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Parse this! Summoning Context-Sensitive Inputs with Goblin
Conference proceeding   Open access

Parse this! Summoning Context-Sensitive Inputs with Goblin

Robert Lorch, Muhammad Daniyal Pirwani Dar, Cesare Tinelli and Omar Chowdhury
Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering, pp.3975-3986
ACM Conferences
ICSE '26: 2026 IEEE/ACM 48th International Conference on Software Engineering
09/11/2026
DOI: 10.1145/3744916.3773270
Appears in  UI Libraries Support Open Access
url
https://doi.org/10.1145/3744916.3773270View
Published (Version of record) Open Access

Abstract

Grammar-based fuzzers have been effective at identifying bugs in software systems with highly structured input formats (e.g., XML). Many existing grammar-based fuzzers rely on context-free grammars (CFGs) to represent input structure; however, CFGs are often insufficient to capture the context-sensitive constraints common in real-world inputs. While application-specific fuzzers can often handle such constraints, they lack the generality needed to adapt to new applications. We present Goblin , a new input generation tool that addresses this gap. Given a context-free grammar annotated with semantic constraints, Goblin generates inputs that both conform to the grammar and satisfy the constraints. A distinguishing feature of Goblin is its support—via an external SMT solver—for constraints expressed in arbitrary SMT theories. Inspired by DPLL-style SAT solvers, Goblin enjoys formal guarantees of solution soundness, solution completeness, and refutation soundness. We evaluate Goblin by comparing it with prior work and by integrating it into a grammar-based network protocol fuzzer.
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