SecLog-Bench: A Scalable Benchmark for Cybersecurity Log Reasoning with Scaffolded Evidence
SecLog-Bench offers a scalable, multi-source benchmark for evaluating how well LLMs reason over security logs — a practical yardstick as SOC teams pilot AI-assisted triage.
Summary written by editorial AI · Source link below
arXiv:2610.31000v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for cybersecurity, leading to growing interest in LLM-based automated cybersecurity log analysis. However, current datasets (1) are limited in scale; (2) rely mainly on single-source evidence; and (3) have only binary or multi-class attack labels, without dense annotations of the reasoning process. To address these limitations, SecLog-Bench has three key features. First, we build an extensib
Editorial Analysis
As enterprises pilot LLM-based SOC tooling, a rigorous benchmark prevents over-reliance on models that perform well in demos but fail on complex, multi-source log scenarios.
Require vendors of AI-driven log analysis products to demonstrate performance against published benchmarks like SecLog-Bench.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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