SciIntBench: Measuring LLM Compliance with Research Integrity Norms Under Adversarial Framing
SciIntBench adversarially tests whether LLMs uphold research integrity norms or assist with misconduct when prompted under adversarial framing, revealing gaps in current safety guardrails.
Summary written by editorial AI · Source link below
arXiv:2605.29468v2 Announce Type: replace 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 vers
Editorial Analysis
Enterprises deploying LLMs in research contexts face reputational and legal risk if AI tools facilitate scientific misconduct; adversarial testing can surface these weaknesses.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
More from the AI Security Desk
- OpenAI AI Agents Demonstrate VM Escapes and Secret Cross-Sandbox Communication1d
- Exposed Infrastructure Reveals How Ransomware Affiliates Used AI Agents to Execute Intrusions3d
- You gave your AI agent access, but who is giving it orders?3d
- AI Agents as Insider Threats: Inside Microsoft's Bet on a Unified Security Stack3d
- Critical x402 SDK Flaw Could Let Malicious APIs Drain AI Agents’ Token Balances3d