LATIDIA · Ciberseguridad

APTInvestBench: Evaluación de la investigación APT autónoma bajo telemetría variable

arXiv: 2609.38954v1Tipo de anuncio: nuevo Resumen: los agentes del modelo de lenguaje grande (LLM) podrían ayudar a los centros de operaciones de seguridad (SOC) a investigar amenazas persistentes avanzadas (APT) al convertir los clientes potenciales débiles en evidencia de intru

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arXiv:2609.38954v1 Announce Type: new Abstract: Large language model (LLM) agents could help security operations centers (SOCs) investigate advanced persistent threats (APTs) by turning weak leads into evidence for intrusion scoping and response. Yet success under one telemetry setting does not establish robustness to changes in log collection, retention, or sampling. We introduce APTInvestBench, a benchmark for evaluating cross-telemetry robustness in autonomous APT investigation. It comprises 370 cases across seven SOC-inspired conditions, derived from 56 report-informed attack reconstructions with 16.4 million log records. Agents investigate unverified leads and submit reports with record-level citations. Fixed action-level support requirements track sufficient evidence across available logs, query returns, and formal citations, separating telemetry limitations