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We introduce Flux, which shows how logical refinements can work hand in glove with Rust's ownership mechanisms to yield ergonomic type-based verification of low-level pointer manipulating programs. First, we design a novel refined type system for Rust that indexes mutable locations, with pure (immutable) values that can appear in refinements, and then exploits Rust's ownership mechanisms to abstract sub-structural reasoning about locations within Rust's polymorphic type constructors, while supporting strong updates. We formalize the crucial dependency upon Rust's strong aliasing guarantees by exploiting the Stacked Borrows aliasing model to prove that "well-borrowed evaluations of well-typed programs do not get stuck". Second, we implement our type system in Flux, a plug-in to the Rust compiler that exploits the factoring of complex invariants into types and refinements to efficiently synthesize loop annotations-including complex quantified invariants describing the contents of containers-via liquid inference. Third, we evaluate Flux with a benchmark suite of vector manipulating programs and parts of a previously verified secure sandboxing library to demonstrate the advantages of refinement types over program logics as implemented in the state-of-the-art Prusti verifier. While Prusti's more expressive program logic can, in general, verify deep functional correctness specifications, for the lightweight but ubiquitous and important verification use-cases covered by our benchmarks, liquid typing makes verification ergonomic by slashing specification lines by a factor of two, verification time by an order of magnitude, and annotation overhead from up to 24% of code size (average 14%), to nothing at all. 

We present STORM, a web framework that allows developers to build MVC applications with compile-time enforcement of centrally specified data-dependent security policies. STORM ensures security using a Security Typed ORM that refines the (type) abstractions of each layer of the MVC API with logical assertions that describe the data produced and consumed by the underlying operation and the users allowed access to that data. To evaluate the security guarantees of STORM, we build a formally verified reference implementation using the Labeled IO (LIO) IFC framework. We present case studies and end-to- end applications that show how STORM lets developers specify diverse policies while centralizing the trusted code to under 1% of the application, and statically enforces security with modest type annotation overhead, and no run-time cost.