arXiv:2605.30913v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in conversational settings where user tone ranges from polite to adversarial or toxic, yet less is known about whether toxic language in otherwise semantically equivalent prompts can degrade factual reliability. We study how lexical and tone-based prompt perturbations affect the factual reliability of LLMs. Using controlled prompt variations across polite, random, and three toxicity levels, we evaluate five LLMs on ARC-Easy, GSM8K, and MMLU. We find that toxic lexical perturbations consiste
As LLMs are increasingly deployed in real-world conversational settings, understanding their robustness to adversarial or toxic inputs is critical for safe and reliable integration.
This research highlights a new vector for LLM degradation, where user tone, not just semantic content, can compromise factual reliability, impacting the trustworthiness and safety of AI applications.
The understanding of LLM vulnerability expands beyond direct adversarial attacks to include subtle prompt perturbations related to toxicity and tone, requiring more sophisticated robustness mechanisms.
- · AI safety researchers
- · Developers of robust LLM safety filters
- · Companies offering AI alignment solutions
- · Unsecured LLM deployments
- · Applications vulnerable to prompt injection/perturbation
- · Users relying on unmitigated LLMs for factual information
Increased focus on prompt engineering and input validation techniques to neutralize toxic or adversarial tones.
Development of LLMs specifically trained to be robust against tone-based perturbations, potentially involving new architectural or fine-tuning approaches.
Enhanced regulatory scrutiny and industry standards around AI toxicity and factual reliability, akin to content moderation but for underlying model veracity.
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Read at arXiv cs.AI