Uplifted Attackers, Human Defenders: The Cyber Offense-Defense Balance for Trailing-Edge Organizations
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Abstract (arXiv)
Advances in AI are widely understood to have implications for cybersecurity. Articles have emphasized the effect of AI on the cyber offense-defense balance, and commentators can be found arguing either that cyber will privilege attackers or defenders. For defenders, arguments are often made that AI will enable solutions like formal verification of all software--and for some well-equipped companies, this may be true. This conversation, however, does not match the reality for most companies. "Trailing-edge organizations," as we term them, rely heavily on legacy software, poorly staff security roles, and struggle to implement best practices like rapid deployment of security patches. These decisions may be the result of corporate inertia, but may also be the result of a seemingly-rational calculation that attackers may not bother targeting a firm due to lack of economic incentives, and as a result, underinvestment in defense will not be punished. This approach to security may have been sufficient prior to the development of AI systems, but it is unlikely to remain viable in the near future. We argue that continuing improvements in AI's capabilities poses additional risks on two fronts: First, increased usage of AI will alter the economics of the marginal cyberattack and expose these trailing-edge organizations to more attackers, more frequently. Second, AI's advances will enable attackers to develop exploits and launch attacks earlier than they can today--meaning that it is insufficient for these companies to attain parity with today's leading defenders, but must instead aim for faster remediation timelines and more resilient software. The situation today portends a dramatically increased number of attacks in the near future. Moving forward, we offer a range of solutions for both organizations and governments to improve the defensive posture of firms which lag behind their peers today.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 68 references that are in the map. First posted on arXiv 14 Aug 2025.
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References · 11
- 2025CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilitiescited by 96
- 2025CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scalecited by 78
- 2025Towards Formal Verification of LLM-Generated Code from Natural Language Promptscited by 8
- 2018The MalSource Dataset: Quantifying Complexity and Code Reuse in Malware Developmentcited by 7
- 2022Will AI Make Cyber Swords or Shields: A few mathematical models of technological progresscited by 6
- 2025Exploring the Challenges and Opportunities of AI-assisted Codebase Generationcited by 4
- 2021Longitudinal Study of the Prevalence of Malware Evasive Techniquescited by 3
- 2024Advancing Software Security and Reliability in Cloud Platforms through AI-based Anomaly Detectioncited by 2
- 2025Do AI models help produce verified bug fixes?cited by 2
- 2023Money Over Morals: A Business Analysis of Conti Ransomwarecited by 1
- 2025The Impact of AI on the Cyber Offense-Defense Balance and the Character of Cyber Conflictcited by 1
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