Modern software systems are increasingly complex, and static analysis tools are commonly used to identify potentially vulnerable code by issuing warnings. However, these warnings often require manual inspection to confirm whether the reported issues are real, making the process time-consuming and error-prone. Directed fuzzing has emerged as a powerful automated technique to validate the warnings. However, applying it to the entire project in response to each warning is computationally infeasible, often requiring days of execution to achieve only incremental improvements in code coverage. We present SNIPTEST, an execution-based warning triage framework that generates and fuzzes compiled code slices centered around static-analysis warnings. Rather than proving exploitability in the full program, SNIPTEST provides evidence about how a warning behaves under progressively expanded sliced execution contexts. It employs a layer-by-layer slicing strategy, incrementally expanding context around the target location to validate potential vulnerabilities with increasing precision. We evaluate SNIPTEST on a benchmark of 97 true vulnerabilities and 97 false alarms across three real-world projects. SNIPTEST produces Possible True Positive evidence for 53 of 97 confirmed vulnerabilities (54.6
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