= Fuzzing Tor

To run the fuzzing test cases in a deterministic fashion, use:
  make fuzz

== Guided Fuzzing with AFL

There is no HTTPS, hash, or signature for American Fuzzy Lop's source code, so
its integrity can't be verified. That said, you really shouldn't fuzz on a
machine you care about, anyway.

To Build:
  Get AFL from http://lcamtuf.coredump.cx/afl/ and unpack it
  cd afl
  make
  cd ../tor
  PATH=$PATH:../afl/ CC="../afl/afl-gcc" ./configure --enable-expensive-hardening
  AFL_HARDEN=1 make clean fuzz

To Find The ASAN Memory Limit: (64-bit only)

On 64-bit platforms, afl needs to know how much memory ASAN uses.
Or, you can configure tor without --enable-expensive-hardening, then use
  make fuzz
to run the generated test cases through an ASAN-enabled fuzz_dir.
Read afl/docs/notes_for_asan.txt for more details.

  Download recidivm from http://jwilk.net/software/recidivm
  Download the signature
  Check the signature
  tar xvzf recidivm*.tar.gz
  cd recidivm*
  make
  /path/to/recidivm -v src/test/fuzz_dir
  Use the final "ok" figure as the input to -m when calling afl-fuzz
  (Normally, recidivm would output a figure automatically, but in some cases,
  the fuzzing harness will hang when the memory limit is too small.)

To Run:
  mkdir -p src/test/fuzz/fuzz_dir_testcase src/test/fuzz/fuzz_dir_findings
  echo "dummy" > src/test/fuzz/fuzz_dir_testcase/minimal.case
  ../afl/afl-fuzz -i src/test/fuzz/fuzz_dir_testcase -o src/test/fuzz/fuzz_dir_findings -m <asan-memory-limit> -- src/test/fuzz_dir

AFL has a multi-core mode, check the documentation for details.
You might find the included fuzz-multi.sh script useful for this.

macOS (OS X) requires slightly more preparation, including:
* using afl-clang (or afl-clang-fast from the llvm directory)
* disabling external crash reporting (AFL will guide you through this step)

AFL may also benefit from using dictionary files for text-based inputs: these
can be placed in src/test/fuzz/fuzz_dir_dictionary/.

Multiple dictionaries can be used with AFL, you should choose a combination of
dictionaries that targets the code you are fuzzing.

== Writing Tor fuzzers

A tor fuzzing harness should:
* read input from standard input (many fuzzing frameworks also accept file
  names)
* parse that input
* produce results on standard output (this assists in diagnosing errors)

Most fuzzing frameworks will produce many invalid inputs - a tor fuzzing
harness should rejecting invalid inputs without crashing or behaving badly.

But the fuzzing harness should crash if tor fails an assertion, triggers a
bug, or accesses memory it shouldn't. This helps fuzzing frameworks detect
"interesting" cases.

== Triaging Issues

Crashes are usually interesting, particularly if using AFL_HARDEN=1 and --enable-expensive-hardening. Sometimes crashes are due to bugs in the harness code.

Hangs might be interesting, but they might also be spurious machine slowdowns.
Check if a hang is reproducible before reporting it. Sometimes, processing
valid inputs may take a second or so, particularly with the fuzzer and
sanitizers enabled.

To see what fuzz_dir is doing with a test case, call it like this:
  src/test/fuzz_dir --debug < /path/to/test.case

(Logging is disabled while fuzzing to increase fuzzing speed.)

== Reporting Issues

Please report any issues discovered using the process in Tor's security issue
policy:

https://trac.torproject.org/projects/tor/wiki/org/meetings/2016SummerDevMeeting/Notes/SecurityIssuePolicy