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

TL;DR

This paper models an adversarial contest where attacker and defender allocate resources across multiple attack surfaces, analyzing how signal correlation affects the arms race dynamics and investment efficiency.

Contribution

It introduces a formal model of adversarial investment with a focus on signal cross-correlation and derives conditions for equilibrium and arms race behavior.

Findings

Full signal cross-correlation neutralizes the advantage of multiple attack surfaces.

Without cross-correlation, defense effectiveness diminishes as attack surfaces increase.

The model highlights overinvestment in private defense and underinvestment in shared signals as inefficiencies.

Abstract

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.