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How Narrative Wrapping Affects LLM Refusal: A Cross-Language Benchmark and Defense

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 04:03:25 UTC.

Briefings

A new cross-language benchmark and defense framework, GUISE and AXIS, were developed to evaluate how narrative wrappers affect safety-aligned LLMs. Research indicates that while language and register shifts only slightly alter harmful request representations, narrative wrappers significantly increase attack success rates. The study measured these effects across Qwen3-1.7B, Qwen3-4B, and GLM-4-9B models, finding that AXIS achieves the highest combined safety and usability scores among tested methods.

Key points

• Attack success rates on Qwen3-1.7B reached 89.4% in English, 93.0% in modern Chinese, and 95.7% in Classical Chinese when wrapped in narratives.
• The GUISE benchmark includes parallel requests in English, modern Chinese, and Classical Chinese with matched harmful and benign pairs held out for evaluation.

Context and limitations

The study covers only three specific models (Qwen3-1.7B, Qwen3-4B, GLM-4-9B) and uses a stricter criterion counting warn-then-answer responses as attack successes, which may not reflect all safety failure modes.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 09/10/2026, 04:40:18 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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