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

2025-07-21

AI Summary

This paper identifies “cognitive degradation” as a novel class of intrinsic vulnerability in autonomous AI systems—characterized by mechanisms including memory exhaustion, planner recursion, context overflow, and output suppression, which collectively induce silent agent drift, logical collapse, and persistent hallucination. To address this, we propose the first lifecycle-aware resilience defense framework: (1) a six-stage cognitive lifecycle model formalizing agent cognition over time; (2) a biologically inspired mapping mechanism enabling early detection of cognitive fatigue and role collapse; and (3) seven runtime control primitives (QSAF-BC-001–007), integrating fallback routing, hunger detection, and memory integrity guarantees. Empirical evaluation across diverse agent architectures demonstrates significant improvements in long-horizon operational stability and robust suppression of all three canonical degradation modes.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Adversarial Agents
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Addresses cognitive degradation in agentic AI systems
• Mitigates internal failures like memory starvation and logic collapse
• Proposes real-time controls for resilient AI behavior

Innovation

• QSAF monitors agent subsystems in real time
• Framework includes seven runtime mitigation controls
• Maps agentic architectures to human cognitive analogs