ISC2 Certified Secure Software Lifecycle Professional (CSSLP) Practice Question

Generation-based (model-driven) fuzzing builds inputs from scratch using a specification or grammar of the target protocol or file format. Because the fuzzer understands what valid and near-valid messages look like, it can deliberately craft edge-case values, omit required fields, repeat optional ones, or adjust length fields to probe deep, seldom-used logic paths in the parser. This produces high code-coverage and is especially effective when a complete format definition is at hand, as in this scenario.

Mutation-based fuzzing relies on altering existing samples; without a large, diverse corpus it is less systematic and may miss boundary conditions. Feeding random ASCII or replaying captured traffic are forms of blind or stress testing, not focused fuzzing, and provide less targeted coverage of parser logic.