The article addresses current issues of software protection against malicious code and the detection of its manifestations during development and operation. It notes that modern methods of software analysis, particularly static and dynamic analysis, have both advantages and significant limitations, including a high number of false positives, low efficiency against polymorphic threats, and high computational resource requirements. As an optimal solution, the use of hybrid analysis is proposed, which combines the strengths of different approaches to improve the accuracy of vulnerability detection and reduce the number of erroneous results. The work presents a mathematical model for vulnerability detection based on symbolic execution and combined code analysis, as well as developed algorithms for constructing a reduced program path graph, calculating distance metrics to potentially dangerous code sections, and implementing directed dynamic symbolic execution. The methodology of vulnerability warning classification involves dividing them into three categories: confirmed, unconfirmed, and requiring additional inspection. This approach significantly reduces the complexity of analysis,
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