This whitepaper explores advanced fuzz testing techniques for software-defined vehicles, where increasing connectivity and software complexity create new security challenges. Traditional fuzz testing methods often fail to detect issues like memory leaks or transient crashes due to limited system visibility. The paper introduces an agent-based instrumentation framework that enhances testing by collecting detailed runtime data directly from the system under test. This approach enables more accurate detection of vulnerabilities and root causes by monitoring system behavior beyond communication protocols. By combining fuzzing with deeper instrumentation, organizations can improve vulnerability detection, strengthen automotive cybersecurity, and ensure the reliability of increasingly software-c

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