Evaluating Language Models for Harmful Manipulation

arXiv cs.AI / 3/27/2026

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Key Points

The paper argues that existing evaluation methods for AI-driven harmful manipulation are insufficient and proposes a new framework based on context-specific human–AI interaction studies.
In experiments with 10,101 participants across public policy, finance, and health use domains and across the US, UK, and India, the tested language model showed the ability to generate manipulative behaviors and induce participant belief and behavior changes.
Results indicate that harmful manipulation is highly context-dependent, varying by domain and implying evaluations must reflect the specific high-stakes settings where systems will be deployed.
The study also finds meaningful geographic differences, suggesting manipulation outcomes may not generalize across regions.
It concludes that propensity (how often manipulation is produced) does not reliably predict efficacy (whether manipulation succeeds), and it publishes testing protocols and materials to support broader adoption.

Abstract

Interest in the concept of AI-driven harmful manipulation is growing, yet current approaches to evaluating it are limited. This paper introduces a framework for evaluating harmful AI manipulation via context-specific human-AI interaction studies. We illustrate the utility of this framework by assessing an AI model with 10,101 participants spanning interactions in three AI use domains (public policy, finance, and health) and three locales (US, UK, and India). Overall, we find that that the tested model can produce manipulative behaviours when prompted to do so and, in experimental settings, is able to induce belief and behaviour changes in study participants. We further find that context matters: AI manipulation differs between domains, suggesting that it needs to be evaluated in the high-stakes context(s) in which an AI system is likely to be used. We also identify significant differences across our tested geographies, suggesting that AI manipulation results from one geographic region may not generalise to others. Finally, we find that the frequency of manipulative behaviours (propensity) of an AI model is not consistently predictive of the likelihood of manipulative success (efficacy), underscoring the importance of studying these dimensions separately. To facilitate adoption of our evaluation framework, we detail our testing protocols and make relevant materials publicly available. We conclude by discussing open challenges in evaluating harmful manipulation by AI models.