Adversarial artificial intelligence

Implications for strategic stability of attacks on military AI systems

Irene van Droffelaar, Kiran Suman-Chauhan, James Black, Thomas Kenchington, Angus Cooper, William Mitchell Reid

ResearchPublished Sep 29, 2026

This research was sponsored by the UK Foreign, Commonwealth & Development Office (FCDO).

Strategic competition in artificial intelligence (AI), including in the military domain, is intensifying, while global governance has not kept pace. The tactical implications of military AI are well documented: autonomous targeting, navigation in robotic systems, and decision-support tools for commanders are relatively well understood. Growing attention is now directed toward AI’s strategic effects and its influence on strategic stability.

Although research on the strategic implications of AI and governance options is expanding, it largely focuses on the potential benefits of effective AI systems. Little attention has been paid to how the disruption or denial of AI systems could threaten strategic stability. As a result, governance proposals rarely address these risks. Yet as AI is increasingly integrated in decision making and reliance on these systems grows, the consequences of adversarial interference will likewise intensify.

This project, commissioned by the UK Foreign Commonwealth and Development Office (FCDO), offers insights into how adversarial AI attacks targeting AI systems integrated in the military or critical national infrastructure may affect strategic stability, and offers recommendations to decrease the probability and impact of adversarial attacks on AI systems.

Key Takeaways

We analysed five scenarios to illustrate the breadth of implications of Adversarial AI attacks for strategic stability, resulting in the following findings. The comprehensive findings can be found in the report.

  • The (perceived) vulnerability of AI systems to adversarial attacks undermines the credibility of defence to adversaries and allies.
  • Adversarial AI may tip the offence-defence balance in favour of the offence.
  • Ambiguity, distinguishing ‘normal’ AI malfunction due to model fragility and deliberate adversarial attacks, and the difficulty of attribution create opportunities for miscalculation and mistrust. Empowerment of non-state actors through democratisation of technology, and specifically adversarial AI, further complicates attribution.
  • Entanglement of conventional and nuclear C3 increases the risk of an accidental attack on NC3, risking escalation.

Recommendation

  • Governments should treat adversarial AI as a strategic stability risk, not merely a cyber security or AI assurance issue. They should incorporate these risks into national governance and international forums; share threat information with allies, incidents, attribution and emerging techniques; review significant incidents; and develop expertise across foreign policy, defence, security, cyber and assurance communities. They should strengthen AI-enabled military systems, nuclear and dual-use command, control and communications, and critical infrastructure through robust design, resilience, recovery planning and fallback options. Clear red lines, response options and communication channels with competitors are also needed to reduce miscalculation, mistrust and escalation risks.

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van Droffelaar, Irene, Kiran Suman-Chauhan, James Black, Thomas Kenchington, Angus Cooper, and William Mitchell Reid, Adversarial artificial intelligence: Implications for strategic stability of attacks on military AI systems. Santa Monica, CA: RAND Corporation, 2026. https://www.rand.org/pubs/research_reports/RRA4734-2.html.
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