arXiv:2606.10281v1 Announce Type: cross Abstract: This paper presents AuditBench, a new benchmark dataset for evaluating the capabilities of LLMs at investigating security-related system audit logs. We design and use this benchmark to explore the performance of LLMs on four log-investigation tasks that incident response teams commonly perform, ranging from triaging alerts generated by detectors to identifying persistence mechanisms on compromised systems. AuditBench consists of system audit logs collected from Linux and Windows machines, and spans over 50 different security investigation scena
The rapid advancement and deployment of LLMs, coupled with increasing cybersecurity threats, create an immediate need to assess their utility in critical security functions.
This development indicates a growing reliance on AI for complex security tasks, potentially transforming incident response and cybersecurity operations for strategic actors.
LLMs are no longer just conversational agents but are now being actively benchmarked for high-stakes analytical roles in cybersecurity.
- · Cybersecurity AI developers
- · Security-conscious enterprises
- · Cloud providers with strong AI platforms
- · Traditional SIEM vendors without AI integration
- · Cybersecurity teams slow to adopt AI tools
LLMs will be increasingly integrated into security operations centers for preliminary investigation and detection.
The specialized training data and security context needed for these LLMs will create new demands on data collection and curriculum development.
Adversaries will likely begin to train their own LLMs to find vulnerabilities or evade detection, leading to an AI-driven arms race in cybersecurity.
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Read at arXiv cs.CL