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Sprint projectJun 14, 2025USA

Red Teaming Policy 5 of A Narrow Path: Evaluating the Threat Resilience of AI Licensing Regimes

Desmond, Wyatt Snyder · Team Desmond Wyatt

Submitted to Red Teaming A Narrow Path: ControlAI Policy Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Red Teaming Policy 5 of A Narrow Path: Evaluating the Threat Resilience of AI Licensing Regimes

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This report presents a red teaming analysis of Policy 5 from A Narrow Path, ControlAI’s proposal to delay Artificial Superintelligence (ASI) development through national AI licensing. Using a simplified PASTA threat modeling approach and comparative case studies (FDA, INCB, and California SB 1047), we identified two critical failure modes: regulatory capture and lack of whistleblower protections.

We developed a custom policy CVSS framework to assess cumulative risk exposure across each case. Due to time constraints, we used ChatGPT-assisted simulation to complete the results section and illustrate potential findings from our scoring method.

Our analysis suggests that, as written, Policy 5 is vulnerable to institutional influence and lacks sufficient safeguards to ensure enforcement. We recommend clearer accountability structures, built-in whistleblower protections, and stronger international coordination to make the policy more resilient.

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Does the analysis realistically assess what government agencies, resources, and expertise would be needed to implement these policies? Are the identified implementation challenges specific and grounded in understanding of how similar policies have worked (or failed) in practice? Does the submission adequately consider bureaucratic, technical, and coordination complexities involved in enforcement? How well does the analysis account for real-world constraints like budget limitations, regulatory capture, and inter-agency coordination?

Does the analysis identify specific ways the policies could fail to prevent ASI development or be circumvented by determined actors? How thoroughly does the submission examine edge cases, loopholes, or unintended consequences that could undermine the 20-year goal? Does the assessment consider different threat models (state actors, rogue researchers, corporate actors) and how policies address each? Are the identified failure modes realistic and significant, or primarily theoretical edge cases?

Does the submission cite relevant historical examples of similar policies (nuclear non-proliferation, export controls, dual-use technology regulation) to support its arguments? Are claims backed by empirical data, documented case studies, or credible expert analysis rather than speculation? How well does the analysis draw lessons from comparable regulatory domains to assess likely outcomes? Does the submission avoid making unsupported assertions about what "would" or "could" happen without evidence?

  1. Appreciate the focus on specific failure modes, and the attempt to bring in an analytic framework. The emphasis on need to avoid regulatory capture and protect whistleblowers is fair. However, the discussion was overall fairly difficult to follow and lacked some of the argumentative depth about _why_ these were the most important considerations that need to be addressed that we were looking for.

  2. I think the possibility of regulatory capture of national regulators is a real concern and Gatling and Snyder are right to highlight it.

    Their comparison tables seem useful.

    I think their recommendations on accountability structures and whistleblower protections make sense. International coordination is sort of out of the scope of this policy, but I think it also makes sense to highlight it.

Cite this project

@misc{desmond2025red,
  title = {{Red Teaming Policy 5 of A Narrow Path: Evaluating the Threat Resilience of AI Licensing Regimes}},
  author = {Desmond and Wyatt Snyder},
  year = {2025},
  month = jun,
  note = {Submitted to Red Teaming A Narrow Path: ControlAI Policy Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/red-teaming-policy-5-of-a-narrow-path-evaluating-the-threat-resilience-of-ai-licensing-regimes-zpgj}},
  url = {https://apartresearch.com/sprints/projects/red-teaming-policy-5-of-a-narrow-path-evaluating-the-threat-resilience-of-ai-licensing-regimes-zpgj}
}

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