The Offense-Defense Balance of Scientific Knowledge: Does Publishing AI Research Reduce Misuse?
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Abstract (arXiv)
There is growing concern over the potential misuse of artificial intelligence (AI) research. Publishing scientific research can facilitate misuse of the technology, but the research can also contribute to protections against misuse. This paper addresses the balance between these two effects. Our theoretical framework elucidates the factors governing whether the published research will be more useful for attackers or defenders, such as the possibility for adequate defensive measures, or the independent discovery of the knowledge outside of the scientific community. The balance will vary across scientific fields. However, we show that the existing conversation within AI has imported concepts and conclusions from prior debates within computer security over the disclosure of software vulnerabilities. While disclosure of software vulnerabilities often favours defence, this cannot be assumed for AI research. The AI research community should consider concepts and policies from a broad set of adjacent fields, and ultimately needs to craft policy well-suited to its particular challenges.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Dec 2019.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 6; 2024: 1; 2025: 1; 2026: 2.
12 papers.
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 95
- 2024On the Societal Impact of Open Foundation Modelscited by 64
- 2023The Gradient of Generative AI Release: Methods and Considerationscited by 63
- 2023Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectivescited by 31
- 2021Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputscited by 18
- 2023Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?cited by 15
- 2023Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Modelscited by 7
- 2026Autonomous Research Agents: A Survey of AI Scientists and the Verification Gapcited by 6
- 2023Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safetycited by 2
- 2021"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendationcited by 0
- 2025Opening the Scope of Openness in AIcited by 0
- 2026RCTs for Frontier AI Governance: Methodological Challenges and Solutions for Human Uplift Studiescited by 0
- 2026Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap29 Jun 2026
- 2026RCTs for Frontier AI Governance: Methodological Challenges and Solutions for Human Uplift Studies11 Mar 2026
- 2025Opening the Scope of Openness in AI9 May 2025
- 2024On the Societal Impact of Open Foundation Models27 Feb 2024
- 2023Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models22 Dec 2023
- 2023Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives29 Sep 2023
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safety6 Jul 2023
- 2023Protecting Society from AI Misuse: When are Restrictions on Capabilities Warranted?16 Mar 2023
- 2023Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety6 Mar 2023
- 2023The Gradient of Generative AI Release: Methods and Considerations5 Feb 2023
- 2021Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs25 May 2021
- 2021"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation1 Apr 2021