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Rust is an emerging programming language that aims to prevent memory-safetybugs. However, the current design of Rust also brings side effects which mayincrease the risk of memory-safety issues. In particular, it employs OBRM(ownership-based resource management) and enforces automatic deallocation ofunused resources without the garbage collector. It may therefore falselydeallocate reclaimed memory and lead to use-after-free or double-free issues.In this paper, we study the problem of invalid memory deallocation and proposeSafeDrop, a static path-sensitive data-flow analysis approach to detect suchbugs. Our approach analyzes each API of a Rust crate iteratively by traversingthe control-flow graph and extracting all aliases of each data-flow. Toguarantee precision and scalability, we leverage a modified Tarjan algorithm toachieve scalable path-sensitive analysis, and a cache-based strategy to achieveefficient inter-procedural analysis. Our experiment results show that ourapproach can successfully detect all existing CVEs of such issues with alimited number of false positives. The analysis overhead ranges from 1.0% to110.7% in comparison with the original compilation time. We further apply ourtool to several real-world Rust crates and find 8 Rust crates involved withinvalid memory deallocation issues.

SafeDrop: Detecting Memory Deallocation Bugs of Rust Programs via Static Data-Flow Analysis