Best Practices

Best practices





Fuzzing a target with source code available

To learn how to fuzz a target if source code is available, see /docs/fuzzing_in_depth/.

Fuzzing a target with dlopen instrumented libraries

If a source code based fuzzing target loads instrumented libraries with dlopen() after the forkserver has been activated and non-colliding coverage instrumentation is used (PCGUARD (which is the default), or LTO), then this an issue, because this would enlarge the coverage map, but afl-fuzz doesn’t know about it.

The solution is to use AFL_PRELOAD for all dlopen()’ed libraries to ensure that all coverage targets are present on startup in the target, even if accessed only later with dlopen().

For PCGUARD instrumentation abort() is called if this is detected, for LTO there will either be no coverage for the instrumented dlopen()’ed libraries or you will see lots of crashes in the UI.

Note that this is not an issue if you use the inferiour afl-gcc-fast, afl-gcc orAFL_LLVM_INSTRUMENT=CLASSIC/NGRAM/CTX afl-clang-fast instrumentation.

Fuzzing a binary-only target

For a comprehensive guide, see /docs/fuzzing_binary-only_targets/.

Fuzzing a GUI program

If the GUI program can read the fuzz data from a file (via the command line, a fixed location or via an environment variable) without needing any user interaction, then it would be suitable for fuzzing.

Otherwise, it is not possible without modifying the source code - which is a very good idea anyway as the GUI functionality is a huge CPU/time overhead for the fuzzing.

So create a new main() that just reads the test case and calls the functionality for processing the input that the GUI program is using.

Fuzzing a network service

Fuzzing a network service does not work “out of the box”.

Using a network channel is inadequate for several reasons:

  • it has a slow-down of x10-20 on the fuzzing speed
  • it does not scale to fuzzing multiple instances easily,
  • instead of one initial data packet often a back-and-forth interplay of packets is needed for stateful protocols (which is totally unsupported by most coverage aware fuzzers).

The established method to fuzz network services is to modify the source code to read from a file or stdin (fd 0) (or even faster via shared memory, combine this with persistent mode instrumentation/ and you have a performance gain of x10 instead of a performance loss of over x10

  • that is a x100 difference!).

If modifying the source is not an option (e.g., because you only have a binary and perform binary fuzzing) you can also use a shared library with AFL_PRELOAD to emulate the network. This is also much faster than the real network would be. See utils/socket_fuzzing/.

There is an outdated AFL++ branch that implements networking if you are desperate though:


Improving speed

  1. Use llvm_mode: afl-clang-lto (llvm >= 11) or afl-clang-fast (llvm >= 9 recommended).
  2. Use persistent mode (x2-x20 speed increase).
  3. Instrument just what you are interested in, see instrumentation/
  4. If you do not use shmem persistent mode, use AFL_TMPDIR to put the input file directory on a tempfs location, see /docs/env_variables/.
  5. Improve Linux kernel performance: modify /etc/default/grub, set GRUB_CMDLINE_LINUX_DEFAULT="ibpb=off ibrs=off kpti=off l1tf=off mds=off mitigations=off no_stf_barrier noibpb noibrs nopcid nopti nospec_store_bypass_disable nospectre_v1 nospectre_v2 pcid=off pti=off spec_store_bypass_disable=off spectre_v2=off stf_barrier=off"; then update-grub and reboot (warning: makes the system less secure).
  6. Running on an ext2 filesystem with noatime mount option will be a bit faster than on any other journaling filesystem.
  7. Use your cores ( Using multiple cores)!

Improving stability

For fuzzing, a 100% stable target that covers all edges is the best case. A 90% stable target that covers all edges is, however, better than a 100% stable target that ignores 10% of the edges.

With instability, you basically have a partial coverage loss on an edge, with ignored functions you have a full loss on that edges.

There are functions that are unstable, but also provide value to coverage, e.g., init functions that use fuzz data as input. If, however, a function that has nothing to do with the input data is the source of instability, e.g., checking jitter, or is a hash map function etc., then it should not be instrumented.

To be able to exclude these functions (based on AFL++’s measured stability), the following process will allow to identify functions with variable edges.

Four steps are required to do this and it also requires quite some knowledge of coding and/or disassembly and is effectively possible only with afl-clang-fast PCGUARD and afl-clang-lto LTO instrumentation.

  1. Instrument to be able to find the responsible function(s):

    a) For LTO instrumented binaries, this can be documented during compile time, just set export AFL_LLVM_DOCUMENT_IDS=/path/to/a/file. This file will have one assigned edge ID and the corresponding function per line.

    b) For PCGUARD instrumented binaries, it is much more difficult. Here you can either modify the __sanitizer_cov_trace_pc_guard function in instrumentation/afl-llvm-rt.o.c to write a backtrace to a file if the ID in __afl_area_ptr[*guard] is one of the unstable edge IDs. (Example code is already there). Then recompile and reinstall llvm_mode and rebuild your target. Run the recompiled target with afl-fuzz for a while and then check the file that you wrote with the backtrace information. Alternatively, you can use gdb to hook __sanitizer_cov_trace_pc_guard_init on start, check to which memory address the edge ID value is written, and set a write breakpoint to that address (watch 0x.....).

    c) In other instrumentation types, this is not possible. So just recompile with the two mentioned above. This is just for identifying the functions that have unstable edges.

  2. Identify which edge ID numbers are unstable.

    Run the target with export AFL_DEBUG=1 for a few minutes then terminate. The out/fuzzer_stats file will then show the edge IDs that were identified as unstable in the var_bytes entry. You can match these numbers directly to the data you created in the first step. Now you know which functions are responsible for the instability

  3. Create a text file with the filenames/functions

    Identify which source code files contain the functions that you need to remove from instrumentation, or just specify the functions you want to skip for instrumentation. Note that optimization might inline functions!

    Follow this document on how to do this: instrumentation/

    If PCGUARD is used, then you need to follow this guide (needs llvm 12+!):

    Only exclude those functions from instrumentation that provide no value for coverage - that is if it does not process any fuzz data directly or indirectly (e.g., hash maps, thread management etc.). If, however, a function directly or indirectly handles fuzz data, then you should not put the function in a deny instrumentation list and rather live with the instability it comes with.

  4. Recompile the target

    Recompile, fuzz it, be happy :)

    This link explains this process for Fuzzbench.