New benchmark tests LLMs against narrative-based rule-breaking attacks

A new benchmark called CoC-Seduce has been developed to test the rule adherence of large language models when faced with adversarial attacks. These attacks, termed Rhetorical Injection, use narrative framing and pseudo-logical reasoning to bypass model adjudication logic. Testing on models like GPT-5.4, Claude Sonnet 4.6, and Gemini 3.5 Flash revealed that neither model scale nor explicit reasoning mechanisms guarantee robustness, with pseudo-logic being the most effective attack vector. AI

IMPACT Highlights vulnerabilities in current LLMs to adversarial attacks, suggesting a need for improved safety mechanisms in deployed AI systems.

RANK_REASON The cluster contains an academic paper detailing a new benchmark for evaluating LLM safety. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Claude Sonnet 4.6
• CoC-Seduce
• Gemini 3.5 Flash
• GPT-5.4
• Pseudo-Logic
• Rhetorical Injection

• paper
• safety

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