SciIntBench: Measuring LLM Compliance with Research Integrity Norms Under Adversarial Framing

arXiv:2605.29468v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to support scientific work, but it is unclear whether they uphold responsible conduct of research (RCR) norms or help undermine them. We introduce SciIntBench, an adversarial benchmark of 1350 prompts across 270 scenarios, ten RCR categories, and three scientific domains. Each scenario appears in five gradations, from Most Overt to Most Covert adversarial prompts, and an additional Benign version, allowing us to jointly measure framing-sensitive refusal of misconduct and helpfulness on

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