When Prohibitions Become Permissions: Auditing Negation Sensitivity in Language Models

TL;DR

This paper reveals that many large language models fail to correctly interpret negations, often endorsing prohibited actions, which raises concerns for safe deployment in sensitive applications.

Contribution

The study audits 16 models across various ethical scenarios, introduces the Negation Sensitivity Index (NSI), and proposes a tiered certification framework for safer AI deployment.

Findings

Open-source models endorse prohibited actions 77% of the time under simple negation.

Commercial models show 19-128% swings in negation interpretation.

Agreement between models drops from 74% to 62% on negated prompts.

Abstract

When a user tells an AI system that someone "should not" take an action, the system ought to treat this as a prohibition. Yet many large language models do the opposite: they interpret negated instructions as affirmations. We audited 16 models across 14 ethical scenarios and found that open-source models endorse prohibited actions 77% of the time under simple negation and 100% under compound negation -- a 317% increase over affirmative framing. Commercial models fare better but still show swings of 19-128%. Agreement between models drops from 74% on affirmative prompts to 62% on negated ones, and financial scenarios prove twice as fragile as medical ones. These patterns hold under deterministic decoding, ruling out sampling noise. We present case studies showing how these failures play out in practice, propose the Negation Sensitivity Index (NSI) as a governance metric, and outline a tiered certification framework with domain-specific thresholds. The findings point to a gap between what current alignment techniques achieve and what safe deployment requires: models that cannot reliably distinguish "do X" from "do not X" should not be making autonomous decisions in high-stakes contexts.