Adversarial artificial intelligence

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

Adversarial artificial intelligence attacks targeting military systems and critical national infrastructure present severe risks to global strategic stability. A study commissioned by the UK Foreign, Commonwealth & Development Office (FCDO) and conducted by RAND Europe warns that these disruptions can compromise defense credibility, alter the offense-defense balance, and trigger accidental nuclear escalation.

The research, published on September 29, 2026, examines five distinct scenarios where the integration of machine learning into command, control, and communications (C3) systems introduces critical vulnerabilities. Attribution remains extremely difficult. This systemic ambiguity, compounded by the rapid democratization of technology to non-state actors, significantly increases the likelihood of miscalculation during international crises. To mitigate these escalation risks, the report urges governments to treat adversarial AI as a strategic stability risk, establish clear red lines, enhance dual-use command resilience, and create dedicated communication channels with strategic competitors to prevent unintended conflict.

Comment

The integration of machine learning into military decision-support frameworks forces a fundamental reassessment of established escalation control doctrines. Traditional deterrence models rely on predictable, linear escalation pathways that are incompatible with the rapid, non-linear failure modes of compromised algorithms. Within the UK Defence AI Centre (DAIC), this vulnerability exposes a critical gap between automated data processing and human command authority. When adversarial inputs corrupt target recognition models, the resulting systemic errors can trigger rapid, unintended kinetic responses before human operators can intervene.

Consequently, this doctrinal vulnerability will likely drive a shift toward defensive decentralisation, forcing local units to operate without centralised cloud-based intelligence feeds. This operational fragmentation directly undermines the joint-force integration goals envisioned in the UK Ministry of Defence's Multi-Domain Integration concept. Ultimately, the risk of algorithmic manipulation will force the DAIC to restrict automated target-generation pipelines within its tactical cloud networks, slowing the overall operational tempo to match human verification speeds.

Strategic Question for Discussion

How can the DAIC maintain the credibility of its automated target-generation pipelines when the threat of adversarial data poisoning introduces persistent, un-attributable vulnerabilities into tactical cloud networks?

The trajectory indicates that the DAIC will likely adopt a hybrid verification model, treating algorithmic outputs as advisory rather than executable intelligence. This approach preserves the speed of machine-enabled sorting while introducing mandatory human-in-the-loop validation at critical firing nodes. However, this compromise inevitably sacrifices the raw processing speed that initially justified the integration of machine learning into tactical cloud networks.

Share your assessment in the comments below.

To read in details click here