手法を比較

	ファジング	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	1990	1976	2005
提唱者	Barton Miller	James C. King	James Newsome
種類	random input-based testing technique	formal verification technique	data flow tracking technique
原典	Miller, B. P., Fredriksen, L., & So, B. (1990). An empirical study of the reliability of UNIX utilities. Communications of the ACM, 33(12), 32-44.	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	fuzz testing, fuzzer, mutation testing	symbolic execution, symbolic analysis, concolic execution	taint analysis, information flow, data tainting
関連	3	3	3
概要	Fuzzing is a software testing technique that inputs large numbers of random or semi-random test cases to a program to find bugs, crashes, and security vulnerabilities. Pioneered by Barton Miller in 1990, fuzzing has become a primary method for discovering zero-day vulnerabilities in complex software. Modern fuzzing tools like libFuzzer, AFL, and HoneyPot combine coverage-guided mutation with instrumentation to efficiently explore program paths and trigger vulnerabilities. Fuzzing has discovered thousands of critical vulnerabilities in major software including browsers, compilers, and cryptographic libraries.	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.