arXiv subjects

Huanyao Rong

Publications and source records attributed to Huanyao Rong.

3 recordsLinked to original sources

arXiv2025

LineBreaker: Finding Token-Inconsistency Bugs with Large Language Models

Token-inconsistency bugs (TIBs) involve the misuse of syntactically valid yet incorrect code tokens, such as misused variables and erroneous function invocations, which can often lead to software bugs. Unlike simple syntactic bugs, TIBs occur at the semantic level and are subtle - sometimes they remain undetected for years. Traditional detection methods, such as static analysis and dynamic testing, often struggle with TIBs due to their versatile and context-dependent nature. However, advancements in large language models (LLMs) like GPT-4 present new opportunities for automating TIB detection by leveraging these models' semantic understanding capabilities. This paper reports the first systematic measurement of LLMs' capabilities in detecting TIBs, revealing that while GPT-4 shows promise, it exhibits limitations in precision and scalability. Specifically, its detection capability is undermined by the model's tendency to focus on the code snippets that do not contain TIBs; its scalability concern arises from GPT-4's high cost and the massive amount of code requiring inspection. To address these challenges, we introduce \name, a novel and cascaded TIB detection system. \name leverages smaller, code-specific, and highly efficient language models to filter out large numbers of code snippets unlikely to contain TIBs, thereby significantly enhancing the system's performance in terms of precision, recall, and scalability. We evaluated \name on 154 Python and C GitHub repositories, each with over 1,000 stars, uncovering 123 new flaws, 45\% of which could be exploited to disrupt program functionalities. Out of our 69 submitted fixes, 41 have already been confirmed or merged.

cs.CR↗

arXiv2024

Disassembling Obfuscated Executables with LLM

Disassembly is a challenging task, particularly for obfuscated executables containing junk bytes, which is designed to induce disassembly errors. Existing solutions rely on heuristics or leverage machine learning techniques, but only achieve limited successes. Fundamentally, such obfuscation cannot be defeated without in-depth understanding of the binary executable's semantics, which is made possible by the emergence of large language models (LLMs). In this paper, we present DisasLLM, a novel LLM-driven dissembler to overcome the challenge in analyzing obfuscated executables. DisasLLM consists of two components: an LLM-based classifier that determines whether an instruction in an assembly code snippet is correctly decoded, and a disassembly strategy that leverages this model to disassemble obfuscated executables end-to-end. We evaluated DisasLLM on a set of heavily obfuscated executables, which is shown to significantly outperform other state-of-the-art disassembly solutions.

cs.CR↗

arXiv2024

Toward Unbiased Multiple-Target Fuzzing with Path Diversity

In this paper, we propose a novel directed fuzzing solution named AFLRun, which features target path-diversity metric and unbiased energy assignment. Firstly, we develop a new coverage metric by maintaining extra virgin map for each covered target to track the coverage status of seeds that hit the target. This approach enables the storage of waypoints into the corpus that hit a target through interesting path, thus enriching the path diversity for each target. Additionally, we propose a corpus-level energy assignment strategy that guarantees fairness for each target. AFLRun starts with uniform target weight and propagates this weight to seeds to get a desired seed weight distribution. By assigning energy to each seed in the corpus according to such desired distribution, a precise and unbiased energy assignment can be achieved. We built a prototype system and assessed its performance using a standard benchmark and several extensively fuzzed real-world applications. The evaluation results demonstrate that AFLRun outperforms state-of-the-art fuzzers in terms of vulnerability detection, both in quantity and speed. Moreover, AFLRun uncovers 29 previously unidentified vulnerabilities, including 8 CVEs, across four distinct programs.

cs.CR↗