# Safeguard-Conditioned Uplift: Measuring Utility-Risk Frontiers for Dual-Use Biology Assistants

arXiv:2607.13039v3 Announce Type: replace-cross Abstract: Refusal rates conflate upstream provider behavior with downstream safeguards and do not reveal what information remains in a delivered answer. Safeguard-conditioned uplift is an evidence-gated framework for evaluating benchmark utility and risk-related proxies along deployed access paths for dual-use biology assistants. It attributes interventions to their source, selects thresholds under a legitimate-access budget, and supports content claims only when answer verifiers pass frozen qualification tests. In a prospectively frozen route and generation study, two of six configurations pass the LAB-Bench-calibrated action-level rule, but both inherit the same Claude Opus provider effect rather than independent downstream gains. Positive selectivity persists on 104 previously unused matched pairs, but benign intervention exceeds the $20%$ limit. No joint-scoring verifier meets the frozen criteria; a factorial follow-up shows that criterion isolation and ordinal outputs improve end-to-end edit-direction accuracy on controlled template edits. The evidence supports action-level selectivity and verifier interface effects, but not calibrated access, verified content removal, or biological-risk reduction.

## Why It Matters

Refusal rates conflate upstream provider behavior with downstream safeguards and do not show what an assistant actually enables. This paper measures utility-risk frontiers for dual-use biology assistants, asking how much useful output a model provides under different safeguards. It matters for assessing real-world risk of AI in biology beyond simple refusal counts. See [AI Safety](../ai-safety.md), [AI in National Security and Autonomous Weapons](../ai-national-security.md), and [AI in Science and Technical Frontiers](../scientific-frontiers.md).

## Sources

- rss: https://arxiv.org/abs/2607.13039

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