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| 퍼징× | 오염 분석 (Taint Analysis)× | |
|---|---|---|
| 분야 | 암호학 | 암호학 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1990 | 2005 |
| 창시자≠ | Barton Miller | James Newsome |
| 유형≠ | random input-based testing technique | data flow tracking technique |
| 원전≠ | 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. DOI ↗ | 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 ↗ |
| 별칭 | fuzz testing, fuzzer, mutation testing | taint analysis, information flow, data tainting |
| 관련 | 3 | 3 |
| 요약≠ | 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. | 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. |
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