SIR-Bench: Evaluating Investigation Depth in Security Incident Response Agents

SIR-Bench addresses a critical gap in LLM evaluation: the 'parroting' problem where agents simply restate alert data rather than performing genuine investigation. Its defensibility (5/10) stems from the high labor cost of curating 129 expert-validated incident patterns, which is a significant barrier compared to generic prompt-engineering benchmarks. However, as a 4-day-old project with 0 stars (despite 5 forks indicating research activity), it lacks the 'standardization' moat required for higher scores. Frontier labs like Microsoft (Security Copilot) and Google (Mandiant/Sec-PaLM) have high platform domination risk here because they own the telemetry data necessary to generate these benchmarks at scale. The project is highly valuable as a research tool, but its long-term survival depends on becoming the de facto community standard for security agent evaluation before cloud providers release proprietary benchmarking suites for their SOAR platforms.