QSAF: A Novel Mitigation Framework for Cognitive Degradation in Agentic AI
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918099211517952 |
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| author | Atta, Hammad Baig, Muhammad Zeeshan Mehmood, Yasir Shahzad, Nadeem Huang, Ken Haq, Muhammad Aziz Ul Awais, Muhammad Ahmed, Kamal |
| author_facet | Atta, Hammad Baig, Muhammad Zeeshan Mehmood, Yasir Shahzad, Nadeem Huang, Ken Haq, Muhammad Aziz Ul Awais, Muhammad Ahmed, Kamal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15330 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QSAF: A Novel Mitigation Framework for Cognitive Degradation in Agentic AI Atta, Hammad Baig, Muhammad Zeeshan Mehmood, Yasir Shahzad, Nadeem Huang, Ken Haq, Muhammad Aziz Ul Awais, Muhammad Ahmed, Kamal Artificial Intelligence 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. |
| title | QSAF: A Novel Mitigation Framework for Cognitive Degradation in Agentic AI |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2507.15330 |