Polarity Blindness in LLM Safety: A Systematic Study of Negation, Antonym Substitution, and Adversarial Framing
We show that this weakness can undermine LLM safety guardrails through a structural vulnerability we term polarity blindness: the failure to recognize harmful intent after polarity-altering linguistic transformations.
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Key points
- Negation remains a persistent challenge for language models across NLP tasks and modalities.
- We study two forms of polarity inversion: grammatical negation and antonym replacement, and use synonym replacement as a polarity-preserving control.
- We further test whether an adversarial textual wrapper amplifies these failures.
- Using a 260-prompt benchmark across 10 state-of-the-art LLMs and 3 languages (English, Spanish, Czech), stratified by harmfulness potential and evaluated on a validated 0-5 harm scale, we find that polarity blindness affects most evaluated models, with mean harm scores reaching 4.12 for the most vulnerable model.
Sources (1)
- [1]Polarity Blindness in LLM Safety: A Systematic Study of Negation, Antonym Substitution, and Adversarial FramingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 09:22 AM
We show that this weakness can undermine LLM safety guardrails through a structural vulnerability we term polarity blindness: the failure to recognize harmful intent after polarity-altering linguistic transformations.
Negation remains a persistent challenge for language models across NLP tasks and modalities.
Extractive summary: sentences quoted from the sources.
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