The Adversarial Discount - AI, Signal Correlation, and the Cybersecurity Arms Race

In a paper titled 'The Adversarial Discount - AI, Signal Correlation, and the Cybersecurity Arms Race' (arXiv:2605.04336), economist James W. Bono presents a contest-theoretic model where attackers and defenders allocate resources to AI-augmented capabilities across multiple attack surfaces. The attacker's investment works through two channels: it directly amplifies offensive potency and, critically, conditionally erodes defensive effectiveness—creating an 'adversarial discount' that grows endogenously with the defender's own investment. Bono derives a closed-form arms race ratio decomposing relative marginal effectiveness into six structural primitives, and establishes equilibrium uniqueness under continuous best-response dynamics.

The central result concerns signal cross-correlation—the degree to which threat intelligence on one surface informs detection on another. When cross-correlation is perfect, the arms race ratio becomes 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 to heterogeneous defenders, Bono identifies a dual inefficiency: overinvestment in private defense (a zero-sum redirective externality) and underinvestment in shared signal correlation (a public good). The paper concludes that collective information aggregation can dominate private capability investment as the decisive margin in adversarial contests.