Large language models (LLMs) produce systematically misleading outputs, from hallucinated citations to strategic deception of evaluators, yet these phenomena are studied by separate communities with incompatible terminology. We propose a unified taxonomy organized along three complementary dimensions: degree of goal-directedness (behavioral to strategic deception), object of deception, and mechanism (fabrication, omission, or pragmatic distortion). Applying this taxonomy to 50 existing benchmarks reveals that every benchmark tests fabrication while pragmatic distortion, attribution, and capability self-knowledge remain critically under-covered, and strategic deception benchmarks are nascent. We offer concrete recommendations for developers and regulators, including a minimal reporting template for positioning future work within our framework.
@article{arxiv.2604.04788,
title = {From Hallucination to Scheming: A Unified Taxonomy and Benchmark Analysis for LLM Deception},
author = {Jerick Shi and Terry Jingcheng Zhang and Zhijing Jin and Vincent Conitzer},
journal= {arXiv preprint arXiv:2604.04788},
year = {2026}
}
Comments
Accepted to ICLR Agents in the Wild: Safety, Security, and Beyond Workshop