A Cryptographic API Misuse Detection Method Based on Intermediate Representation Simulation

HE Yafei; ZHAN Lige; NIE Yu; FU Jianming; PENG Guojun

doi:10.12263/DZXB.20250813

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A Cryptographic API Misuse Detection Method Based on Intermediate Representation Simulation

PAPERS | 更新时间：2026-06-16

- A Cryptographic API Misuse Detection Method Based on Intermediate Representation Simulation
- ACTA ELECTRONICA SINICA Vol. 54, Issue 3, Pages: 1132-1146(2026)
- 作者机构：
  
  1.武汉大学国家网络安全学院，湖北武汉 430072
  2.空天信息安全与可信计算教育部重点实验室，湖北武汉 430072
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62272351;62572354)
- DOI：10.12263/DZXB.20250813
  CLC： TP311.5;TP309.7
- Received：16 September 2025，
  
  Accepted：09 March 2026，
  
  Published：25 March 2026
- 稿件说明：
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何亚非, 占力戈, 聂宇, 等. 一种基于IR模拟执行的密码学API误用检测方法[J]. 电子学报, 2026, 54(03): 1132-1146.

HE Yafei, ZHAN Lige, NIE Yu, et al. A Cryptographic API Misuse Detection Method Based on Intermediate Representation Simulation[J]. Acta Electronica Sinica, 2026, 54(03): 1132-1146.
何亚非, 占力戈, 聂宇, 等. 一种基于IR模拟执行的密码学API误用检测方法[J]. 电子学报, 2026, 54(03): 1132-1146. DOI：10.12263/DZXB.20250813

HE Yafei, ZHAN Lige, NIE Yu, et al. A Cryptographic API Misuse Detection Method Based on Intermediate Representation Simulation[J]. Acta Electronica Sinica, 2026, 54(03): 1132-1146. DOI：10.12263/DZXB.20250813

摘要

密码学算法是现代软件系统中保护数据机密性与完整性的核心机制。然而，密码学应用程序编程接口（Application Programming Interface，API）的误用，例如使用可预测密钥或不安全密码算法，严重影响着软件系统的安全性，可能导致软件破解、网络攻击等后果，表明了检测密码学API误用的必要性。现有密码学API误用检测研究主要通过分析API参数值来识别误用现象，根据分析方式可分为动态分析方法和静态分析方法。动态分析方法能获取运行时的参数值，但受代码覆盖率低的限制，易遗漏未触发的代码，从而导致漏报。相比之下，静态分析方法具备更高的代码覆盖率，但现有的静态检测方法在理论与实现上仍存在显著局限：它们大多依赖简单的常量传播或模式匹配，仅能识别直接赋值的简单参数形式，而难以解析经过复杂指令（如字符串拼接、编码转换等“值变化”操作）处理的目标参数，导致当密码学参数经过复杂变换时，现有静态分析方法会出现大量漏报的情况，形成分析盲区。针对上述问题，本文提出并实现了ParamScope。这是一种基于中间表示（Intermediate Representation，IR）解释模拟的Java密码学API误用静态检测方法。首先，ParamScope实现了一种“赋值驱动”的程序切片算法。该算法基于SootUp程序分析工具构建，通过引入严格的赋值模式约束，能够精准提取密码学参数的依赖路径。其次，ParamScope实现了轻量级IR解释器对切片后的路径进行模拟执行，并引入包含真实逻辑的Android核心类库，结合Java反射机制，在静态分析中实现了对复杂方法调用的语义还原。该方法具有静态分析的高覆盖率优势，同时有效解决了静态环境下复杂指令导致的参数值分析难题。实验表明，ParamScope在公开数据集上的参数值重建精确率达到97.31%，且误用检测准确率达到96.2%，优于现有的先进静态与动态工具；在真实Android应用程序中的检测实验进一步显示，ParamScope有效识别出大量被编码或隐藏的真实参数典型案例，并报告其中的误用现象，相较于现有的静态工具CogniCrypt额外检测出约27%的误用案例，且单个应用平均分析用时仅约4.85 min。综上，ParamScope兼具静态分析的高覆盖率与复杂参数解析的高精准度，为密码学API误用检测提供了一种高精度、高效率的新型解决方案。

Abstract

Cryptographic algorithms serve as the core mechanisms for protecting data confidentiality and integrity in modern software systems. However

the misuse of cryptographic application programming interfaces (APIs)

such as using predictable keys or insecure cryptographic algorithms

severely compromises software security

leading to software cracking and network attacks

which highlights the necessity of detecting such misuses. Existing studies on cryptographic API misuse detection primarily identify such misuses by analyzing API parameter values

and can be categorized into dynamic and static methods. While dynamic approaches can retrieve precise runtime parameter values

they suffer from low code coverage

leading to false negatives caused by untriggered code paths. In contrast

static analysis offers higher code coverage

but existing static methods face significant theoretical and practical limitations: they mostly rely on simple constant propagation or pattern matching

which only allow them to identify directly assigned parameters. Consequently

they struggle to resolve target parameters processed by complex “value-transformation” instructions (e.g.

string concatenation or encoding conversion)

leading to substantial blind spots and false negatives when analyzing cryptographic parameters that undergo complex transformations. To address these limitations

this paper proposes Pa

ramScope

a static detection method for Java cryptographic API misuse based on intermediate representation (IR) interpretation and simulation. First

ParamScope implements an “assignment-driven” program slicing algorithm built upon the SootUp

framework

which incorporates strict assignment pattern constraints to precisely extract the dependency paths of cryptographic parameters. Second

it utilizes a lightweight IR interpreter to simulate the execution of the sliced statements

and integrates core Android libraries containing actual implementations. By combining this with Java reflection mechanisms

it achieves the semantic restoration of complex method calls during static analysis. This approach leverages the high coverage of static analysis and effectively resolves the challenge of statically analyzing parameter values derived from complex instructions. Evaluations on public datasets demonstrate that ParamScope achieves parameter value reconstruction and misuse detection accuracies of 97.31% and 96.2% respectively

outperforming state-of-the-art static and dynamic tools. Furthermore

experiments on real-world Android applications reveal that ParamScope effectively identifies typical cases of encoded or hidden real parameters

and reports the misuses within them. It detects approximately 27% more misuses compared to the leading static tool

CogniCrypt

with an average analysis time of only about 4.85 minutes per application. In summary

ParamScope combines the high coverage of static analysis with the high precision of complex parameter resolution

thereby providing a novel

precise

and efficient solution for cryptographic API misuse detection.

关键词

Keywords

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