Jar Analyzer - 一个 JAR 包 GUI 分析工具,方法调用关系搜索,方法调用链 DFS 算法分析,模拟 JVM 的污点分析验证 DFS 结果,字符串搜索,Java Web 组件入口分析,CFG 程序分析,JVM 栈帧分析,自定义表达式搜索,紧跟 AI 技术发展,支持 MCP 调用,支持 n8n 工作流,文档:https://docs.qq.com/doc/DV3pKbG9GS0pJS0tk
git clone https://github.com/jar-analyzer/jar-analyzer.gitThe Jar Analyzer is a powerful GUI tool designed for analyzing JAR files, providing developers with the ability to search for method call relationships and analyze them using DFS algorithms. This skill enables users to simulate JVM taint analysis, perform string searches, and analyze Java Web component entry points. With support for MCP calls and integration into n8n workflows, Jar Analyzer enhances workflow automation and improves the efficiency of JAR file management. One of the key benefits of using Jar Analyzer is the significant time savings it offers. Developers can quickly identify where a specific method is defined across multiple JAR files, allowing for faster debugging and vulnerability detection. For instance, it can analyze method calls to pinpoint potential security risks, such as the misuse of Runtime.exec, and visualize control flow graphs for deeper insights into method execution paths. By integrating JAR analysis into existing CI/CD workflows, teams can automate security checks, thus streamlining their development processes. This skill is particularly beneficial for developers, product managers, and AI practitioners who are involved in software security and quality assurance. By leveraging Jar Analyzer, these professionals can ensure that their applications are not only functional but also secure from potential vulnerabilities. The intermediate complexity of the skill makes it accessible to those with a foundational understanding of Java and JAR file structures, while its implementation time of just 30 minutes means that teams can quickly adopt it into their workflows. In an AI-first environment, the Jar Analyzer fits seamlessly into automated workflows, enabling teams to maintain high standards of code quality and security. As AI technology continues to evolve, skills like Jar Analyzer are essential for keeping pace with the demands of modern software development. With its practical applications and robust features, it stands out as a valuable tool for any organization looking to enhance their JAR file analysis capabilities.
["Prepare your JAR file: Place the target JAR in a known directory and note its full path (e.g., /projects/app/target/app.jar).","Configure analysis scope: Decide whether to analyze the entire JAR or focus on specific packages (e.g., com.company.payment.*). Use the --packages flag if needed.","Run the analysis: Execute Jar Analyzer with your parameters. For GUI mode: `java -jar jar-analyzer.jar --gui --file /path/to/your.jar`. For CLI mode: `jar-analyzer --file /path/to/your.jar --depth 5 --taint-analysis true`.","Review results: Examine the generated call graphs, CFG visualizations, and taint analysis reports. Use the string search feature to locate specific patterns (e.g., passwords, API keys).","Export findings: Save the analysis report in JSON/XML format for further processing or integration with other tools. Use MCP endpoints to feed results into n8n workflows if needed."]
Quickly identify where a specific method is defined across multiple JAR files.
Analyze method calls to detect potential vulnerabilities like the use of Runtime.exec.
Perform string searches to locate sensitive information leaks within JAR files.
Visualize control flow graphs for deeper insights into method execution paths.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/jar-analyzer/jar-analyzerCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Analyze the JAR file [JAR_FILE_PATH] using Jar Analyzer to: 1) Map all method call relationships with a depth-first search (DFS) algorithm, 2) Simulate JVM taint analysis to validate call chains, 3) Identify Java Web component entry points (Servlets, JSP, Spring MVC, etc.), 4) Generate a control flow graph (CFG) for critical methods, and 5) Search for strings matching [SEARCH_PATTERN]. Output findings in a structured report with call graphs, taint propagation paths, and entry point summaries. Use MCP integration if available.
Jar Analyzer v2.4.1 Analysis Report for 'payment-service-1.0.0.jar'
=== Method Call Graph (DFS Depth: 5) ===
CorePaymentService.processPayment()
├── PaymentValidator.validate() [taint source: user input]
│ └── DatabaseConnector.query() [taint sink: SQL]
├── TransactionLogger.log() [taint source: payment data]
│ └── LogManager.write() [taint sink: file system]
└── FraudDetection.check()
└── RiskEngine.evaluate() [taint source: transaction metadata]
=== JVM Taint Analysis Validation ===
Confirmed taint propagation from PaymentValidator.validate() to DatabaseConnector.query() via 3 intermediate calls. No sanitization found at any step. High risk of SQL injection.
=== Java Web Entry Points ===
1. Servlet: PaymentServlet.doPost() (url-pattern: /api/payments)
2. Spring Controller: PaymentController.process() (mapping: POST /payments)
3. JSP: payment-confirmation.jsp (forwarded from PaymentServlet)
=== Control Flow Graph (CFG) ===
Method: CorePaymentService.processPayment()
Nodes: 12 (including 2 conditional branches)
Edges: 15
Critical Path: validate() → log() → save() → notify()
=== String Search Results ===
Found 42 occurrences of 'password' in:
- config/security.properties (3)
- PaymentService.java (2)
- test/PaymentServiceTest.java (37)
=== Security Recommendations ===
1. Add input validation in PaymentValidator.validate()
2. Implement parameterized queries in DatabaseConnector
3. Mask sensitive data in TransactionLogger
4. Review Spring Security configuration for /api/payments endpoint
Analysis completed in 42.3 seconds using 1.2GB RAM.Cloud ETL platform for non-technical data integration
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