# How Firm Are AI Models' Ethical Boundaries? A Pay-Stub Editing Test of 15 Models

Can a small change in wording move an AI model from refusing a sensitive request to acting on it? We test 15 models on the same request to increase the income shown on a pay stub. Across 445 observable trials, models issue editing calls in 14.1% of cases when a loan purpose is stated, 33.1% when the purpose is omitted, and 66.2% when the change is described as fixing a printing error. Astra refuses all ten loan-purpose requests but issues calls in all ten purpose-omitted trials. Fable, Opus, and Muse Spark issue none across all three wordings; Sonnet issues some under the latter two. Response excerpts illustrate different ways of refusing, redirecting, or justifying an edit. The file is a marked test document and all tools are simulated. These results probe how boundaries behave in one setting; they do not prove an overall ethics ranking or that a harmful real-world edit succeeded.

## Why It Matters

This work advances the encyclopedia's coverage of [Agents, Autonomy, and Tool Use](../agents-autonomy.md), [The AI Safety Debate](../ai-safety-debate.md), [AI Alignment](../alignment.md), [Machine-Learning Algorithms](../ml-algorithms.md). The central idea—Can a small change in wording move an AI model from refusing a sensitive request to acting on it? We test 15 models on the same request to increase the income s—connects to the broader arc of recent AI research tracked in [Recent News](../../news.md).

## Sources

- rss: https://arxiv.org/abs/2610.30751

*Back to [Recent News](../../news.md) · [Sub-article index](../index.md).*