mlsanityData to 14 Oct 2026

The Age of Sensorial Zero Trust: Why We Can No Longer Trust Our Senses

arXiv 2507.00907 ↗First posted 1 Jul 2025cs.CR cs.AIcited by 0

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Abstract (arXiv)

In a world where deepfakes and cloned voices are emerging as sophisticated attack vectors, organizations require a new security mindset: Sensorial Zero Trust [9]. This article presents a scientific analysis of the need to systematically doubt information perceived through the senses, establishing rigorous verification protocols to mitigate the risks of fraud based on generative artificial intelligence. Key concepts, such as Out-of-Band verification, Vision-Language Models (VLMs) as forensic collaborators, cryptographic provenance, and human training, are integrated into a framework that extends Zero Trust principles to human sensory information. The approach is grounded in empirical findings and academic research, emphasizing that in an era of AI-generated realities, even our eyes and ears can no longer be implicitly trusted without verification. Leaders are called to foster a culture of methodological skepticism to protect organizational integrity in this new threat landscape.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 8 references that are in the map. First posted on arXiv 1 Jul 2025.

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3 of 8 references.

References · 3

  1. 2025Securing GenAI Multi-Agent Systems Against Tool Squatting: A Zero Trust Registry-Based Approachcited by 18
  2. 2025LLMs Are Not Yet Ready for Deepfake Image Detectioncited by 4
  3. 2025Something Just Like TRuST : Toxicity Recognition of Span and Targetcited by 2

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  1. 2026The End of Trust: How Agentic AI Breaks Security Assumptions
  2. 2026AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation
  3. 2026Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era
  4. 2025Beyond Accuracy: Rethinking Hallucination and Regulatory Response in Generative AI
  5. 2026Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Models
  6. 2026"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems
  7. 2026Eroding Trust in Real Speech: A Large-Scale Study of Human Audio Deepfake Perception
  8. 2026Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms