手法を比較

	動的アプリケーションセキュリティテスト	シンボリック実行	Taint Analysis（汚染解析）
分野	暗号学	暗号学	暗号学
系統	Machine learning	Machine learning	Machine learning
提唱年	2000s	1976	2005
提唱者	Various researchers	James C. King	James Newsome
種類	runtime vulnerability detection	formal verification technique	data flow tracking technique
原典	Kals, S., Kirda, E., Kruegel, C., & Jovanovic, N. (2006). Secubat: A web vulnerability scanner. In Proceedings of the 15th International Conference on World Wide Web (WWW 2006), pp. 247-256.	King, J. C. (1976). Symbolic execution and program testing. Communications of the ACM, 19(7), 385-394.	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).
別名	DAST, black-box testing, runtime security testing	symbolic execution, symbolic analysis, concolic execution	taint analysis, information flow, data tainting
関連	3	3	3
概要	Dynamic Application Security Testing (DAST) is a security analysis technique that tests a running application by sending various inputs and observing responses to identify vulnerabilities and security flaws. Developed in the 2000s as a complement to static analysis, DAST exercises the application at runtime, finding vulnerabilities that only manifest during execution such as authentication bypass, insecure redirects, and logic flaws. DAST is commonly used for web application testing and is considered a black-box testing approach since the tester requires no knowledge of internal code structure.	Symbolic execution is a program analysis technique that executes programs using symbolic (non-concrete) values instead of actual inputs, tracking how symbolic values flow through the program. Introduced by James C. King in 1976, symbolic execution builds mathematical constraints on program variables and can determine which inputs cause specific program behaviors, enabling automatic test generation and vulnerability detection. Modern symbolic execution tools like KLEE, S2E, and Z3 have become powerful instruments for finding subtle bugs and security vulnerabilities.	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.