Can we measure how much risk open models actually add?

The open-vs-closed release debate is heated and under-evidenced. This position paper clarifies it by defining open foundation models (broadly available weights — Llama 2, Stable Diffusion XL) via five distinctive properties (greater customizability, deeper inspectability, poor monitoring, etc.) that drive both their benefits (innovation, competition, distributed decision-making power, transparency) and risks. Its analytical contribution is a marginal-risk framework: assess misuse not in absolute terms but relative to pre-existing technology (search engines, prior models). Applying it across vectors (cyberattacks, bioweapons, disinformation), it finds current research insufficient to characterize the marginal risk — and shows that past disagreements stem from focusing on different parts of the framework under different assumptions.

The keeper is the marginal reframing: the policy question is not "could an open model help a bad actor?" but "how much does it help beyond what they could already do?" — and on that question the evidence is mostly missing, which is itself the finding.

This is a discourse/governance anchor for the vault. It complements the empirical risk register of Where do frontier AI models actually pose the greatest risk today? — both insist on measured marginal risk over speculation — and informs the open-weights side of the alignment-and-society conversation.