APTInvestBench: Evaluating Autonomous APT Investigation under Varying Telemetry
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Abstract (arXiv)
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 from acquisition and reporting gaps. Across eleven LLMs, agents acquire sufficient evidence for 44.3% of recoverable attack actions on average, while formal citations support only 25.0%. More importantly, aggregate coverage can conceal substantial instability: from Full to endpoint-only telemetry, coverage declines by only 1.6 percentage points, yet 35.5% of previously covered actions lose sufficient citation support despite remaining recoverable. Across four frameworks, such losses persist even when registered supporting records remain unchanged. APTInvestBench provides reusable investigation environments and diagnostic evaluation for identifying these gaps and developing more reliable defensive agents.
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References · 12
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2024Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Modelscited by 164
- 2025CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scalecited by 78
- 2018HOLMES: Real-time APT Detection through Correlation of Suspicious Information Flowscited by 43
- 2023Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenancecited by 31
- 2023MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation Learningcited by 20
- 2025ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigationcited by 15
- 2026Retrieval-Augmented LLMs for Security Incident Analysiscited by 4
- 2025KnowHow: Automatically Applying High-Level CTI Knowledge for Interpretable and Accurate Provenance Analysiscited by 3
- 2026An Empirical Study of Observability Limits in Advanced Software Supply Chain Attackscited by 3
- 2026DiagChain: A Diagnostic Benchmark for Evaluating LLM Agents on Evidence-Grounded Attack Chain Reconstructioncited by 3
- 2026DUPIN: Attack Learning Is Still Needed! Demonstrating Few-Shot after Unsupervised Pretraining Is A Nimble Forensics Learnercited by 1
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