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| Taint Analysis（汚染解析）× | 静的アプリケーションセキュリティテスト× | |
|---|---|---|
| 分野 | 暗号学 | 暗号学 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 2005 | 2000s |
| 提唱者≠ | James Newsome | Various researchers |
| 種類≠ | data flow tracking technique | source code vulnerability detection |
| 原典≠ | Newsome, J., & Song, D. X. (2005). Dynamic taint analysis for automatic detection, analysis, and signature generation of exploits on commodity software. In Network and Distributed System Security Symposium (NDSS 2005). link ↗ | Chess, B., & West, J. (2007). Secure Programming with Static Analysis. Addison-Wesley Professional. ISBN: 978-0321424778 |
| 別名 | taint analysis, information flow, data tainting | SAST, white-box testing, source code analysis |
| 関連 | 3 | 3 |
| 概要≠ | Taint analysis is a data flow analysis technique that tracks how untrusted (tainted) input flows through a program to identify vulnerabilities where tainted data reaches dangerous operations (sinks). Formalized by Newsome and Song in 2005, taint analysis marks input data as tainted and propagates taint labels through the program, alerting when tainted data reaches sensitive operations like SQL queries or system calls. Taint analysis is fundamental to detecting injection vulnerabilities and is widely used in dynamic analysis tools and security monitoring systems. | Static Application Security Testing (SAST) is a security analysis technique that examines source code or compiled binaries without executing the program to identify vulnerabilities, code quality issues, and security flaws. Developed in the 2000s, SAST analyzes code structure, data flow, and control flow to detect potential bugs such as SQL injection, buffer overflows, and insecure cryptographic usage. SAST is widely integrated into development workflows as a shift-left security practice, enabling early detection of vulnerabilities before code reaches production. |
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