mlsanityData to 7 Oct 2026

Access Controls Will Solve the Dual-Use Dilemma

arXiv 2505.09341 ↗First posted 14 May 2025cs.AIcited by 3

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Abstract (arXiv)

AI safety systems face the dual-use dilemma. It is unclear whether to answer dual-use requests, since the same query could be either harmless or harmful depending on who made it and why. To make better decisions, such systems would need to examine requests' real-world context, but currently, they lack access to this information. Instead, they sometimes end up making arbitrary choices that result in refusing legitimate queries and allowing harmful ones, which hurts both utility and safety. To address this, we propose a conceptual framework based on access controls where only verified users can access dual-use outputs. We describe the framework's components, analyse its feasibility, and explain how it addresses both over-refusals and under-refusals. While only a high-level proposal, our work takes the first step toward giving model providers more granular tools for managing dual-use content. Such tools would enable users to access more capabilities without sacrificing safety, and offer regulators new options for targeted policies.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 27 references that are in the map. First posted on arXiv 14 May 2025.

Builds on

14 of 27 references.

References · 14

  1. 2017Deep reinforcement learning from human preferencescited by 3,846
  2. 2023Sparse Autoencoders Find Highly Interpretable Features in Language Modelscited by 1,359
  3. 2024How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMscited by 533
  4. 2024Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attackcited by 364
  5. 2025Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teamingcited by 172
  6. 2025Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversationscited by 88
  7. 2024Do Unlearning Methods Remove Information from Language Model Weights?cited by 66
  8. 2023LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?cited by 47
  9. 2024Jailbreaking Large Language Models Against Moderation Guardrails via Cipher Characterscited by 39
  10. 2024Gradient Routing: Masking Gradients to Localize Computation in Neural Networkscited by 28
  11. 2025Virology Capabilities Test (VCT): A Multimodal Virology Q&A Benchmarkcited by 27
  12. 2024Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responsescited by 18
2 more
  1. 2025No Free Lunch with Guardrailscited by 10
  2. 2025When Grammar Guides the Attack: Uncovering Control-Plane Vulnerabilities in LLMs with Structured Outputcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.