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fuzz/README.md

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# Building
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## Build Requirements
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Note that `LibFuzzer` is part of `clang`, LibFuzzer is an in-process, coverage-guided, evolutionary fuzzing engine.
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`LibFuzzer` is linked with the library under test, and feeds fuzzed inputs to the library via a specific fuzzing entrypoint (aka “target function”); the fuzzer then tracks which areas of the code are reached, and generates mutations on the corpus of input data in order to maximize the code coverage.
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```
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## Build and Install
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To build fuzzer program, just add `-DBUILD_FUZZ=on` option is enough, then you would see fuzz program in `/usr/share/rats-tls/fuzz`.
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```shell
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# FUZZ
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## rats_tls_init API
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To fuzz `rats_tls_init()`, we use random input `* data` to fill the `conf`, and set value to part of the `conf` in order to run `rats_tls_init()` more frequently.
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```bash
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cd /usr/share/rats-tls/fuzz/
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mkdir corpus && cd corpus # create corpus dir
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```
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## rats_tls_negotiate API
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Start the `/usr/share/rats_tls/fuzz/fuzz_server` first, then use `tls_negotiate` to connect to server and fuzz the `rats_tls_negotiate()` API.
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```bash
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cd /usr/share/rats_tls/fuzz/
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mkdir corpus && cd corpus # create corpus dir
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```
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## rats_tls_transmit / rats_tls_recv / rats_tls_cleanup
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We synthesis the 3 sequential API in one program, start the `/usr/share/rats_tls/fuzz/fuzz_server` first, then use `tls_transmit` to connect to server and fuzz the `rats_tls_transmit()` and `rats_tls_recv()`,`rats_tls_cleanup` APIs by sending ramdom string and receiving the same response.
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```shell
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cd /usr/share/rats_tls/fuzz/
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mkdir corpus && cd corpus # create corpus dir and fill in random string

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