面向API交互的Python语义化模糊测试方案

李政浩; 闫新成; 王继刚; 贾相堃; 苏璞睿

doi:10.12263/DZXB.20250614

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面向API交互的Python语义化模糊测试方案

学术论文 | 更新时间：2026-06-16

- 面向API交互的Python语义化模糊测试方案
- Python Semantic Fuzzing Solution for API Interaction
- 电子学报 2026年54卷第3期页码：1280-1295
- 作者机构：
  
  1.中国科学院大学，北京 100049
  2.中国科学院软件研究所，北京 100190
  3.基础软件与系统重点实验室（中国科学院），北京 100049
  4.中兴通讯股份有限公司，广东深圳 518057
  5.东南大学网络空间安全学院，江苏南京 211189
- 作者简介：
  
  李政浩男，1997年12月出生于山西省太原市。现为中国科学院软件研究所博士研究生、CCF学生会员。主要研究领域为系统安全、漏洞挖掘、漏洞分析。E-mail: zhenghao2021@iscas.ac.cn
  闫新成男，1977年12月出生于甘肃省武威市。现为东南大学网络空间安全学院校外导师。主要研究领域为通信网络安全和AI安全。E-mail: 230239737@seu.edu.cn
  王继刚男，1978年7月出生于安徽省马鞍山市。现为中兴通讯股份有限公司首席专家。主要研究领域网络安全、具身智能。E-mail: wang.jigang@zte.com.cn
  贾相堃男，1990年2月出生于河北省邯郸市。博士，现为中国科学院软件所副研究员，CCF专业会员。主要研究领域为系统安全、漏洞挖掘与分析。E-mail: xiangkun@iscas.ac.cn
  苏璞睿男，1976年12月出生于湖北省宜昌市。博士，博士生导师，现为中国科学院软件研究所研究员、CF专业会员。主要研究领域为系统安全、恶意代码分析、漏洞挖掘。中国电子学会会员编号：E190029904M。E-mail: purui@iscas.ac.cn
- 基金信息：
  
  国家自然科学基金(62472414;62232016)
- DOI：10.12263/DZXB.20250614
  中图分类号： TN915.08;TP311.5
- 收稿：2025-07-10，
  
  录用：2026-03-16，
  
  纸质出版：2026-03-25
- 稿件说明：
移动端阅览
李政浩, 闫新成, 王继刚, 等. 面向API交互的Python语义化模糊测试方案[J]. 电子学报, 2026, 54(03): 1280-1295.

LI Zhenghao, YAN Xincheng, WANG Jigang, et al. Python Semantic Fuzzing Solution for API Interaction[J]. Acta Electronica Sinica, 2026, 54(03): 1280-1295.
李政浩, 闫新成, 王继刚, 等. 面向API交互的Python语义化模糊测试方案[J]. 电子学报, 2026, 54(03): 1280-1295. DOI：10.12263/DZXB.20250614

LI Zhenghao, YAN Xincheng, WANG Jigang, et al. Python Semantic Fuzzing Solution for API Interaction[J]. Acta Electronica Sinica, 2026, 54(03): 1280-1295. DOI：10.12263/DZXB.20250614

摘要

Python第三方库在现代软件生态中被广泛应用，其面临的安全威胁也变得日益严重，如Python包索引平台（Python Package Index，PyPI）中漏洞数量持续快速上升、依赖网络高度复杂，导致单一漏洞易通过依赖链波及大量下游项目。现有模糊测试工具在测试Python第三方库时，复杂的应用程序编程接口（Application Programming Interface，API）交互场景下接口间的隐式数据流关系会导致探索能力不足；Python语言自身的动态特性，例如鸭子类型及反射机制进一步降低了静态分析的准确性，导致对多重条件保护的复杂约束探索能力低下，使得漏洞发现能力受限。此外，传统的模糊测试引擎往往采用随机变异的策略，因而无法针对测试目标进行定制化变异，使得测试资源大量消耗在无状态依赖的低价值浅层路径上，难以探索深层代码漏洞。为此，本文提出PyBoros——一种针对Python第三方库的高效模糊测试框架。该框架通过构建包级API依赖图，并结合团渗流方法（Clique Percolation Method，CPM）进行子图分割，以精准捕获隐式依赖；在此基础上，使用大语言模型生成语义丰富的初始模糊测试驱动以捕捉隐式依赖；采用停滞触发式非阻塞动态分析，即以覆盖率增长停滞作为信号，按需捕获运行时代码状态与变量快照等约束上下文信息，并利用大语言模型进行智能约束推理以产生突破性种子；在模糊测试的过程中，通过引入API n-gram覆盖指导与分支价值评分相结合的资源调度策略，引导测试资源优先向高价值路径探索。我们在平均Github Star数超过2 000的10个真实Python第三方库中进行了测试，PyBoros发现20个真实漏洞（其中10个为0-day），漏洞检出数量较Atheris提高100%；边覆盖率较Atheris提高8.57%；初始生成的模糊测试驱动的API覆盖数达到Fuzz4All的1.8倍；对四种大语言模型（包含两种开源、两种闭源）生成的模糊测试驱动平均接受率为72.6%；在额外的扰动实验中，模糊测试驱动的接受率即使在10%静态分析扰动攻击场景也仅下降了3.8%，仍保持较高鲁棒性。总体而言，PyBoros模糊测试框架为Python第三方库的安全分析提供了一种高效实用的方法。

Abstract

Third-party Python libraries are extensively utilized in modern software ecosystems

and the security threats they face have become increasingly severe. As the number of vulnerabilities in Python package index (PyPI) continues to surge and dependency networks grow highly complex

a single vulnerability can easily propagate through dependency chains and affect a large number of downstream projects. When testing third-party Python libraries

existing fuzzing tools suffer from insufficient exploration capability due to the implicit data flow relationships between interfaces under complex application programming interface (API) interaction scenarios. Meanwhile

Python’s inherent dynamic features—such as duck typing and reflection—further reduce the accuracy of static analysis. This results in weak exploration of complex constraints protected by multiple conditional checks and ultimately limits vulnerability discovery capabilities. Traditional fuzzing engines often adopt random mutation strategies

making them incapable of performing targeted customized mutations. As a result

testing resources are heavily wasted on stateless

low-value shallow paths

which severely hinders the exploration of deep code vulnerabilities. To address these challenges

this paper presents PyBoros

an efficient fuzzing framework tailored for third-party Python libraries. The framework constructs an package-level API dependency graph and employs the clique percolation method (CPM) for subgraph partitioning. On this basis

it leverages large language models to generate semantically rich initial harnesses that capture implicit dependencies; adopts a stagnation-triggered non-blocking dynamic analysis

which takes the stagnation of coverage growth as a signal to capture constraint context information such as runtime code states and variable snapshots on demand

and utilizes large language models for intelligent constraint reasoning to generate breakthrough seeds; and introduces a resource scheduling strategy that integrates API n-gram coverage guidance with branch value scoring to prioritize the exploration of high-value paths during the fuzzing process. We evaluated PyBoros on 10 real-world third-party Python libraries with an average GitHub star count exceeding 2 000. The results demonstrate that PyBoros discovered 20 real vulnerabilities (10 of which are 0-day)

representing a 100% increase in vulnerability detection over Atheris. It also achievs an 8.57% improvement in edge coverage compared to Atheris. The API coverage of the initially generated harnesses reaches 1.8 × that of Fuzz4All. Across four large language models(two open-source and two closed-source)

the average acceptance rate of the generated harnesses is 72.6%. In additional robustness experiments

even under 10% static-analysis perturbation attacks

the acceptance rate of the harnesses drops by only 3.8%

maintaining strong robustness. Overall

PyBoros provides an effective approach for the security analysis of third-party Python libraries.

关键词

Keywords

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