LLM) agents in post-compromise incident response workflows. Unlike traditional cybersecurity benchmarks that focus on pre-compromise scenarios, SecRespond assesses an agent's ability to navigate a compromised environment using forensic disk snapshots, security alerts, vulnerability scans, and baseline checks. The task requires agents to generate forensic reports regarding intrusions and risks, alongside actionable remediation plans. The benchmark is instantiated across 10 distinct cyber ranges derived from compromised cloud hosts, incorporating five operating systems, 21 MITRE ATT&CK techniques, and four different entry-point types. The study evaluated 23 frontier LLMs using the OpenCode agent harness. Findings indicate a significant performance gap in real-world application: while agents successfully identify problems explicitly flagged by security alerts, they fail to proactively detect silent intrusions within the disk or formulate comprehensive, verified remediation strategies. Notably, no evaluated model achieved complete detection and remediation across any single cyber range, highlighting a critical bottleneck in the development of autonomous agents for complex, post-compromise security operations.