MASFuzzer: Fuzz Driver Generation and Adaptive Scheduling via Multidimensional API Sequences
- URL: http://arxiv.org/abs/2604.17977v1
- Date: Mon, 20 Apr 2026 08:57:49 GMT
- Title: MASFuzzer: Fuzz Driver Generation and Adaptive Scheduling via Multidimensional API Sequences
- Abstract summary: MASFUZZER is a fuzzing framework that integrates multidimensional API sequence construction with adaptive fuzzing scheduling strategies.<n>We evaluate MASFUZZER on 12 widely used open-source libraries.
- Score: 13.097146027708511
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fuzz testing of software libraries relies on fuzz drivers to invoke library APIs. Traditionally, these drivers are written manually by developers - a process that is time-consuming and often inadequate for exercising complex program behaviors. While recent studies have explored the use of Large Language Models (LLMs) to automate fuzz driver generation, the resulting drivers often fail to cover deep program branches. To address these challenges, we propose MASFUZZER, a fuzzing framework that integrates multidimensional API sequence construction with adaptive fuzzing scheduling strategies to improve library testing. At its core, MASFUZZER synthesizes context-relevant API call sequences by referring to API usage examples from the codebase and applying mutation-propagation-based and semantic-aware API sequence mining. These multidimensional API sequences serve as the basis for LLMs to generate effective initial drivers. In addition, MASFUZZER incorporates a coverage-guided scheduler that prioritizes testing time for the most promising drivers, along with a driver mutation strategy to evolve them. This enables systematic generation of fuzz drivers to explore previously untested code regions. We evaluate MASFUZZER on 12 widely used open-source libraries. The results show that MASFUZZER achieves 8.54 percent higher code coverage than state-of-the-art techniques. Moreover, MASFUZZER uncovers 16 previously unknown vulnerabilities in extensively tested libraries, with 14 confirmed by developers and 9 assigned CVE identifiers. These results indicate that MASFUZZER provides an efficient and practical approach for fuzzing software libraries.
Related papers
- Framework-Aware Code Generation with API Knowledge Graph-Constructed Data: A Study on HarmonyOS [52.483888557864326]
APIKG4SYN is a framework designed to exploit API knowledge graphs for the construction of API-oriented question-code pairs.<n>We build the first benchmark for HarmonyOS code generation using APIKG4SYN.
arXiv Detail & Related papers (2025-11-29T08:13:54Z) - Scheduzz: Constraint-based Fuzz Driver Generation with Dual Scheduling [16.453200060615234]
We propose a novel automatic library fuzzing technique, Scheduzz, to understand rational usage of libraries and extract API combination constraints.<n>Scheduzz significantly reduces computational overhead and outperforms UTopia on 16 out of 21 libraries.<n>It achieves 1.62x, 1.50x, and 1.89x higher overall coverage than the state-of-the-art techniques CKGFuzzer, Promptfuzz, and the handcrafted project OSS-Fuzz.
arXiv Detail & Related papers (2025-07-24T10:51:11Z) - Your Fix Is My Exploit: Enabling Comprehensive DL Library API Fuzzing with Large Language Models [49.214291813478695]
Deep learning (DL) libraries, widely used in AI applications, often contain vulnerabilities like overflows and use buffer-free errors.<n>Traditional fuzzing struggles with the complexity and API diversity of DL libraries.<n>We propose DFUZZ, an LLM-driven fuzzing approach for DL libraries.
arXiv Detail & Related papers (2025-01-08T07:07:22Z) - ExploraCoder: Advancing code generation for multiple unseen APIs via planning and chained exploration [70.26807758443675]
ExploraCoder is a training-free framework that empowers large language models to invoke unseen APIs in code solution.<n> Experimental results demonstrate that ExploraCoder significantly improves performance for models lacking prior API knowledge.
arXiv Detail & Related papers (2024-12-06T19:00:15Z) - CKGFuzzer: LLM-Based Fuzz Driver Generation Enhanced By Code Knowledge Graph [29.490817477791357]
We propose an automated fuzz testing method driven by a code knowledge graph and powered by an intelligent agent system.<n>The code knowledge graph is constructed through interprocedural program analysis, where each node in the graph represents a code entity.<n> CKGFuzzer achieved an average improvement of 8.73% in code coverage compared to state-of-the-art techniques.
arXiv Detail & Related papers (2024-11-18T12:41:16Z) - FuzzCoder: Byte-level Fuzzing Test via Large Language Model [46.18191648883695]
We propose to adopt fine-tuned large language models (FuzzCoder) to learn patterns in the input files from successful attacks.
FuzzCoder can predict mutation locations and strategies locations in input files to trigger abnormal behaviors of the program.
arXiv Detail & Related papers (2024-09-03T14:40:31Z) - Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph [83.90988015005934]
Uncertainty quantification is a key element of machine learning applications.<n>We introduce a novel benchmark that implements a collection of state-of-the-art UQ baselines.<n>We conduct a large-scale empirical investigation of UQ and normalization techniques across eleven tasks, identifying the most effective approaches.
arXiv Detail & Related papers (2024-06-21T20:06:31Z) - Prompt Fuzzing for Fuzz Driver Generation [6.238058387665971]
We propose PromptFuzz, a coverage-guided fuzzer for prompt fuzzing.
It iteratively generates fuzz drivers to explore undiscovered library code.
PromptFuzz achieved 1.61 and 1.63 times higher branch coverage than OSS-Fuzz and Hopper, respectively.
arXiv Detail & Related papers (2023-12-29T16:43:51Z) - HOPPER: Interpretative Fuzzing for Libraries [6.36596812288503]
HOPPER can fuzz libraries without requiring any domain knowledge.
It transforms the problem of library fuzzing into the problem of interpreter fuzzing.
arXiv Detail & Related papers (2023-09-07T06:11:18Z) - Private-Library-Oriented Code Generation with Large Language Models [52.73999698194344]
This paper focuses on utilizing large language models (LLMs) for code generation in private libraries.
We propose a novel framework that emulates the process of programmers writing private code.
We create four private library benchmarks, including TorchDataEval, TorchDataComplexEval, MonkeyEval, and BeatNumEval.
arXiv Detail & Related papers (2023-07-28T07:43:13Z) - How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation [31.77886516971502]
This study is the first in-depth study targeting the important issues of using LLMs to generate effective fuzz drivers.
Our study evaluated 736,430 generated fuzz drivers, with 0.85 billion token costs ($8,000+ charged tokens)
Our insights have been implemented to improve the OSS-Fuzz-Gen project, facilitating practical fuzz driver generation in industry.
arXiv Detail & Related papers (2023-07-24T01:49:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.