SMT solvers are essential for applications in artificial intelligence, software verification, and optimisation. However, no single solver excels across all formula types, and different applications may require the use of different solvers. While the SMT-LIB language enables multi-solver support, it also incurs heavy I/O overhead. To address this, we introduce Smt.ml , an SMT-solver frontend for OCaml that simplifies integration with various solvers through a consistent interface. Its parametric encoding facilitates the easy addition of new solver backends, while optimisations like formula simplification, result caching, and detailed error feedback enhance performance and usability. Furthermore, Smt.ml is the only SMT frontend that includes a simplification-management engine for streamlining the integration of new formula simplifications and the verification of their correctness. Our evaluation demonstrates that Smt.ml ’s results are consistent with those of its backend solvers and that its optimisations are highly effective on formulas generated from the symbolic execution of an extensive program-analysis benchmark.

The Node.js ecosystem, with its growing popularity and increasing exposure to security vulnerabilities, has a pressing need for more effective security analysis tools. To reduce false positives, recent works on detecting vulnerabilities in Node.js packages have developed synthesis algorithms to generate proof-of-concept exploits. However, these tools focus mainly on vulnerabilities that can be triggered by a single direct call to an exported function of the analyzed package, failing to generate exploits that require more complex interactions. In this paper, we present Explode.js , the first tool capable of synthesizing exploits that include complex call sequences to trigger vulnerabilities in Node.js packages. By combining static analysis and symbolic execution, Explode.js generates functional exploits that confirm the existence of command, code injection, prototype pollution, and path traversal vulnerabilities, effectively eliminating false positives. The results of evaluating Explode.js on two state-of-the-art datasets of Node.js packages with confirmed vulnerabilities show that it generates significantly more exploits than its main competitor tools. Furthermore, when applied to real-world Node.js packages, Explode.js uncovered 44 zero-day vulnerabilities, with 4 new CVEs.

WebAssembly (Wasm) is a new binary instruction format that allows targeted compiled code written in high-level languages to be executed by the browser’s JavaScript engine with near-native speed. Despite its clear performance advantages, Wasm opens up the opportunity for bugs or security vulnerabilities to be introduced into Web programs, as pre-existing issues in programs written in unsafe languages can be transferred down to cross-compiled binaries. The source code of such binaries is frequently unavailable for static analysis, creating the demand for tools that can directly tackle Wasm code. Despite this potentially security-critical situation, there is still a noticeable lack of tool support for analysing Wasm binaries. We present WASP, a symbolic execution engine for testing Wasm modules, which works directly on Wasm code and was built on top of a standard-compliant Wasm reference implementation. WASP was thoroughly evaluated: it was used to symbolically test a generic data-structure library for C and the Amazon Encryption SDK for C, demonstrating that it can find bugs and generate high-coverage testing inputs for real-world C applications; and was further tested against the Test-Comp benchmark, obtaining results comparable to well-established symbolic execution and testing tools for C.

This work proposes a business-oriented approach to designing IT infrastructure in an e-commerce context subject to load surges. The main difference between the proposed approach and conventional ones is that it includes the negative business impact - loss - incurred due to IT infrastructure failures and performance degradation. The approach minimizes the sum of infrastructure cost and business losses, rather than only considering infrastructure cost. A complete example scenario shows the value of the method