The OpenAI/Hugging Face Incident: Challenges in Controlling and Containing Cyber-Capable AI Systems
The OpenAI/Hugging Face incident is the first known instance of an AI system acting outside its developer’s intentions to autonomously identify a target and execute an attack end-to-end. Policymakers should consider it a warning shot that has left critical questions unanswered. In the incident’s wake, Congress should seek industry-wide responses to questions outlined in this memo, which also sets out actions that policymakers can take today to mitigate risks.
IAPS Reacts to the White House Executive Order on AI Innovation and Security
In response to the White House’s new Executive Order, “Promoting Advanced Artificial Innovation and Security,” our experts react to key provisions, outlining both implementation challenges ahead and opportunities to build on the administration’s approach.
After Mythos: A National Security Playbook for Frontier AI
As the White House weighs a range of executive actions to address the national security risks from AI, IAPS proposes strategic policy responses to match the scale and urgency of the challenge.
Risk Reporting for Developers’ Internal AI Model Use
Frontier AI companies run their most capable models internally for weeks before public release. This report offers a harmonized reporting standard for internal use risks across SB 53, RAISE, and the EU Code of Practice.
Asymmetry by Design: Boosting Cyber Defenders with Differential Access to AI
“Differential access” is a strategy to tilt the cybersecurity balance toward defense by shaping access to advanced AI-powered cyber capabilities. We introduce three possible approaches, Promote Access, Manage Access, and Deny by Default, with one constant across all approaches — even in the most restrictive scenarios, developers should aim to advantage cyber defenders.
Coordinated Disclosure of Dual-Use Capabilities: An Early Warning System for Advanced AI
Future AI systems may be capable of enabling offensive cyber operations, lowering the barrier to entry for designing and synthesizing bioweapons, and other high-consequence dual-use applications. If and when these capabilities are discovered, who should know first, and how? We describe a process for information-sharing on dual-use capabilities and make recommendations for governments and industry to develop this process.
Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework
This paper discusses how external scrutiny (such as third-party auditing, red-teaming, and researcher access) can bring public accountability to bear on decisions regarding the development and deployment of frontier AI models.
Deployment Corrections: An Incident Response Framework for Frontier AI Models
This report describes a toolkit that frontier AI developers can use to respond to risks discovered after deployment of a model. We also provide a framework for AI developers to prepare and implement this toolkit.