From Hallucination to Scheming: A Unified Taxonomy and Benchmark Analysis for LLM Deception

TL;DR

This paper introduces a unified taxonomy and benchmark analysis for deception in large language models, highlighting gaps and providing recommendations for future research and regulation.

Contribution

It proposes a comprehensive taxonomy for LLM deception phenomena and analyzes existing benchmarks to identify coverage gaps and future directions.

Findings

All benchmarks test fabrication of information.

Pragmatic distortion and self-knowledge are under-covered.

Strategic deception benchmarks are still developing.

Abstract

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.