arXiv subjects

Momoko Shiraishi

Publications and source records attributed to Momoko Shiraishi.

2 recordsLinked to original sources

arXiv2026

PILOT: Command-line Interface Fuzzing via Path-Guided, Iterative Large Language Model Prompting

Command-line interface (CLI) fuzzing tests programs by mutating both command-line options and input file contents, thus enabling discovery of vulnerabilities that only manifest under specific option-input combinations. Prior works of CLI fuzzing face the challenges of generating semantics-rich option strings and input files, which cannot reach deeply embedded target functions. This often leads to a misdetection of such a deep vulnerability using existing CLI fuzzing techniques. In this paper, we design a novel Path-guided, Iterative LLM-Orchestrated Testing framework, called PILOT, to fuzz CLI applications. The key insight is to provide potential call paths to target functions as context to LLM so that it can better generate CLI option strings and input files. Then, PILOT iteratively repeats the process, and provides reached functions as additional context so that target functions are reached. Our evaluation on real-world CLI applications demonstrates that PILOT achieves higher coverage than state-of-the-art fuzzing approaches and discovers 51 zero-day vulnerabilities. We responsibly disclosed all the vulnerabilities to their developers and so far 41 have been confirmed by their developers with 33 being fixed and three assigned CVE identifiers.

cs.CR↗

arXiv2025

SmartC2Rust: Iterative, Feedback-Driven C-to-Rust Translation via Large Language Models for Safety and Equivalence

Memory safety vulnerabilities remain prevalent in today's software systems and one promising solution to mitigate them is to adopt memory-safe languages such as Rust. Due to legacy code written in memory unsafe C, there is strong motivation to translate legacy C code into Rust. Prior works have already shown promise in using Large Language Models (LLMs) for such translations. However, significant challenges persist for LLM-based translation: the translated code often fails to compile, let alone reduce unsafe statements and maintain the semantic functionalities due to inherent limitations of LLMs such as limited token size and inconsistent outputs. In this paper, we design an automated C-to-Rust translation system, called SmartC2Rust, to segment and convert the C code to Rust with memory safety and semantic equivalence. The key insight is to iteratively refine the output Rust code with additional feedback, e.g., compilation errors, segmentation contexts, semantic discrepancies, and memory unsafe statements. Such feedback will gradually improve the quality of generated Rust code, thus mitigating unsafety, inconsistency, and semantic issues. Our evaluation shows that SmartC2Rust significantly decreases the unsafe statements and outperforms prior works in security and semantic equivalence.

cs.SE↗