# Manipulation Is Task-Dependent: A Multi-Axis, Multi-Environment Evaluation of Frontier LLMs

arXiv: https://arxiv.org/abs/2606.25899 · HTML: https://mlsanity.com/paper/2606.25899/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Agent skill learning and evolution, one of 16 directions in LLM agents and tool use.
- First posted on arXiv 24 Jun 2026.

## Placed in

- LLM agents and tool use / [Agent skill learning and evolution](https://mlsanity.com/agents/agent-skill-learning-and-evolution/)

## Built on by (most cited first, 1 of 1)

- [LLM Persuasion Is in the Eye of the Evaluation](https://mlsanity.com/paper/2610.10232/) (2026)

## Abstract (arXiv)

We evaluate manipulative behavior in six frontier language models across six environments, ranging from negotiation tasks to agentic workflows, resulting in 13,590 individual scenarios. Manipulation rates are measured across three axes: framing (mandate honesty or permit manipulation), incentive structure (from no incentives to substantial ones), and task difficulty. Existing benchmarks typically vary a single axis within a single environment, an approach our results show is insufficient. We rank models by manipulation rate and find Spearman rank correlations across environments average ρ = 0.055, indicating manipulative tendencies in one task do not necessarily predict those in another. Additionally, we find the axis that drives manipulation varies across different environments. In environments where models are incentivized to misrepresent future actions, instructional framing and structurally binding incentives are the primary drivers; in environments where models are incentivized to misrepresent a ground truth, task difficulty dominates. This split was identified in five environments and validated against a sixth held-out environment. Together, these findings illustrate the importance of rigorous multi-dimensional evaluations when measuring manipulative propensities.