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This paper introduces a contest-theoretic model to analyze adversarial investment in AI-augmented cybersecurity, where attackers amplify offensive potency and erode defensive effectiveness. The model derives a closed-form arms race ratio, decomposing the relative effectiveness of offensive and defensive investments into six structural primitives. Key findings show that full cross-correlation of threat intelligence across attack surfaces neutralizes the attacker's advantage from surface proliferation, while a lack of cross-correlation leads to vanishing per-surface defense effectiveness.
Threat intelligence sharing can completely neutralize an attacker's advantage gained from increasing the number of attack surfaces.
We study a contest-theoretic model of adversarial investment in which an attacker and a defender allocate resources to AI-augmented capabilities across multiple attack surfaces. The attacker's investment operates through two channels: it amplifies offensive potency unconditionally and erodes defensive effectiveness conditionally, generating an adversarial discount that deepens endogenously with the defender's own investment. We derive a closed-form arms race ratio decomposing the relative marginal effectiveness of offensive and defensive investment into six structural primitives and establish equilibrium uniqueness and global convergence under a continuous best-response dynamic. The central result concerns signal cross-correlation, the degree to which threat intelligence on one surface informs detection on another. With full cross-correlation, the arms race ratio is independent of the number of attack surfaces: the attacker's structural advantage from surface proliferation is completely neutralized. Under the benchmark full-dilution case, without cross-correlation, per-surface defense effectiveness vanishes as the attack surface grows. Extending the analysis to heterogeneous defenders facing an attacker who targets by expected value, we argue that the model points to a dual inefficiency: overinvestment in private defense (a zero-sum redirective externality) and underinvestment in shared signal correlation (a public good). These formal results, together with public-good reasoning outside the base model, characterize when collective information aggregation can dominate private capability investment as the decisive margin in adversarial contests.
