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@ -35,4 +35,4 @@ Mutation-based fuzzers use samples of valid input that are mutated randomly to p
Generation-based fuzzers actually generate input from scratch rather than mutating existing input. They usually require some level of intelligence to construct input that makes at least some sense to the program, although generating completely random data would also technically be generation. Generation fuzzers often split a protocol or file format into chunks, which they can build up in a valid order, and randomly fuzz some of those chunks independently. This can create inputs that preserve their overall structure, but contain inconsistent data within it. The granularity of these chunks and the intelligence with which they’re constructed define the level of intelligence of the fuzzer. While mutation-based fuzzing can have a similar effect as generation fuzzing (as, over time, mutations will be randomly applied without completely breaking the input’s structure), generating inputs ensures this will be so. Generation fuzzing can also get deeper into a protocol more easily, as it can construct valid sequences of inputs applying fuzzing to specific parts of that communication. It also allows the fuzzer to act as a true client/server, generating correct, dynamic responses where these can’t be blindly replayed. Generation-based fuzzers actually generate input from scratch rather than mutating existing input. They usually require some level of intelligence to construct input that makes at least some sense to the program, although generating completely random data would also technically be generation. Generation fuzzers often split a protocol or file format into chunks, which they can build up in a valid order, and randomly fuzz some of those chunks independently. This can create inputs that preserve their overall structure, but contain inconsistent data within it. The granularity of these chunks and the intelligence with which they’re constructed define the level of intelligence of the fuzzer. While mutation-based fuzzing can have a similar effect as generation fuzzing (as, over time, mutations will be randomly applied without completely breaking the input’s structure), generating inputs ensures this will be so. Generation fuzzing can also get deeper into a protocol more easily, as it can construct valid sequences of inputs applying fuzzing to specific parts of that communication. It also allows the fuzzer to act as a true client/server, generating correct, dynamic responses where these can’t be blindly replayed.
### Evolutionary ### Evolutionary
Evolutionary fuzzing’s an advanced technique, which we’ll briefly describe. It allows the fuzzer to use feedback from each test case to learn the format of the input over time. Evolutionary fuzzing’s an advanced technique. It allows the fuzzer to use feedback from each test case to learn the format of the input over time.