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| 오염 분석 (Taint Analysis)× | 기호 실행× | |
|---|---|---|
| 분야 | 암호학 | 암호학 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2005 | 1976 |
| 창시자≠ | James Newsome | James C. King |
| 유형≠ | data flow tracking technique | formal verification technique |
| 원전≠ | 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 ↗ | King, J. C. (1976). Symbolic execution and program testing. Communications of the ACM, 19(7), 385-394. DOI ↗ |
| 별칭 | taint analysis, information flow, data tainting | symbolic execution, symbolic analysis, concolic execution |
| 관련 | 3 | 3 |
| 요약≠ | 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. | 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. |
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