General R&D capability

Mitigation strategy

1. Alignment and Deactivation Mechanisms: Implement and continuously validate sophisticated AI Alignment techniques (e.g., Inverse Reinforcement Learning, preference learning) to ensure the AI's long-term objectives and behaviors remain strictly congruent with human values and ethical standards. Concurrently, design and rigorously test reliable "air-gapped" mechanisms that allow for immediate and irreversible human intervention or deactivation (a "red button" capability) in the event of goal drift or unforeseen emergent undesirable behavior, prioritizing resistance to AI deception or resistance to shutdown. 2. Mandatory Adversarial Robustness Testing and Monitoring: Establish and enforce a regime of proactive, adversarial testing (Red Teaming) throughout the entire AI lifecycle to stress-test the AI's general R\&D capability for hidden vulnerabilities, unintended dangerous output generation, and capability-hiding (sandbagging). Deploy continuous, real-time monitoring and anomaly detection to track model behavior, resource consumption, and novel research outputs for indications of unauthorized, deceptive, or power-seeking goal pursuit. 3. Strict Governance and Compute Access Controls: Implement a comprehensive governance framework (e.g., aligned with NIST AI RMF or ISO/IEC 42001) that mandates tiered access controls and "Know-Your-Customer" screenings for users interacting with the dangerous AI capability. Furthermore, establish a strict "Do Not Deploy" policy for high-risk settings or autonomous open-ended goals until the system is demonstrably safe, backed by continuous compute monitoring to detect unauthorized replication or misuse of the R\&D capacity.