Injecting a wide range of malformed data with an automated approach uncovers unexpected failures and memory issues. A public set of known exploits may miss unknown weaknesses, fixtures of normal traffic patterns do not explore edge cases, and manual tests rely on user assumptions without exploring large-scale variable input.
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How does fuzzing help uncover memory-related flaws?