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91 lines
3.3 KiB
Markdown
91 lines
3.3 KiB
Markdown
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= Fuzzing Tor
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To run the fuzzing test cases in a deterministic fashion, use:
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make fuzz
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== Guided Fuzzing with AFL
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There is no HTTPS, hash, or signature for American Fuzzy Lop's source code, so
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its integrity can't be verified. That said, you really shouldn't fuzz on a
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machine you care about, anyway.
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To Build:
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Get AFL from http://lcamtuf.coredump.cx/afl/ and unpack it
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cd afl
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make
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cd ../tor
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PATH=$PATH:../afl/ CC="../afl/afl-gcc" ./configure --enable-expensive-hardening
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AFL_HARDEN=1 make clean fuzz
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To Find The ASAN Memory Limit: (64-bit only)
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On 64-bit platforms, afl needs to know how much memory ASAN uses.
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Or, you can configure tor without --enable-expensive-hardening, then use
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make fuzz
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to run the generated test cases through an ASAN-enabled fuzz_dir.
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Read afl/docs/notes_for_asan.txt for more details.
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Download recidivm from http://jwilk.net/software/recidivm
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Download the signature
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Check the signature
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tar xvzf recidivm*.tar.gz
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cd recidivm*
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make
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/path/to/recidivm -v src/test/fuzz_dir
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Use the final "ok" figure as the input to -m when calling afl-fuzz
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(Normally, recidivm would output a figure automatically, but in some cases,
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the fuzzing harness will hang when the memory limit is too small.)
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To Run:
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mkdir -p src/test/fuzz/fuzz_dir_testcase src/test/fuzz/fuzz_dir_findings
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echo "dummy" > src/test/fuzz/fuzz_dir_testcase/minimal.case
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../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
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AFL has a multi-core mode, check the documentation for details.
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macOS (OS X) requires slightly more preparation, including:
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* using afl-clang (or afl-clang-fast from the llvm directory)
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* disabling external crash reporting (AFL will guide you through this step)
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AFL may also benefit from using dictionary files for text-based inputs: these
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can be placed in src/test/fuzz/fuzz_dir_dictionary/.
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Multiple dictionaries can be used with AFL, you should choose a combination of
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dictionaries that targets the code you are fuzzing.
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== Writing Tor fuzzers
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A tor fuzzing harness should:
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* read input from standard input (many fuzzing frameworks also accept file
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names)
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* parse that input
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* produce results on standard output (this assists in diagnosing errors)
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Most fuzzing frameworks will produce many invalid inputs - a tor fuzzing
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harness should rejecting invalid inputs without crashing or behaving badly.
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But the fuzzing harness should crash if tor fails an assertion, triggers a
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bug, or accesses memory it shouldn't. This helps fuzzing frameworks detect
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"interesting" cases.
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== Triaging Issues
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Crashes are usually interesting, particularly if using AFL_HARDEN=1 and --enable-expensive-hardening. Sometimes crashes are due to bugs in the harness code.
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Hangs might be interesting, but they might also be spurious machine slowdowns.
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Check if a hang is reproducible before reporting it. Sometimes, processing
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valid inputs may take a second or so, particularly with the fuzzer and
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sanitizers enabled.
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To see what fuzz_dir is doing with a test case, call it like this:
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src/test/fuzz_dir --debug < /path/to/test.case
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(Logging is disabled while fuzzing to increase fuzzing speed.)
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== Reporting Issues
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Please report any issues discovered using the process in Tor's security issue
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policy:
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https://trac.torproject.org/projects/tor/wiki/org/meetings/2016SummerDevMeeting/Notes/SecurityIssuePolicy
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