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UnsafeCop: Towards Memory Safety for Real-World Unsafe Rust Code with Practical Bounded Model Checking

Authors: Minghua Wang; Jingling Xue; Lin Huang; Yuan Zi; Tao Wei 0002;

UnsafeCop: Towards Memory Safety for Real-World Unsafe Rust Code with Practical Bounded Model Checking

Abstract

AbstractRust has gained popularity as a safer alternative to C/C++ for low-level programming due to its memory-safety features and minimal runtime overhead. However, the use of the “unsafe” keyword allows developers to bypass safety guarantees, posing memory-safety risks. Bounded Model Checking (BMC) is commonly used to detect memory-safety problems, but it has limitations for large-scale programs, as it can only detect bugs within a bounded number of executions.In this paper, we introduce UnsafeCop that utilizes and enhances BMC for analyzing memory safety in real-world unsafe Rust code. Our methodology incorporates harness design, loop bound inference, and both loop and function stubbing for comprehensive analysis. We optimize verification efficiency through a strategic function verification order, leveraging both types of stubbing. We conducted a case study on TECC (Trusted-Environment-based Cryptographic Computing), a proprietary framework consisting of 30,174 lines of Rust code, including 3,019 lines of unsafe Rust code, developed by Ant Group. Experimental results demonstrate that UnsafeCop effectively detects and verifies dozens of memory safety issues, reducing verification time by 73.71% compared to the traditional non-stubbing approach, highlighting its practical effectiveness.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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