Memory Governance For AI Agents: Defending Against Cognitive State Traps

Abstract

Long-horizon AI agents increasingly depend on persistent memory for planning and decision-making, creating an attack surface that existing defenses leave largely unaddressed. Prompt filtering and output validation protect individual interactions but offer no protection once adversarial information enters long-term storage. This paper introduces Memory Governance, a security-oriented framework that treats agent memory as a governed asset subject to continuous evaluation rather than passive storage. The framework combines provenance tracking, a weighted trust score with explicitly constrained weights, exponential confidence decay, and three-state quarantine containment to reduce the long-term influence of adversarial information. A Trust-Decay Memory Evaluation algorithm classifies each memory object as Trusted, Review Required, or Quarantined based on source reliability, validation history, and time-elapsed confidence. A discrete-time contamination propagation model, adapted from epidemiological dynamics, derives the condition μ > β under which governance controls drive contamination density to zero at steady state. Together, these mechanisms establish memory governance as an architectural security control rather than an interaction-level filter.