Methoden vergleichen

	Taint-Analyse	Statische Anwendungssicherheitsprüfung	Symbolische Ausführung
Fachgebiet	Kryptographie	Kryptographie	Kryptographie
Familie	Machine learning	Machine learning	Machine learning
Entstehungsjahr	2005	2000s	1976
Urheber	James Newsome	Various researchers	James C. King
Typ	data flow tracking technique	source code vulnerability detection	formal verification technique
Wegweisende Quelle	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).	Chess, B., & West, J. (2007). Secure Programming with Static Analysis. Addison-Wesley Professional. ISBN: 978-0321424778	King, J. C. (1976). Symbolic execution and program testing. Communications of the ACM, 19(7), 385-394.
Aliasnamen	taint analysis, information flow, data tainting	SAST, white-box testing, source code analysis	symbolic execution, symbolic analysis, concolic execution
Verwandt	3	3	3
Zusammenfassung	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.	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.