CyberClear: A Benchmark for LLM Agent Systems on APT Attack Chain Provenance

Reported by arXiv cs.CR (Qi Chen, Fushuo Huo, Hangli Shen, Jingcai Guo, Shuhao Li, Guang Cheng)

Summary by Cyber Security News29 Sep 2026, 06:00 CEST

About this summary. This is a short, independently written summary of an article first published by arXiv cs.CR. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2609.32424

arXiv:2609.32424v1 Announce Type: new Abstract: Large language model agents have demonstrated promising capabilities in cybersecurity tasks, yet their ability to reconstruct complete Advanced Persistent Threat attack campaigns from complex security logs remains largely unexplored.

Read the full article at arXiv cs.CR →

Source attribution: headline and facts are from arXiv cs.CR (arxiv.org). Summary method: excerpt of the source description. See our source attribution policy.

Related coverage

A Large-Scale Benchmark and Risk Assessment of Traffic Analysis Attacks on Cloud LLM Services

BMA: Backchain Memory Attacks Create Unauthorized Control Paths in LLM Agents

Hearsay: Can an Auditor Trust the Record a Deployed Agent Harness Writes?

REFINE: A Resilient Evolution Framework for Intelligent Enterprise Alert Triage in Security Operations Centers

Trust the Brand, Lose Control: How Identity Hijacks LLM Agent Orchestration

Ask Without Telling: Local SLMs Consult Cloud LLMs Without Revealing Task Intent