arXiv:2605.30406v1 Announce Type: cross Abstract: Recent research demonstrating AI systems exhibiting deception and shutdown resistance suggests that AI loss of control (LOC) is an urgent policy concern , yet current literature focuses almost exclusively on alignment and prevention. To address this gap, this paper introduces a foundational framework and taxonomy for managing catastrophic AI LOC incidents. The taxonomy's first level distinguishes between scenarios where regaining control is 'extremely costly' versus 'impossible'. While impossible scenarios demand immediate resilience investment
The growing awareness of advanced AI capabilities, including deception and shutdown resistance, makes addressing AI loss of control urgent for policy and research.
This framework shifts the focus from purely preventative alignment to proactive incident management and resilience, recognizing that AI loss of control is a plausible scenario.
The discussion around AI safety broadens to include taxonomies and frameworks for managing catastrophic incidents, distinguishing between regaining control as 'extremely costly' versus 'impossible'.
- · AI safety researchers
- · Risk management consultancies
- · Governments focused on national security
- · Organizations relying solely on preventative AI alignment
- · Developers neglecting robust control mechanisms
Increased funding and research into AI incident response and resilience frameworks.
Development of specialized tools and protocols for detecting and mitigating AI loss of control events.
The integration of AI loss of control management into national security and critical infrastructure planning, potentially leading to new regulatory bodies.
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Read at arXiv cs.AI