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Secure human oversight of AI: Threat modeling in a socio-technical context

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arxiv 2509.12290 v3 pith:JHED3N6F submitted 2025-09-15 cs.CR cs.CYcs.HC

Secure human oversight of AI: Threat modeling in a socio-technical context

classification cs.CR cs.CYcs.HC
keywords humanoversightsecuritymodelingthreatattackriskssecure
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Human oversight of AI is promoted as a safeguard against risks such as inaccurate outputs, system malfunctions, or violations of fundamental rights, and is mandated in regulation like the European AI Act. Yet debates on human oversight have largely focused on its effectiveness, while overlooking a critical dimension: the security of human oversight. We argue that human oversight creates a new attack surface within the safety, security, and accountability architecture of AI operations. Drawing on cybersecurity perspectives, we model human oversight as an IT application for the purpose of systematic threat modeling of the human oversight process. Threat modeling allows us to identify security risks within human oversight and points towards possible mitigation strategies. Our contributions are: (1) introducing a security perspective on human oversight, (2) offering researchers and practitioners guidance on how to approach their human oversight applications from a security point of view, and (3) providing a systematic overview of attack vectors and hardening strategies to enable secure human oversight of AI.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Keeping an Eye on AI: A Framework for Effective Human Oversight of AI Systems

    cs.CY 2026-04 unverdicted novelty 6.0

    The paper introduces a foundational framework with definition, architecture, and processes for effective human oversight of AI systems, plus a documentation template and open research challenges.

  2. When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Making

    cs.AI 2026-02 unverdicted novelty 6.0

    Adversarial explanation attacks preserve nearly all human trust in wrong AI outputs by using persuasive framing, shown in a study varying reasoning, evidence, style, and format with over 200 participants.