The Ouroboros Threat - How AI Cannibalism Degrades Cyber Threat Intelligence
Fuente:
Zenodo
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Recurso digital |
| Sprache: | Englisch |
| Veröffentlicht: |
Zenodo
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866901711335981056 |
|---|---|
| author | Ahmad, Faran |
| author_facet | Ahmad, Faran |
| contents | <p class="MsoNormal">This paper investigates the systemic degradation of Cyber Threat Intelligence (CTI) caused by the exponential rise of generative artificial intelligence (GenAI) and its impact on machine learning-based defensive infrastructures. As cybersecurity models increasingly <span>learn from</span> synthetic, machine-generated data rather than continuous human-generated inputs, they suffer from a systemic architectural flaw known as Model Autophagy Disorder (MAD), or "model collapse." Through mathematically grounded analysis, this paper demonstrates that this recursive training progressively erases statistical outliers from training datasets. Consequently, automated security tools become highly optimized for filtering routine noise but develop catastrophic blind spots to sophisticated, human-engineered Advanced Persistent Threats (APTs), non-linear social engineering attacks, and zero-day exploits. This intrinsic vulnerability is further compounded by active adversarial exploitation, including data poisoning and the proliferation of fake proof-of-concept (PoC) exploits.</p> <p class="MsoNormal">After systematically evaluating existing mitigation strategies, algorithmic synthetic data filtering, Human-in-the-Loop (HITL) verification, and cryptographic data provenance this study concludes that no single technological solution can fully neutralize synthetic data pollution. Instead, the paper proposes the adoption of a Hybrid Zero-Trust Intelligence Architecture. This framework combines strict cryptographic data provenance (aligning with emerging C2PA, STIX 2.1, and OASIS standards) with targeted, human-led validation for high-stakes intelligence. Supported by a rigorous Return on Security Investment (ROSI) analysis, the proposed architecture provides a vital strategic roadmap for enterprises to restore threat visibility, ensure operational resilience, and remain viable within an increasingly stringent cyber insurance market.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19643839 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | The Ouroboros Threat - How AI Cannibalism Degrades Cyber Threat Intelligence Ahmad, Faran AI Cannibalism Model Autophagy Disorder (MAD) Cyber Threat Intelligence (CTI) Data Provenance Zero-Trust Architecture Model Collapse Cyber Insurance <p class="MsoNormal">This paper investigates the systemic degradation of Cyber Threat Intelligence (CTI) caused by the exponential rise of generative artificial intelligence (GenAI) and its impact on machine learning-based defensive infrastructures. As cybersecurity models increasingly <span>learn from</span> synthetic, machine-generated data rather than continuous human-generated inputs, they suffer from a systemic architectural flaw known as Model Autophagy Disorder (MAD), or "model collapse." Through mathematically grounded analysis, this paper demonstrates that this recursive training progressively erases statistical outliers from training datasets. Consequently, automated security tools become highly optimized for filtering routine noise but develop catastrophic blind spots to sophisticated, human-engineered Advanced Persistent Threats (APTs), non-linear social engineering attacks, and zero-day exploits. This intrinsic vulnerability is further compounded by active adversarial exploitation, including data poisoning and the proliferation of fake proof-of-concept (PoC) exploits.</p> <p class="MsoNormal">After systematically evaluating existing mitigation strategies, algorithmic synthetic data filtering, Human-in-the-Loop (HITL) verification, and cryptographic data provenance this study concludes that no single technological solution can fully neutralize synthetic data pollution. Instead, the paper proposes the adoption of a Hybrid Zero-Trust Intelligence Architecture. This framework combines strict cryptographic data provenance (aligning with emerging C2PA, STIX 2.1, and OASIS standards) with targeted, human-led validation for high-stakes intelligence. Supported by a rigorous Return on Security Investment (ROSI) analysis, the proposed architecture provides a vital strategic roadmap for enterprises to restore threat visibility, ensure operational resilience, and remain viable within an increasingly stringent cyber insurance market.</p> |
| title | The Ouroboros Threat - How AI Cannibalism Degrades Cyber Threat Intelligence |
| topic | AI Cannibalism Model Autophagy Disorder (MAD) Cyber Threat Intelligence (CTI) Data Provenance Zero-Trust Architecture Model Collapse Cyber Insurance |
| url | https://doi.org/10.5281/zenodo.19643839 |