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

arXiv: https://arxiv.org/abs/2507.15330 · HTML: https://mlsanity.com/paper/2507.15330/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 21 Jul 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 3 of 3)

- [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2309.01431/) (2023)
- [Logic layer Prompt Control Injection (LPCI): A Novel Security Vulnerability Class in Agentic Systems](https://mlsanity.com/paper/2507.10457/) (2025)
- [Safety Shielding under Delayed Observation](https://mlsanity.com/paper/2307.02164/) (2023)

## Built on by (most cited first, 6 of 6)

- [How Far Are We from Genuinely Useful Deep Research Agents?](https://mlsanity.com/paper/2512.01948/) (2025)
- [Fortifying the Agentic Web: A Unified Zero-Trust Architecture Against Logic-layer Threats](https://mlsanity.com/paper/2508.12259/) (2025)
- [AAGATE: A NIST AI RMF-Aligned Governance Platform for Agentic AI](https://mlsanity.com/paper/2510.25863/) (2025)
- [DIRF: A Framework for Digital Identity Protection and Clone Governance in Agentic AI Systems](https://mlsanity.com/paper/2508.01997/) (2025)
- [Instruction Bleed: Cross-Module Interference in Prompt-Composed Agentic Systems](https://mlsanity.com/paper/2606.26356/) (2026)
- [Emergence of Agentic AI: A Review on Evolution, Background, Working Principles, Applications, Adoption Factors, and Future Research Directions](https://mlsanity.com/paper/2608.18110/) (2026)

## Abstract (arXiv)

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.