Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lens

Abstract:Generative AI enables customized misinformation at scale, yet defenses remain largely reactive. We present empirical findings from a human-subject study (n=504 participants, n=2,438 judgments) in which users classified news fragments by origin (human vs. machine) and veracity (real vs. fake). We organize results using an adapted cybersecurity kill chain as a taxonomy for intervention, mapping perception data onto stages of a cognitive attack lifecycle. Three key findings emerge: (1) a perception-accuracy gap where heightened suspicion does not improve detection; (2) modern LLMs frequently produce human-indistinguishable text; and (3) an asymmetric cognitive fatigue effect where fake-news detection degrades by 10.2 percentage points under sustained exposure while AI-origin detection remains stable. These findings identify candidate intervention points for proactive defense against AI-driven disinformation.

Accepted at the GI 2026 workshop (LNI). 10 pages

Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)

K.4.2; K.6.5; H.1.2; I.2.7

arXiv:2608.21389 [cs.CY]

(or arXiv:2608.21389v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2608.21389 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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