agentprivacy-threat-adversarial

Privacy threat modeling for AI agents defines the capabilities and goals of an adversary attempting to reconstruct a principal's private state from observed agent outputs. It determines the precise attack vectors and failure modes before conducting a red team exercise.

Adversarial analysis measures privacy vulnerabilities by modeling probabilistic polynomial-time adversaries and defining reconstruction resistance alongside information bounds. It identifies specific empirical breaking conditions and measurement gaps to quantify exposure.

Privacy threat modeling explicitly excludes side-channel attacks, collusion, internal state access, social engineering, compromised counterparties, and quantum adversaries. It focuses strictly on passive surveillance, traffic analysis, and network-level observation.

A privacy-preserving AI architecture breaks down under four empirical conditions: structural incompatibility, privacy loss epsilon exceeding 0.1, sublinear network effects, and singular Sigma matrices. These specific conditions invalidate the entire privacy framework.

AI agent privacy analysis identifies four measurement gaps: cross-force separations, empirical decay rate lambda, network exponent k, and edge weight f(e). Addressing these gaps is crucial for accurately quantifying reconstruction resistance.

You cannot use this threat modeling approach for side-channel attacks because it explicitly excludes them from its defensive scope. It focuses solely on passive surveillance, traffic analysis, temporal correlation, and network-level observation.