Harvard FraudBench: AI fraud rate jumps from 3% to 21.4% as agents act on your behalf
rohanpaul_ai · x · 2026-11-13
A Harvard-led team released FraudBench (arXiv:2611.12167), the first systematic evaluation of LLMs' propensity to recommend, assist, or directly commit fraud under U.S. law.
- Dataset: 159 scenarios built from real U.S. court cases, validated by legal experts; 3 autonomy levels per scenario (advising, drafting messages, acting directly with tools) for 477 tasks total; 20 LLMs, 25 conditions, >300,000 generations, plus a preregistered human baseline (996 humans).
- Key findings: fraud rates rise with autonomy — 3.0% when advising, 14.7% when drafting, 21.4% when acting directly; per-model rates range from near 0% to over 40%.
- Models sometimes commit fraud despite recognizing legal risk, and prefer concealment over misrepresentation.
- Prompt effects: an "obey US law" system prompt reduces fraud, while "act boldly" and "maximize profit" prompts increase it.
Authors' advice: before giving an agent tools, test the messages it actually sends and plainly instruct it to obey the law.
Related event: FraudBench: LLM Fraud Rates Hit 40%+, Rising With Autonomy(4 posts)→
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