Repairing Broken Access Control in Web Applications with LLMs: An Empirical Benchmark Study

Abstract

Broken access control (BAC) ranks among the most severe web-application vulnerability classes, yet its automated repair remains largely unexplored. Unlike memory-safety vulnerabilities, BAC is a logic vulnerability whose repair demands a deep understanding of an application's access-control model and business semantics, which traditional automated vulnerability repair techniques handle poorly. Large language models (LLMs) open a promising avenue for this problem, but no standard benchmark exists for studying how well they repair BAC vulnerabilities. We construct BAC-Bench, the first benchmark for BAC repair, consisting of 46 real-world BAC vulnerabilities drawn from six widely used web applications spanning four programming languages. Each vulnerability is equipped with a sandboxed runtime environment, a PoC validation program and a functional test program. We further propose a BAC repair-strategy framework that characterizes any BAC patch along four dimensions, Position, Coverage, Mechanism, and Content. Evaluating eight base LLMs and two commercial LLM agents on BAC-Bench, we find that single-turn base LLMs repair 37.68% of the vulnerabilities on average, while two minimal scaffolds, Apply-Feedback and Navigate-and-Locate, raise this average to 66.03%, with the best base LLM reaching 75.36%, close to the 78.99% of the best agent. Notably, some LLM-generated patches surpass the official patches, and one incidentally closed a zero-day vulnerability that had lain dormant for three years. Our findings delineate the current capability boundary of LLM-based BAC repair and offer guidance toward its practical adoption.