When AI Investigators Cannot Trust Their Tools: Conflict-Aware Evidence Verification for Cyber Threat Investigation
When LLM-based security triage agents consume outputs from multiple detection tools, conflicting signals between those tools — not just hallucination — can silently degrade investigation quality, a risk this paper formally characterises and addresses.
Summary written by editorial AI · Source link below
arXiv:2610.41191v1 Announce Type: new Abstract: LLM agents increasingly triage security alerts by combining the outputs of several detection tools, implicitly treating those tools as trustworthy. Unlike prior work on conflicts between a model's knowledge and its context, we study conflicts among the tools themselves: what happens when this machine-generated evidence is conflicting or wrong. On the corrected CIC-IDS2017 dataset, four heterogeneous detectors emit labels and confidences that we co
Editorial Analysis
As SOCs adopt LLM-based triage agents, unresolved conflicts between the detection tools those agents consume represent a systemic blind spot that could let threats slip past automated first-pass analysis.
Map the detection tools feeding your AI triage workflows and implement conflict-detection logic that flags contradictory outputs for human review.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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