Open-source libraries are widely used in modern software development, introducing significant security vulnerabilities. While static analysis tools can identify potential vulnerabilities at scale, they often generate overwhelming reports with high false positive rates. Automated Exploit Generation (AEG) emerges as a promising solution to confirm vulnerability authenticity by generating an exploit. However, traditional AEG approaches based on fuzzing or symbolic execution face path coverage and constraint-solving problems. Although LLMs show great potential for AEG, how to effectively leverage them to comprehend vulnerabilities and generate corresponding exploits is still an open question.

Static program analysis for JavaScript is more difficult than for many other programming languages. One of the main reasons is the presence of dynamic property accesses that read and write object properties via dynamically computed property names. To ensure scalability and precision, existing state-of-the-art analyses for JavaScript mostly ignore these operations although it results in missed call edges and aliasing relations. We present a novel dynamic analysis technique named approximate interpretation that is designed to efficiently and fully automatically infer likely determinate facts about dynamic property accesses, in particular those that occur in complex library API initialization code, and how to use the produced information in static analysis to recover much of the abstract information that is otherwise missed. Our implementation of the technique and experiments on 141 real-world Node.js-based JavaScript applications and libraries show that the approach leads to significant improvements in call graph construction. On average the use of approximate interpretation leads to 55.1 % more call edges, 21.8 % more reachable functions, 17.7 % more resolved call sites, and only 1.5 % fewer monomorphic call sites. For 36 JavaScript projects where dynamic call graphs are available, average analysis recall is improved from 75.9 % to 88.1 % with a negligible reduction in precision.