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

arXiv: https://arxiv.org/abs/2608.21389 · HTML: https://mlsanity.com/paper/2608.21389/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 26 Jul 2026.

## Builds on (most cited first, 10 of 10)

- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [Human heuristics for AI-generated language are flawed](https://mlsanity.com/paper/2206.07271/) (2022)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Models](https://mlsanity.com/paper/2310.00737/) (2023)
- [Blessing or curse? A survey on the Impact of Generative AI on Fake News](https://mlsanity.com/paper/2404.03021/) (2024)
- [Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems](https://mlsanity.com/paper/2601.21963/) (2026)
- [Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild](https://mlsanity.com/paper/2601.22871/) (2026)
- [CRED-1: An Open Multi-Signal Domain Credibility Dataset for Automated Pre-Bunking of Online Misinformation](https://mlsanity.com/paper/2604.20856/) (2026)
- [The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance](https://mlsanity.com/paper/2602.02100/) (2026)
- [Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News](https://mlsanity.com/paper/2604.03755/) (2026)

## Abstract (arXiv)

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.