RAISE: Reinforcing Access Control Policy Synthesis in LLMs via Symbolic Evaluation
Translating natural-language access-control requirements into policies requires careful reasoning about permissions, constraints, and exceptions, and even frontier LLMs often produce policies that violate the intended authorization semantics. We construct CedarInstruct, to our knowledge the first dataset that supports both training and semantic evaluation for formally verifiable Cedar policy synthesis. It contains 5,800 scenarios across 44 domains and 1,408 representing a single synthetic organization, each with a verified target policy and an executable verification plan. On this data we introduce RAISE, which trains policy synthesizers from formal verification in two stages, verified su...
| Authors | Yingming Zhou, Adarsh Vatsa, William Eiers |
|---|---|
| Primary category | cs.SE |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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