Symbolic execution (SE) tools often rely on intermediate languages (ILs) to support multiple programming languages, promising reusability and efficiency. In practice, this approach introduces trade-offs between performance, accuracy, and language feature support. We argue that building SE engines directly for each source language is both simpler and more effective. We present Soteria, a lightweight OCaml library for writing SE engines in a functional style, without compromising on performance, accuracy or feature support. Soteria enables developers to construct SE engines that operate directly over source-language semantics, offering configurability, compositional reasoning, and ease of implementation. Using Soteria, we develop Soteria-Rust, the first Rust SE engine supporting TreeBorrows (the intricate aliasing model of Rust), and Soteria-C, a compositional SE engine for C. Both tools are competitive with or outperform state-of-the-art tools such as Kani, Infer.Pulse, CBMC and Gillian-C in performance and the number of bugs detected. We formalise the theoretical foundations of Soteria and prove its soundness, demonstrating that sound, efficient, accurate, and expressive SE can be achieved without the compromises of ILs.

Multiple successful compositional symbolic execution (CSE) tools and platforms exploit separation logic (SL) for compositional verification and/or incorrectness separation logic (ISL) for compositional bug-finding, including VeriFast, Viper, Gillian, CN, and Infer-Pulse. Previous work on the Gillian platform, the only CSE platform that is parametric on the memory model, meaning that it can be instantiated to different memory models, suggests that the ability to use custom memory models allows for more flexibility in supporting analysis of a wide range of programming languages, for implementing custom automation, and for improving performance. However, the literature lacks a satisfactory formal foundation for memory-model-parametric CSE platforms. In this paper, inspired by Gillian, we provide a new formal foundation for memory-model-parametric CSE platforms. Our foundation advances the state of the art in four ways. First, we mechanise our foundation (in the interactive theorem prover Rocq). Second, we validate our foundation by instantiating it to a broad range of memory models, including models for C and CHERI. Third, whereas previous memory-model-parametric work has only covered SL analyses, we cover both SL and ISL analyses. Fourth, our foundation is based on standard definitions of SL and ISL (including definitions of function specification validity, to ensure sound interoperation with other tools and platforms also based on standard definitions).