Method for improving software fuzzy testing efficiency in combination with symbol execution

Abstract

The invention discloses a method for improving software fuzzy testing efficiency in combination with symbol execution. The method comprises the steps of S1, preprocessing a target program to obtain information of the target program; S2, inputting a fuzzy tester into the target program to generate a test case, and detecting whether the program is abnormal or not; S3, constructing scheduling parameters based on a path coverage rate, calculating the scheduling parameters in a fuzzy testing process, judging a state of the fuzzy tester, if the fuzzy tester is in a low-speed state, going to the stepS4, if the tester is in a high-speed state, returning to the step S2, and if the tester is in a halt state, exiting from the testing; and S4, identifying branches which are not traversed in the fuzzytesting process, constructing a non-traversed program path, generating an effective test case by using a symbol execution method to stimulate the fuzzy tester to be in the high-speed state, and if the effective test case cannot be generated and the tester is in the halt state, exiting from the testing. The method has the advantages of simple implementation principle, good vulnerability mining performance, high efficiency, low false alarm rate, flexible and reliable use, and the like.

Description

technical field [0001] The invention relates to the technical field of vulnerability mining of non-source code executable binary programs, in particular to a method for improving the efficiency of software fuzz testing in combination with symbolic execution. Background technique [0002] With the increasing attention to software security issues, vulnerability mining technology has gradually become the current focus of research. Vulnerability mining technology refers to the exploration of unknown vulnerabilities, and the comprehensive application of various technologies and tools to find potential vulnerabilities in software as much as possible. The traditional vulnerability mining technology including static analysis technology and dynamic analysis technology is not only time-consuming and labor-intensive, but also difficult to scan every software for all-round vulnerabilities, and it is very easy to miss vulnerabilities. Therefore, even the rigorously tested software , aft...

AI Technical Summary

Problems solved by technology

White-box testing has the characteristics of strong coverage, but this method requires a lot of manual analysis, and the complexity is very high. When the source code of the software to be tested has hundreds of thousands of lines or more, the operability is relatively poor

[0006] 2. Black box testing: It is a method of directly writing test cases for testing without knowing the internal details of the software to be tested. It is easy to operate and does not need to obtain the source code of the software to be tested. The usability is good, but most of the test cases come from testers Subjective guess, poor coverage

Although gray box testing has good usability, it has a lot of complexity, especially in the binary audit work, which will spend a lot of time

[0008] The above three fuzz tests have their own defects, and they are not well compatible with binary programs without source code. The generated test cases have problems such as blindness and low code coverage.

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