Owner-Harm: A Missing Threat Model for AI Agent Safety
Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a distinct and commercially consequential threat category: agents harming their own deployers. Real-world incidents illustrate the gap: Slack AI credential exfiltration (Aug 2024), Microsoft 365 Copilot calendar-injection leaks (Jan 2024), and a Meta agent unauthorized forum post exposing operational data (Mar 2026). We propose Owner-Harm, a formal th
Record details
Published: 20 April 2026
Source: arXiv
Category: Research
Topics: Military & security · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (20 April 2026), “Owner-Harm: A Missing Threat Model for AI Agent Safety,” evidence record 5615, https://ethics.ai/record/5615 (originally published by arXiv).
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