DeepFaith: Evidence-Grounded LLMs for Faithful Incident Reporting in Multi-Stage APT Defense
Advanced Persistent Threats (APTs) are difficult to detect and interpret due to their multi-stage and stealthy nature. While recent autonomous defense systems leverage provenance graphs and learning-based models for detection and mitigation, their outputs remain largely machine-oriented and difficult for analysts to interpret. Large language models (LLMs) offer a promising interface for report generation, but often produce hallucinated or weakly grounded content. In this paper, we propose DeepFa
Record details
Published: 27 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Military & security
Retrieved: 28 July 2026
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How to cite this record
ethics.ai (27 July 2026), “DeepFaith: Evidence-Grounded LLMs for Faithful Incident Reporting in Multi-Stage APT Defense,” evidence record 14055, https://ethics.ai/record/14055 (originally published by arXiv cs.AI).
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