QSAF: A Novel Mitigation Framework for Cognitive Degradation in Agentic AI

TL;DR

This paper identifies a new internal vulnerability class in agentic AI called Cognitive Degradation and introduces QSAF, a lifecycle-aware framework with real-time controls to mitigate these systemic failures.

Contribution

It defines Cognitive Degradation as a systemic vulnerability in agentic AI and proposes the first comprehensive, cross-platform defense framework with real-time mitigation controls.

Findings

Cognitive Degradation is a critical new AI vulnerability class.

QSAF framework enables early detection and mitigation of internal failures.

The framework improves agent resilience through lifecycle management.

Abstract

We introduce Cognitive Degradation as a novel vulnerability class in agentic AI systems. Unlike traditional adversarial external threats such as prompt injection, these failures originate internally, arising from memory starvation, planner recursion, context flooding, and output suppression. These systemic weaknesses lead to silent agent drift, logic collapse, and persistent hallucinations over time. To address this class of failures, we introduce the Qorvex Security AI Framework for Behavioral & Cognitive Resilience (QSAF Domain 10), a lifecycle-aware defense framework defined by a six-stage cognitive degradation lifecycle. The framework includes seven runtime controls (QSAF-BC-001 to BC-007) that monitor agent subsystems in real time and trigger proactive mitigation through fallback routing, starvation detection, and memory integrity enforcement. Drawing from cognitive neuroscience, we map agentic architectures to human analogs, enabling early detection of fatigue, starvation, and role collapse. By introducing a formal lifecycle and real-time mitigation controls, this work establishes Cognitive Degradation as a critical new class of AI system vulnerability and proposes the first cross-platform defense model for resilient agentic behavior.