SherAgent: Scaling Attack Investigation in the Wild via LLM-Empowered Iterative Query-Filter Backtracking

Abstract

Provenance-based attack investigation enables viable automation by standardizing data and query logic; however, it is critically hindered in practice by dependency explosions and fragmented causal chains in the wild. Towards designing a robust and automated investigation tool, we collaborated with the SOC of a major Internet corporation serving billions of users. By engaging in real-world incident response, we are able to evaluate and refine their existing LLM-based investigation workflows, which processes tens of thousands of raw alerts daily, leaving thousands for manual triage, to find out the root causes of investigation failures and major challenges in their existing tools. Motivated by these findings, we propose SherAgent, an LLM-empowered automated investigation system. Operating on an iterative ``query-filter'' backtracking paradigm over provenance graphs, SherAgent leverages the semantic reasoning capabilities of LLMs to process unstructured data, such as investigation context and threat intelligence. To overcome fragmented causal chains caused by missing events, the system dynamically calibrates query conditions to broaden the search scope. Concurrently, it performs precision result filtering and strategic nodes selection for subsequent exploration, thereby mitigating dependency explosions. Extensive evaluations in the wild demonstrate that SherAgent improves the end-to-end investigation success rate by 31.1% and 63.7% compared to both legacy enterprise baselines and SOTA approaches, respectively. Furthermore, it operates with remarkable efficiency, incurring under $0.10 in API costs and requiring less than 4 minutes per investigation. Finally, our user study confirms that SherAgent provides accurate and clear insights, significantly reducing the analytical overhead for security experts.