================== american fuzzy lop

Written and maintained by Michal Zalewski lcamtuf@google.com

For new versions and additional information, check out: http://lcamtuf.coredump.cx/afl/

To compare notes with other users or get notified about major new features, send a mail to afl-users+subscribe@googlegroups.com.

** See QuickStartGuide.txt if you don't have time to read this file. **

Challenges of guided fuzzing

Fuzzing is one of the most powerful and proven strategies for identifying security issues in real-world software; it is responsible for the vast majority of remote code execution and privilege escalation bugs found to date in security-critical software.

Unfortunately, fuzzing is also relatively shallow; blind, random mutations make it very unlikely to reach certain code paths in the tested code, leaving some vulnerabilities firmly outside the reach of this technique.

There have been numerous attempts to solve this problem. One of the early approaches - pioneered by Tavis Ormandy - is corpus distillation. The method relies on coverage signals to select a subset of interesting seeds from a massive, high-quality corpus of candidate files, and then fuzz them by traditional means. The approach works exceptionally well, but requires such a corpus to be readily available. In addition, block coverage measurements provide only a very simplistic understanding of program state, and are less useful for guiding the fuzzing effort in the long haul.

Other, more sophisticated research has focused on techniques such as program flow analysis ("concolic execution"), symbolic execution, or static analysis. All these methods are extremely promising in experimental settings, but tend to suffer from reliability and performance problems in practical uses - and currently do not offer a viable alternative to "dumb" fuzzing techniques.

The afl-fuzz approach

American Fuzzy Lop is a brute-force fuzzer coupled with an exceedingly simple but rock-solid instrumentation-guided genetic algorithm. It uses a modified form of edge coverage to effortlessly pick up subtle, local-scale changes to program control flow.

Simplifying a bit, the overall algorithm can be summed up as:

Load user-supplied initial test cases into the queue,
Take next input file from the queue,
Attempt to trim the test case to the smallest size that doesn't alter the measured behavior of the program,
Repeatedly mutate the file using a balanced and well-researched variety of traditional fuzzing strategies,
If any of the generated mutations resulted in a new state transition recorded by the instrumentation, add mutated output as a new entry in the queue.
Go to 2.

The discovered test cases are also periodically culled to eliminate ones that have been obsoleted by newer, higher-coverage finds; and undergo several other instrumentation-driven effort minimization steps.

As a side result of the fuzzing process, the tool creates a small, self-contained corpus of interesting test cases. These are extremely useful for seeding other, labor- or resource-intensive testing regimes - for example, for stress-testing browsers, office applications, graphics suites, or closed-source tools.

The fuzzer is thoroughly tested to deliver out-of-the-box performance far superior to blind fuzzing or coverage-only tools.

Instrumenting programs for use with AFL

When source code is available, instrumentation can be injected by a companion tool that works as a drop-in replacement for gcc or clang in any standard build process for third-party code.

The instrumentation has a fairly modest performance impact; in conjunction with other optimizations implemented by afl-fuzz, most programs can be fuzzed as fast or even faster than possible with traditional tools.

The correct way to recompile the target program may vary depending on the specifics of the build process, but a nearly-universal approach would be:

$ CC=/path/to/afl/afl-gcc ./configure $ make clean all

For C++ programs, you'd would also want to set CXX=/path/to/afl/afl-g++.

The clang wrappers (afl-clang and afl-clang++) can be used in the same way; clang users may also opt to leverage a higher-performance instrumentation mode, as described in llvm_mode/README.llvm.

When testing libraries, you need to find or write a simple program that reads data from stdin or from a file and passes it to the tested library. In such a case, it is essential to link this executable against a static version of the instrumented library, or to make sure that the correct .so file is loaded at runtime (usually by setting LD_LIBRARY_PATH). The simplest option is a static build, usually possible via:

$ CC=/path/to/afl/afl-gcc ./configure --disable-shared

Setting AFL_HARDEN=1 when calling 'make' will cause the CC wrapper to automatically enable code hardening options that make it easier to detect simple memory bugs. Libdislocator, a helper library included with AFL (see libdislocator/README.dislocator) can help uncover heap corruption issues, too.

PS. ASAN users are advised to review notes_for_asan.txt file for important caveats.

Instrumenting binary-only apps

When source code is NOT available, the fuzzer offers experimental support for fast, on-the-fly instrumentation of black-box binaries. This is accomplished with a version of QEMU running in the lesser-known "user space emulation" mode.

QEMU is a project separate from AFL, but you can conveniently build the feature by doing:

$ cd qemu_mode $ ./build_qemu_support.sh

For additional instructions and caveats, see qemu_mode/README.qemu.

The mode is approximately 2-5x slower than compile-time instrumentation, is less conductive to parallelization, and may have some other quirks.

Choosing initial test cases

To operate correctly, the fuzzer requires one or more starting file that contains a good example of the input data normally expected by the targeted application. There are two basic rules:

Keep the files small. Under 1 kB is ideal, although not strictly necessary. For a discussion of why size matters, see perf_tips.txt.
Use multiple test cases only if they are functionally different from each other. There is no point in using fifty different vacation photos to fuzz an image library.

You can find many good examples of starting files in the testcases/ subdirectory that comes with this tool.

PS. If a large corpus of data is available for screening, you may want to use the afl-cmin utility to identify a subset of functionally distinct files that exercise different code paths in the target binary.

Fuzzing binaries

The fuzzing process itself is carried out by the afl-fuzz utility. This program requires a read-only directory with initial test cases, a separate place to store its findings, plus a path to the binary to test.

For target binaries that accept input directly from stdin, the usual syntax is:

$ ./afl-fuzz -i testcase_dir -o findings_dir /path/to/program [...params...]

For programs that take input from a file, use '@@' to mark the location in the target's command line where the input file name should be placed. The fuzzer will substitute this for you:

$ ./afl-fuzz -i testcase_dir -o findings_dir /path/to/program @@

You can also use the -f option to have the mutated data written to a specific file. This is useful if the program expects a particular file extension or so.

Non-instrumented binaries can be fuzzed in the QEMU mode (add -Q in the command line) or in a traditional, blind-fuzzer mode (specify -n).

You can use -t and -m to override the default timeout and memory limit for the executed process; rare examples of targets that may need these settings touched include compilers and video decoders.

Tips for optimizing fuzzing performance are discussed in perf_tips.txt.

Note that afl-fuzz starts by performing an array of deterministic fuzzing steps, which can take several days, but tend to produce neat test cases. If you want quick & dirty results right away - akin to zzuf and other traditional fuzzers - add the -d option to the command line.

Interpreting output

See the status_screen.txt file for information on how to interpret the displayed stats and monitor the health of the process. Be sure to consult this file especially if any UI elements are highlighted in red.

The fuzzing process will continue until you press Ctrl-C. At minimum, you want to allow the fuzzer to complete one queue cycle, which may take anywhere from a couple of hours to a week or so.

There are three subdirectories created within the output directory and updated in real time:

queue/ - test cases for every distinctive execution path, plus all the starting files given by the user. This is the synthesized corpus mentioned in section 2.

       Before using this corpus for any other purposes, you can shrink
       it to a smaller size using the afl-cmin tool. The tool will find
       a smaller subset of files offering equivalent edge coverage.

crashes/ - unique test cases that cause the tested program to receive a fatal signal (e.g., SIGSEGV, SIGILL, SIGABRT). The entries are grouped by the received signal.
hangs/ - unique test cases that cause the tested program to time out. The default time limit before something is classified as a hang is the larger of 1 second and the value of the -t parameter. The value can be fine-tuned by setting AFL_HANG_TMOUT, but this is rar

Afl

Install / Use

README

================== american fuzzy lop