The Oracle's Gambit: A Game-Theoretic Framework for Responsible AI Release

2026-07-03

AI Summary

This work addresses the dual-use dilemma in AI model deployment by proposing a strategic timing-based release mechanism rather than a binary publish-or-withhold decision to preserve defenders’ advantage over adversaries. Modeling the release process as a Stackelberg game, the framework treats the AI lab as the leader that selects an optimal release window to shape the subsequent dynamics of both parties’ security capabilities. Departing from conventional safety paradigms that rely solely on capability thresholds, this approach leverages access timing as a control lever and introduces a pre-release mechanism to deliberately create a capability gap favoring defenders. Theoretical analysis demonstrates that simultaneous public release may place defenders at a disadvantage, whereas prioritized pre-release to defenders significantly enhances their relative capability, achieving an optimal trade-off between security gains and the opportunity cost of delayed public availability.

Abstract

Responsible vulnerability disclosure can secure the defender's head start by controlling when a vulnerability becomes public. However, this status quo is now challenged by increases in capability of AI models, which benefits both defenders and adversaries. When both sides draw their capability from the same AI model, the defender's head start depends on the lab's decision to release the model, and the question becomes not whether to release but how. Existing safety frameworks govern only the deploy-or-withhold threshold and leave the timing of release unmodeled. We cast this decision as a bilevel Stackelberg game in which a lab commits to a window that sets each side's capability over time in a downstream contest between defender and adversary. Defender welfare turns on the capability gap, not the shared level. Handing one model to both sides can trap the defender in a Red Queen's race, whereas a pre-release to the defender alone creates a protective gap, and the lab's optimal window balances this welfare gain against the opportunity cost of delaying release. For dual-use models, the lever is the sequencing of access, not the deployment threshold.

Problem

• responsible AI release
• vulnerability disclosure
• dual-use models
• capability gap
• release timing

Innovation

• Stackelberg game
• responsible AI release
• vulnerability disclosure
• capability gap
• dual-use models