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# american fuzzy lop
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[](https://travis-ci.org/google/AFL)
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Originally developed by Michal Zalewski <lcamtuf@google.com>.
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See [QuickStartGuide.txt](docs/QuickStartGuide.txt) if you don't have time to read
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this file.
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## 1) Challenges of guided fuzzing
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Fuzzing is one of the most powerful and proven strategies for identifying
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security issues in real-world software; it is responsible for the vast
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majority of remote code execution and privilege escalation bugs found to date
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in security-critical software.
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Unfortunately, fuzzing is also relatively shallow; blind, random mutations
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make it very unlikely to reach certain code paths in the tested code, leaving
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some vulnerabilities firmly outside the reach of this technique.
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There have been numerous attempts to solve this problem. One of the early
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approaches - pioneered by Tavis Ormandy - is corpus distillation. The method
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relies on coverage signals to select a subset of interesting seeds from a
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massive, high-quality corpus of candidate files, and then fuzz them by
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traditional means. The approach works exceptionally well, but requires such
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a corpus to be readily available. In addition, block coverage measurements
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provide only a very simplistic understanding of program state, and are less
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useful for guiding the fuzzing effort in the long haul.
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Other, more sophisticated research has focused on techniques such as program
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flow analysis ("concolic execution"), symbolic execution, or static analysis.
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All these methods are extremely promising in experimental settings, but tend
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to suffer from reliability and performance problems in practical uses - and
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currently do not offer a viable alternative to "dumb" fuzzing techniques.
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## 2) The afl-fuzz approach
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American Fuzzy Lop is a brute-force fuzzer coupled with an exceedingly simple
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but rock-solid instrumentation-guided genetic algorithm. It uses a modified
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form of edge coverage to effortlessly pick up subtle, local-scale changes to
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program control flow.
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Simplifying a bit, the overall algorithm can be summed up as:
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1) Load user-supplied initial test cases into the queue,
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2) Take next input file from the queue,
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3) Attempt to trim the test case to the smallest size that doesn't alter
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the measured behavior of the program,
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4) Repeatedly mutate the file using a balanced and well-researched variety
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of traditional fuzzing strategies,
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5) If any of the generated mutations resulted in a new state transition
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recorded by the instrumentation, add mutated output as a new entry in the
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queue.
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6) Go to 2.
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The discovered test cases are also periodically culled to eliminate ones that
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have been obsoleted by newer, higher-coverage finds; and undergo several other
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instrumentation-driven effort minimization steps.
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As a side result of the fuzzing process, the tool creates a small,
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self-contained corpus of interesting test cases. These are extremely useful
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for seeding other, labor- or resource-intensive testing regimes - for example,
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for stress-testing browsers, office applications, graphics suites, or
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closed-source tools.
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The fuzzer is thoroughly tested to deliver out-of-the-box performance far
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superior to blind fuzzing or coverage-only tools.
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## 3) Instrumenting programs for use with AFL
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When source code is available, instrumentation can be injected by a companion
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tool that works as a drop-in replacement for gcc or clang in any standard build
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process for third-party code.
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The instrumentation has a fairly modest performance impact; in conjunction with
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other optimizations implemented by afl-fuzz, most programs can be fuzzed as fast
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or even faster than possible with traditional tools.
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The correct way to recompile the target program may vary depending on the
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specifics of the build process, but a nearly-universal approach would be:
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```shell
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$ CC=/path/to/afl/afl-gcc ./configure
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$ make clean all
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```
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For C++ programs, you'd would also want to set `CXX=/path/to/afl/afl-g++`.
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The clang wrappers (afl-clang and afl-clang++) can be used in the same way;
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clang users may also opt to leverage a higher-performance instrumentation mode,
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as described in llvm_mode/README.llvm.
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When testing libraries, you need to find or write a simple program that reads
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data from stdin or from a file and passes it to the tested library. In such a
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case, it is essential to link this executable against a static version of the
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instrumented library, or to make sure that the correct .so file is loaded at
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runtime (usually by setting `LD_LIBRARY_PATH`). The simplest option is a static
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build, usually possible via:
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```shell
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$ CC=/path/to/afl/afl-gcc ./configure --disable-shared
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```
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Setting `AFL_HARDEN=1` when calling 'make' will cause the CC wrapper to
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automatically enable code hardening options that make it easier to detect
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simple memory bugs. Libdislocator, a helper library included with AFL (see
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libdislocator/README.dislocator) can help uncover heap corruption issues, too.
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PS. ASAN users are advised to review [notes_for_asan.txt](docs/notes_for_asan.txt) file for important
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caveats.
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## 4) Instrumenting binary-only apps
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When source code is *NOT* available, the fuzzer offers experimental support for
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fast, on-the-fly instrumentation of black-box binaries. This is accomplished
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with a version of QEMU running in the lesser-known "user space emulation" mode.
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QEMU is a project separate from AFL, but you can conveniently build the
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feature by doing:
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```shell
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$ cd qemu_mode
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$ ./build_qemu_support.sh
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```
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For additional instructions and caveats, see qemu_mode/README.qemu.
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The mode is approximately 2-5x slower than compile-time instrumentation, is
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less conducive to parallelization, and may have some other quirks.
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## 5) Choosing initial test cases
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To operate correctly, the fuzzer requires one or more starting file that
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contains a good example of the input data normally expected by the targeted
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application. There are two basic rules:
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- Keep the files small. Under 1 kB is ideal, although not strictly necessary.
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For a discussion of why size matters, see [perf_tips.txt](docs/perf_tips.txt).
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- Use multiple test cases only if they are functionally different from
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each other. There is no point in using fifty different vacation photos
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to fuzz an image library.
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You can find many good examples of starting files in the testcases/ subdirectory
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that comes with this tool.
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PS. If a large corpus of data is available for screening, you may want to use
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the afl-cmin utility to identify a subset of functionally distinct files that
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exercise different code paths in the target binary.
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## 6) Fuzzing binaries
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The fuzzing process itself is carried out by the afl-fuzz utility. This program
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requires a read-only directory with initial test cases, a separate place to
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store its findings, plus a path to the binary to test.
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For target binaries that accept input directly from stdin, the usual syntax is:
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```shell
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$ ./afl-fuzz -i testcase_dir -o findings_dir /path/to/program [...params...]
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```
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For programs that take input from a file, use '@@' to mark the location in
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the target's command line where the input file name should be placed. The
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fuzzer will substitute this for you:
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```shell
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$ ./afl-fuzz -i testcase_dir -o findings_dir /path/to/program @@
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```
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You can also use the -f option to have the mutated data written to a specific
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file. This is useful if the program expects a particular file extension or so.
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Non-instrumented binaries can be fuzzed in the QEMU mode (add -Q in the command
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line) or in a traditional, blind-fuzzer mode (specify -n).
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You can use -t and -m to override the default timeout and memory limit for the
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executed process; rare examples of targets that may need these settings touched
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include compilers and video decoders.
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Tips for optimizing fuzzing performance are discussed in [perf_tips.txt](docs/perf_tips.txt).
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Note that afl-fuzz starts by performing an array of deterministic fuzzing
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steps, which can take several days, but tend to produce neat test cases. If you
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want quick & dirty results right away - akin to zzuf and other traditional
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fuzzers - add the -d option to the command line.
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## 7) Interpreting output
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See the [status_screen.txt](docs/status_screen.txt) file for information on
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how to interpret the displayed stats and monitor the health of the process.
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Be sure to consult this file especially if any UI elements are highlighted in
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red.
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The fuzzing process will continue until you press Ctrl-C. At minimum, you want
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to allow the fuzzer to complete one queue cycle, which may take anywhere from a
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couple of hours to a week or so.
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There are three subdirectories created within the output directory and updated
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in real time:
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- queue/ - test cases for every distinctive execution path, plus all the
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starting files given by the user. This is the synthesized corpus
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mentioned in section 2.
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Before using this corpus for any other purposes, you can shrink
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it to a smaller size using the afl-cmin tool. The tool will find
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a smaller subset of files offering equivalent edge coverage.
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- crashes/ - unique test cases that cause the tested program to receive a
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fatal signal (e.g., SIGSEGV, SIGILL, SIGABRT). The entries are
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grouped by the received signal.
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- hangs/ - unique test cases that cause the tested program to time out. The
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default time limit before something is classified as a hang is
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the larger of 1 second and the value of the -t parameter.
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The value can be fine-tuned by setting AFL_HANG_TMOUT, but this
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is rarely necessary.
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Crashes and hangs are considered "unique" if the associated execution paths
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involve any state transitions not seen in previously-recorded faults. If a
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single bug can be reached in multiple ways, there will be some count inflation
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early in the process, but this should quickly taper off.
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The file names for crashes and hangs are correlated with parent, non-faulting
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queue entries. This should help with debugging.
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When you can't reproduce a crash found by afl-fuzz, the most likely cause is
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that you are not setting the same memory limit as used by the tool. Try:
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```shell
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$ LIMIT_MB=50
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$ ( ulimit -Sv $[LIMIT_MB << 10]; /path/to/tested_binary ... )
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```
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Change LIMIT_MB to match the -m parameter passed to afl-fuzz. On OpenBSD,
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also change -Sv to -Sd.
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Any existing output directory can be also used to resume aborted jobs; try:
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```shell
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$ ./afl-fuzz -i- -o existing_output_dir [...etc...]
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```
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If you have gnuplot installed, you can also generate some pretty graphs for any
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active fuzzing task using afl-plot. For an example of how this looks like,
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see [http://lcamtuf.coredump.cx/afl/plot/](http://lcamtuf.coredump.cx/afl/plot/).
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## 8) Parallelized fuzzing
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Every instance of afl-fuzz takes up roughly one core. This means that on
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multi-core systems, parallelization is necessary to fully utilize the hardware.
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For tips on how to fuzz a common target on multiple cores or multiple networked
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machines, please refer to [parallel_fuzzing.txt](docs/parallel_fuzzing.txt).
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The parallel fuzzing mode also offers a simple way for interfacing AFL to other
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fuzzers, to symbolic or concolic execution engines, and so forth; again, see the
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last section of [parallel_fuzzing.txt](docs/parallel_fuzzing.txt) for tips.
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## 9) Fuzzer dictionaries
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By default, afl-fuzz mutation engine is optimized for compact data formats -
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say, images, multimedia, compressed data, regular expression syntax, or shell
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scripts. It is somewhat less suited for languages with particularly verbose and
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redundant verbiage - notably including HTML, SQL, or JavaScript.
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To avoid the hassle of building syntax-aware tools, afl-fuzz provides a way to
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seed the fuzzing process with an optional dictionary of language keywords,
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magic headers, or other special tokens associated with the targeted data type
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-- and use that to reconstruct the underlying grammar on the go:
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[http://lcamtuf.blogspot.com/2015/01/afl-fuzz-making-up-grammar-with.html](http://lcamtuf.blogspot.com/2015/01/afl-fuzz-making-up-grammar-with.html)
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To use this feature, you first need to create a dictionary in one of the two
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formats discussed in dictionaries/README.dictionaries; and then point the fuzzer
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to it via the -x option in the command line.
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(Several common dictionaries are already provided in that subdirectory, too.)
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There is no way to provide more structured descriptions of the underlying
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syntax, but the fuzzer will likely figure out some of this based on the
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instrumentation feedback alone. This actually works in practice, say:
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[http://lcamtuf.blogspot.com/2015/04/finding-bugs-in-sqlite-easy-way.html](http://lcamtuf.blogspot.com/2015/04/finding-bugs-in-sqlite-easy-way.html)
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PS. Even when no explicit dictionary is given, afl-fuzz will try to extract
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existing syntax tokens in the input corpus by watching the instrumentation
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very closely during deterministic byte flips. This works for some types of
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parsers and grammars, but isn't nearly as good as the -x mode.
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If a dictionary is really hard to come by, another option is to let AFL run
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for a while, and then use the token capture library that comes as a companion
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utility with AFL. For that, see libtokencap/README.tokencap.
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## 10) Crash triage
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The coverage-based grouping of crashes usually produces a small data set that
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can be quickly triaged manually or with a very simple GDB or Valgrind script.
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Every crash is also traceable to its parent non-crashing test case in the
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queue, making it easier to diagnose faults.
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Having said that, it's important to acknowledge that some fuzzing crashes can be
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difficult to quickly evaluate for exploitability without a lot of debugging and
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code analysis work. To assist with this task, afl-fuzz supports a very unique
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"crash exploration" mode enabled with the -C flag.
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In this mode, the fuzzer takes one or more crashing test cases as the input,
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and uses its feedback-driven fuzzing strategies to very quickly enumerate all
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code paths that can be reached in the program while keeping it in the
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crashing state.
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Mutations that do not result in a crash are rejected; so are any changes that
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do not affect the execution path.
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The output is a small corpus of files that can be very rapidly examined to see
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what degree of control the attacker has over the faulting address, or whether
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it is possible to get past an initial out-of-bounds read - and see what lies
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beneath.
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Oh, one more thing: for test case minimization, give afl-tmin a try. The tool
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can be operated in a very simple way:
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```shell
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$ ./afl-tmin -i test_case -o minimized_result -- /path/to/program [...]
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```
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The tool works with crashing and non-crashing test cases alike. In the crash
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mode, it will happily accept instrumented and non-instrumented binaries. In the
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non-crashing mode, the minimizer relies on standard AFL instrumentation to make
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the file simpler without altering the execution path.
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The minimizer accepts the -m, -t, -f and @@ syntax in a manner compatible with
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afl-fuzz.
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Another recent addition to AFL is the afl-analyze tool. It takes an input
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file, attempts to sequentially flip bytes, and observes the behavior of the
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tested program. It then color-codes the input based on which sections appear to
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be critical, and which are not; while not bulletproof, it can often offer quick
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insights into complex file formats. More info about its operation can be found
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near the end of [technical_details.txt](docs/technical_details.txt).
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## 11) Going beyond crashes
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Fuzzing is a wonderful and underutilized technique for discovering non-crashing
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design and implementation errors, too. Quite a few interesting bugs have been
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found by modifying the target programs to call abort() when, say:
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- Two bignum libraries produce different outputs when given the same
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fuzzer-generated input,
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- An image library produces different outputs when asked to decode the same
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input image several times in a row,
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- A serialization / deserialization library fails to produce stable outputs
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when iteratively serializing and deserializing fuzzer-supplied data,
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- A compression library produces an output inconsistent with the input file
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when asked to compress and then decompress a particular blob.
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Implementing these or similar sanity checks usually takes very little time;
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if you are the maintainer of a particular package, you can make this code
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conditional with `#ifdef FUZZING_BUILD_MODE_UNSAFE_FOR_PRODUCTION` (a flag also
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shared with libfuzzer) or `#ifdef __AFL_COMPILER` (this one is just for AFL).
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## 12) Common-sense risks
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Please keep in mind that, similarly to many other computationally-intensive
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tasks, fuzzing may put strain on your hardware and on the OS. In particular:
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- Your CPU will run hot and will need adequate cooling. In most cases, if
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cooling is insufficient or stops working properly, CPU speeds will be
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automatically throttled. That said, especially when fuzzing on less
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suitable hardware (laptops, smartphones, etc), it's not entirely impossible
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for something to blow up.
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- Targeted programs may end up erratically grabbing gigabytes of memory or
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filling up disk space with junk files. AFL tries to enforce basic memory
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limits, but can't prevent each and every possible mishap. The bottom line
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is that you shouldn't be fuzzing on systems where the prospect of data loss
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is not an acceptable risk.
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- Fuzzing involves billions of reads and writes to the filesystem. On modern
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systems, this will be usually heavily cached, resulting in fairly modest
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"physical" I/O - but there are many factors that may alter this equation.
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It is your responsibility to monitor for potential trouble; with very heavy
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I/O, the lifespan of many HDDs and SSDs may be reduced.
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A good way to monitor disk I/O on Linux is the 'iostat' command:
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```shell
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$ iostat -d 3 -x -k [...optional disk ID...]
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```
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## 13) Known limitations & areas for improvement
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Here are some of the most important caveats for AFL:
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- AFL detects faults by checking for the first spawned process dying due to
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a signal (SIGSEGV, SIGABRT, etc). Programs that install custom handlers for
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these signals may need to have the relevant code commented out. In the same
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vein, faults in child processed spawned by the fuzzed target may evade
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detection unless you manually add some code to catch that.
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- As with any other brute-force tool, the fuzzer offers limited coverage if
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encryption, checksums, cryptographic signatures, or compression are used to
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wholly wrap the actual data format to be tested.
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To work around this, you can comment out the relevant checks (see
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experimental/libpng_no_checksum/ for inspiration); if this is not possible,
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you can also write a postprocessor, as explained in
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experimental/post_library/.
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- There are some unfortunate trade-offs with ASAN and 64-bit binaries. This
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isn't due to any specific fault of afl-fuzz; see [notes_for_asan.txt](docs/notes_for_asan.txt)
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for tips.
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- There is no direct support for fuzzing network services, background
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daemons, or interactive apps that require UI interaction to work. You may
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need to make simple code changes to make them behave in a more traditional
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way. Preeny may offer a relatively simple option, too - see:
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https://github.com/zardus/preeny
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Some useful tips for modifying network-based services can be also found at:
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https://www.fastly.com/blog/how-to-fuzz-server-american-fuzzy-lop
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- AFL doesn't output human-readable coverage data. If you want to monitor
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coverage, use afl-cov from Michael Rash: https://github.com/mrash/afl-cov
|
||||
|
||||
- Occasionally, sentient machines rise against their creators. If this
|
||||
happens to you, please consult http://lcamtuf.coredump.cx/prep/.
|
||||
|
||||
Beyond this, see INSTALL for platform-specific tips.
|
||||
|
||||
## 14) Special thanks
|
||||
|
||||
Many of the improvements to afl-fuzz wouldn't be possible without feedback,
|
||||
bug reports, or patches from:
|
||||
|
||||
```
|
||||
Jann Horn Hanno Boeck
|
||||
Felix Groebert Jakub Wilk
|
||||
Richard W. M. Jones Alexander Cherepanov
|
||||
Tom Ritter Hovik Manucharyan
|
||||
Sebastian Roschke Eberhard Mattes
|
||||
Padraig Brady Ben Laurie
|
||||
@dronesec Luca Barbato
|
||||
Tobias Ospelt Thomas Jarosch
|
||||
Martin Carpenter Mudge Zatko
|
||||
Joe Zbiciak Ryan Govostes
|
||||
Michael Rash William Robinet
|
||||
Jonathan Gray Filipe Cabecinhas
|
||||
Nico Weber Jodie Cunningham
|
||||
Andrew Griffiths Parker Thompson
|
||||
Jonathan Neuschfer Tyler Nighswander
|
||||
Ben Nagy Samir Aguiar
|
||||
Aidan Thornton Aleksandar Nikolich
|
||||
Sam Hakim Laszlo Szekeres
|
||||
David A. Wheeler Turo Lamminen
|
||||
Andreas Stieger Richard Godbee
|
||||
Louis Dassy teor2345
|
||||
Alex Moneger Dmitry Vyukov
|
||||
Keegan McAllister Kostya Serebryany
|
||||
Richo Healey Martijn Bogaard
|
||||
rc0r Jonathan Foote
|
||||
Christian Holler Dominique Pelle
|
||||
Jacek Wielemborek Leo Barnes
|
||||
Jeremy Barnes Jeff Trull
|
||||
Guillaume Endignoux ilovezfs
|
||||
Daniel Godas-Lopez Franjo Ivancic
|
||||
Austin Seipp Daniel Komaromy
|
||||
Daniel Binderman Jonathan Metzman
|
||||
Vegard Nossum Jan Kneschke
|
||||
Kurt Roeckx Marcel Bohme
|
||||
Van-Thuan Pham Abhik Roychoudhury
|
||||
Joshua J. Drake Toby Hutton
|
||||
Rene Freingruber Sergey Davidoff
|
||||
Sami Liedes Craig Young
|
||||
Andrzej Jackowski Daniel Hodson
|
||||
```
|
||||
|
||||
Thank you!
|
||||
|
||||
## 15) Contact
|
||||
|
||||
Questions? Concerns? Bug reports? Please use GitHub.
|
||||
|
||||
There is also a mailing list for the project; to join, send a mail to
|
||||
<afl-users+subscribe@googlegroups.com>. Or, if you prefer to browse
|
||||
archives first, try: [https://groups.google.com/group/afl-users](https://groups.google.com/group/afl-users).
|
||||
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Reference in new issue