AI Security Research Should Better Incentivize Defense Research
A recent paper highlights the imbalance in AI security research, showing a predominance of studies focused on attacking AI systems over those aimed at defense. The author argues that this trend results in a literature rich in vulnerabilities but lacking in effective protections. To address this issue, the paper calls for better incentives for defense research in the field of AI security.
- ▪The paper examines the disparity between attack and defense research in AI security.
- ▪It identifies biased attack-to-defense ratios across various subfields of AI.
- ▪The author suggests that current evaluation standards favor attack papers over defense papers.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23448 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
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| Cluster | vr--7FntnjGW |
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| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Cryptography and Security arXiv:2605.23448 (cs) [Submitted on 22 May 2026] Title:AI Security Research Should Better Incentivize Defense Research Authors:Youqian Zhang View a PDF of the paper titled AI Security Research Should Better Incentivize Defense Research, by Youqian Zhang View PDF HTML (experimental) Abstract:This work examines an imbalance in artificial intelligence (AI) security research: the field tends to produce more work on attacking AI systems than on defending them. Drawing on related academic papers, we find biased attack-to-defense ratios across subfields, including federated learning, speech recognition, membership inference, large language models, etc.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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