AFL User Guide

Understanding the status screen

This document provides an overview of the status screen - plus tips for troubleshooting any warnings and red text shown in the UI.

0) A note about colors

The status screen and error messages use colors to keep things readable and attract your attention to the most important details. For example, red almost always means “consult this doc” :-)

Unfortunately, the UI will render correctly only if your terminal is using traditional un*x palette (white text on black background) or something close to that.

If you are using inverse video, you may want to change your settings, say:

  • For GNOME Terminal, go to Edit > Profile preferences, select the “colors” tab, and from the list of built-in schemes, choose “white on black”.
  • For the MacOS X Terminal app, open a new window using the “Pro” scheme via the Shell > New Window menu (or make “Pro” your default).

Alternatively, if you really like your current colors, you can edit config.h to comment out USE_COLORS, then do ‘make clean all’.

I’m not aware of any other simple way to make this work without causing other side effects - sorry about that.

With that out of the way, let’s talk about what’s actually on the screen…

1) Process timing

|        run time : 0 days, 8 hrs, 32 min, 43 sec    |
|   last new path : 0 days, 0 hrs, 6 min, 40 sec     |
| last uniq crash : none seen yet                    |
|  last uniq hang : 0 days, 1 hrs, 24 min, 32 sec    |

This section is fairly self-explanatory: it tells you how long the fuzzer has been running and how much time has elapsed since its most recent finds. This is broken down into “paths” (a shorthand for test cases that trigger new execution patterns), crashes, and hangs.

When it comes to timing: there is no hard rule, but most fuzzing jobs should be expected to run for days or weeks; in fact, for a moderately complex project, the first pass will probably take a day or so. Every now and then, some jobs will be allowed to run for months.

There’s one important thing to watch out for: if the tool is not finding new paths within several minutes of starting, you’re probably not invoking the target binary correctly and it never gets to parse the input files we’re throwing at it; another possible explanations are that the default memory limit (-m) is too restrictive, and the program exits after failing to allocate a buffer very early on; or that the input files are patently invalid and always fail a basic header check.

If there are no new paths showing up for a while, you will eventually see a big red warning in this section, too :-)

2) Overall results

|  cycles done : 0      |
|  total paths : 2095   |
| uniq crashes : 0      |
|   uniq hangs : 19     |

The first field in this section gives you the count of queue passes done so far - that is, the number of times the fuzzer went over all the interesting test cases discovered so far, fuzzed them, and looped back to the very beginning. Every fuzzing session should be allowed to complete at least one cycle; and ideally, should run much longer than that.

As noted earlier, the first pass can take a day or longer, so sit back and relax. If you want to get broader but more shallow coverage right away, try the -d option - it gives you a more familiar experience by skipping the deterministic fuzzing steps. It is, however, inferior to the standard mode in a couple of subtle ways.

To help make the call on when to hit Ctrl-C, the cycle counter is color-coded. It is shown in magenta during the first pass, progresses to yellow if new finds are still being made in subsequent rounds, then blue when that ends - and finally, turns green after the fuzzer hasn’t been seeing any action for a longer while.

The remaining fields in this part of the screen should be pretty obvious: there’s the number of test cases (“paths”) discovered so far, and the number of unique faults. The test cases, crashes, and hangs can be explored in real-time by browsing the output directory, as discussed in Interpreting output.

3) Cycle progress

|  now processing : 1296 (61.86%)     |
| paths timed out : 0 (0.00%)         |

This box tells you how far along the fuzzer is with the current queue cycle: it shows the ID of the test case it is currently working on, plus the number of inputs it decided to ditch because they were persistently timing out.

The “*” suffix sometimes shown in the first line means that the currently processed path is not “favored” (a property discussed later on, in section 6).

If you feel that the fuzzer is progressing too slowly, see the note about the -d option in section 2 of this doc.

4) Map coverage

|    map density : 10.15% / 29.07%     |
| count coverage : 4.03 bits/tuple     |

The section provides some trivia about the coverage observed by the instrumentation embedded in the target binary.

The first line in the box tells you how many branch tuples we have already hit, in proportion to how much the bitmap can hold. The number on the left describes the current input; the one on the right is the value for the entire input corpus.

Be wary of extremes:

  • Absolute numbers below 200 or so suggest one of three things: that the program is extremely simple; that it is not instrumented properly (e.g., due to being linked against a non-instrumented copy of the target library); or that it is bailing out prematurely on your input test cases. The fuzzer will try to mark this in pink, just to make you aware.

  • Percentages over 70% may very rarely happen with very complex programs that make heavy use of template-generated code.

    Because high bitmap density makes it harder for the fuzzer to reliably discern new program states, I recommend recompiling the binary with AFL_INST_RATIO=10 or so and trying again (see env_variables.txt).

    The fuzzer will flag high percentages in red. Chances are, you will never see that unless you’re fuzzing extremely hairy software (say, v8, perl, ffmpeg).

The other line deals with the variability in tuple hit counts seen in the binary. In essence, if every taken branch is always taken a fixed number of times for all the inputs we have tried, this will read “1.00”. As we manage to trigger other hit counts for every branch, the needle will start to move toward “8.00” (every bit in the 8-bit map hit), but will probably never reach that extreme.

Together, the values can be useful for comparing the coverage of several different fuzzing jobs that rely on the same instrumented binary.

5) Stage progress

|  now trying : interest 32/8         |
| stage execs : 3996/34.4k (11.62%)   |
| total execs : 27.4M                 |
|  exec speed : 891.7/sec             |

This part gives you an in-depth peek at what the fuzzer is actually doing right now. It tells you about the current stage, which can be any of:

  • calibration - a pre-fuzzing stage where the execution path is examined to detect anomalies, establish baseline execution speed, and so on. Executed very briefly whenever a new find is being made.
  • trim L/S - another pre-fuzzing stage where the test case is trimmed to the shortest form that still produces the same execution path. The length (L) and stepover (S) are chosen in general relationship to file size.
  • bitflip L/S - deterministic bit flips. There are L bits toggled at any given time, walking the input file with S-bit increments. The current L/S variants are: 1/1, 2/1, 4/1, 8/8, 16/8, 32/8.
  • arith L/8 - deterministic arithmetics. The fuzzer tries to subtract or add small integers to 8-, 16-, and 32-bit values. The stepover is always 8 bits.
  • interest L/8 - deterministic value overwrite. The fuzzer has a list of known “interesting” 8-, 16-, and 32-bit values to try. The stepover is 8 bits.
  • extras - deterministic injection of dictionary terms. This can be shown as “user” or “auto”, depending on whether the fuzzer is using a user-supplied dictionary (-x) or an auto-created one. You will also see “over” or “insert”, depending on whether the dictionary words overwrite existing data or are inserted by offsetting the remaining data to accommodate their length.
  • havoc - a sort-of-fixed-length cycle with stacked random tweaks. The operations attempted during this stage include bit flips, overwrites with random and “interesting” integers, block deletion, block duplication, plus assorted dictionary-related operations (if a dictionary is supplied in the first place).
  • splice - a last-resort strategy that kicks in after the first full queue cycle with no new paths. It is equivalent to ‘havoc’, except that it first splices together two random inputs from the queue at some arbitrarily selected midpoint.
  • sync - a stage used only when -M or -S is set (see Tips for parallel fuzzing). No real fuzzing is involved, but the tool scans the output from other fuzzers and imports test cases as necessary. The first time this is done, it may take several minutes or so.

The remaining fields should be fairly self-evident: there’s the exec count progress indicator for the current stage, a global exec counter, and a benchmark for the current program execution speed. This may fluctuate from one test case to another, but the benchmark should be ideally over 500 execs/sec most of the time - and if it stays below 100, the job will probably take very long.

The fuzzer will explicitly warn you about slow targets, too. If this happens, see Performance Tips for ideas on how to speed things up.

6) Findings in depth

| favored paths : 879 (41.96%)         |
|  new edges on : 423 (20.19%)         |
| total crashes : 0 (0 unique)         |
|  total tmouts : 24 (19 unique)       |

This gives you several metrics that are of interest mostly to complete nerds. The section includes the number of paths that the fuzzer likes the most based on a minimization algorithm baked into the code (these will get considerably more air time), and the number of test cases that actually resulted in better edge coverage (versus just pushing the branch hit counters up). There are also additional, more detailed counters for crashes and timeouts.

Note that the timeout counter is somewhat different from the hang counter; this one includes all test cases that exceeded the timeout, even if they did not exceed it by a margin sufficient to be classified as hangs.

7) Fuzzing strategy yields

|   bit flips : 57/289k, 18/289k, 18/288k             |
|  byte flips : 0/36.2k, 4/35.7k, 7/34.6k             |
| arithmetics : 53/2.54M, 0/537k, 0/55.2k             |
|  known ints : 8/322k, 12/1.32M, 10/1.70M            |
|  dictionary : 9/52k, 1/53k, 1/24k                   |
|       havoc : 1903/20.0M, 0/0                       |
|        trim : 20.31%/9201, 17.05%                   |

This is just another nerd-targeted section keeping track of how many paths we have netted, in proportion to the number of execs attempted, for each of the fuzzing strategies discussed earlier on. This serves to convincingly validate assumptions about the usefulness of the various approaches taken by afl-fuzz.

The trim strategy stats in this section are a bit different than the rest. The first number in this line shows the ratio of bytes removed from the input files; the second one corresponds to the number of execs needed to achieve this goal. Finally, the third number shows the proportion of bytes that, although not possible to remove, were deemed to have no effect and were excluded from some of the more expensive deterministic fuzzing steps.

8) Path geometry

|    levels : 5       |
|   pending : 1570    |
|  pend fav : 583     |
| own finds : 0       |
|  imported : 0       |
| stability : 100.00% |

The first field in this section tracks the path depth reached through the guided fuzzing process. In essence: the initial test cases supplied by the user are considered “level 1”. The test cases that can be derived from that through traditional fuzzing are considered “level 2”; the ones derived by using these as inputs to subsequent fuzzing rounds are “level 3”; and so forth. The maximum depth is therefore a rough proxy for how much value you’re getting out of the instrumentation-guided approach taken by afl-fuzz.

The next field shows you the number of inputs that have not gone through any fuzzing yet. The same stat is also given for “favored” entries that the fuzzer really wants to get to in this queue cycle (the non-favored entries may have to wait a couple of cycles to get their chance).

Next, we have the number of new paths found during this fuzzing section and imported from other fuzzer instances when doing parallelized fuzzing; and the extent to which identical inputs appear to sometimes produce variable behavior in the tested binary.

That last bit is actually fairly interesting: it measures the consistency of observed traces. If a program always behaves the same for the same input data, it will earn a score of 100%. When the value is lower but still shown in purple, the fuzzing process is unlikely to be negatively affected. If it goes into red, you may be in trouble, since AFL will have difficulty discerning between meaningful and “phantom” effects of tweaking the input file.

Now, most targets will just get a 100% score, but when you see lower figures, there are several things to look at:

  • The use of uninitialized memory in conjunction with some intrinsic sources of entropy in the tested binary. Harmless to AFL, but could be indicative of a security bug.
  • Attempts to manipulate persistent resources, such as left over temporary files or shared memory objects. This is usually harmless, but you may want to double-check to make sure the program isn’t bailing out prematurely. Running out of disk space, SHM handles, or other global resources can trigger this, too.
  • Hitting some functionality that is actually designed to behave randomly. Generally harmless. For example, when fuzzing sqlite, an input like ‘select random();’ will trigger a variable execution path.
  • Multiple threads executing at once in semi-random order. This is harmless when the ‘stability’ metric stays over 90% or so, but can become an issue if not. Here’s what to try:
    • Use afl-clang-fast from llvm_mode/ - it uses a thread-local tracking model that is less prone to concurrency issues,
    • See if the target can be compiled or run without threads. Common ./configure options include –without-threads, –disable-pthreads, or –disable-openmp.
    • Replace pthreads with GNU Pth (, which allows you to use a deterministic scheduler.
  • In persistent mode, minor drops in the “stability” metric can be normal, because not all the code behaves identically when re-entered; but major dips may signify that the code within __AFL_LOOP() is not behaving correctly on subsequent iterations (e.g., due to incomplete clean-up or reinitialization of the state) and that most of the fuzzing effort goes to waste.

The paths where variable behavior is detected are marked with a matching entry in the <out_dir>/queue/.state/variable_behavior/ directory, so you can look them up easily.

9) CPU load

[cpu: 25%]

This tiny widget shows the apparent CPU utilization on the local system. It is calculated by taking the number of processes in the “runnable” state, and then comparing it to the number of logical cores on the system.

If the value is shown in green, you are using fewer CPU cores than available on your system and can probably parallelize to improve performance; for tips on how to do that, see Tips for parallel fuzzing.

If the value is shown in red, your CPU is possibly oversubscribed, and running additional fuzzers may not give you any benefits.

Of course, this benchmark is very simplistic; it tells you how many processes are ready to run, but not how resource-hungry they may be. It also doesn’t distinguish between physical cores, logical cores, and virtualized CPUs; the performance characteristics of each of these will differ quite a bit.

If you want a more accurate measurement, you can run the afl-gotcpu utility from the command line.

10) Addendum: status and plot files

For unattended operation, some of the key status screen information can be also found in a machine-readable format in the fuzzer_stats file in the output directory. This includes:

  • start_time - unix time indicating the start time of afl-fuzz
  • last_update - unix time corresponding to the last update of this file
  • fuzzer_pid - PID of the fuzzer process
  • cycles_done - queue cycles completed so far
  • execs_done - number of execve() calls attempted
  • execs_per_sec - current number of execs per second
  • paths_total - total number of entries in the queue
  • paths_found - number of entries discovered through local fuzzing
  • paths_imported - number of entries imported from other instances
  • max_depth - number of levels in the generated data set
  • cur_path - currently processed entry number
  • pending_favs - number of favored entries still waiting to be fuzzed
  • pending_total - number of all entries waiting to be fuzzed
  • stability - percentage of bitmap bytes that behave consistently
  • variable_paths - number of test cases showing variable behavior
  • unique_crashes - number of unique crashes recorded
  • unique_hangs - number of unique hangs encountered

Most of these map directly to the UI elements discussed earlier on.

On top of that, you can also find an entry called ‘plot_data’, containing a plottable history for most of these fields. If you have gnuplot installed, you can turn this into a nice progress report with the included ‘afl-plot’ tool.

Environmental variables

This document discusses the environment variables used by American Fuzzy Lop to expose various exotic functions that may be (rarely) useful for power users or for some types of custom fuzzing setups. See README for the general instruction manual.

1) Settings for afl-gcc, afl-clang, and afl-as

Because they can’t directly accept command-line options, the compile-time tools make fairly broad use of environmental variables:

  • Setting AFL_HARDEN automatically adds code hardening options when invoking the downstream compiler. This currently includes -D_FORTIFY_SOURCE=2 and -fstack-protector-all. The setting is useful for catching non-crashing memory bugs at the expense of a very slight (sub-5%) performance loss.

  • By default, the wrapper appends -O3 to optimize builds. Very rarely, this will cause problems in programs built with -Werror, simply because -O3 enables more thorough code analysis and can spew out additional warnings. To disable optimizations, set AFL_DONT_OPTIMIZE.

  • Setting AFL_USE_ASAN automatically enables ASAN, provided that your compiler supports that. Note that fuzzing with ASAN is mildly challenging - see Using ASAN with AFL.

    (You can also enable MSAN via AFL_USE_MSAN; ASAN and MSAN come with the same gotchas; the modes are mutually exclusive. UBSAN and other exotic sanitizers are not officially supported yet, but are easy to get to work by hand.)

  • Setting AFL_CC, AFL_CXX, and AFL_AS lets you use alternate downstream compilation tools, rather than the default ‘clang’, ‘gcc’, or ‘as’ binaries in your $PATH.

  • AFL_PATH can be used to point afl-gcc to an alternate location of afl-as. One possible use of this is experimental/clang_asm_normalize/, which lets you instrument hand-written assembly when compiling clang code by plugging a normalizer into the chain. (There is no equivalent feature for GCC.)

  • Setting AFL_INST_RATIO to a percentage between 0 and 100% controls the probability of instrumenting every branch. This is (very rarely) useful when dealing with exceptionally complex programs that saturate the output bitmap. Examples include v8, ffmpeg, and perl.

    (If this ever happens, afl-fuzz will warn you ahead of the time by displaying the “bitmap density” field in fiery red.)

    Setting AFL_INST_RATIO to 0 is a valid choice. This will instrument only the transitions between function entry points, but not individual branches.

  • AFL_NO_BUILTIN causes the compiler to generate code suitable for use with (but perhaps running a bit slower than without the flag).

  • TMPDIR is used by afl-as for temporary files; if this variable is not set, the tool defaults to /tmp.

  • Setting AFL_KEEP_ASSEMBLY prevents afl-as from deleting instrumented assembly files. Useful for troubleshooting problems or understanding how the tool works. To get them in a predictable place, try something like:

    mkdir assembly_here TMPDIR=$PWD/assembly_here AFL_KEEP_ASSEMBLY=1 make clean all

  • Setting AFL_QUIET will prevent afl-cc and afl-as banners from being displayed during compilation, in case you find them distracting.

2) Settings for afl-clang-fast

The native LLVM instrumentation helper accepts a subset of the settings discussed in section #1, with the exception of:

  • AFL_AS, since this toolchain does not directly invoke GNU as.
  • TMPDIR and AFL_KEEP_ASSEMBLY, since no temporary assembly files are created.

Note that AFL_INST_RATIO will behave a bit differently than for afl-gcc, because functions are not instrumented unconditionally - so low values will have a more striking effect. For this tool, 0 is not a valid choice.

3) Settings for afl-fuzz

The main fuzzer binary accepts several options that disable a couple of sanity checks or alter some of the more exotic semantics of the tool:

  • Setting AFL_SKIP_CPUFREQ skips the check for CPU scaling policy. This is useful if you can’t change the defaults (e.g., no root access to the system) and are OK with some performance loss.

  • Setting AFL_NO_FORKSRV disables the forkserver optimization, reverting to fork + execve() call for every tested input. This is useful mostly when working with unruly libraries that create threads or do other crazy things when initializing (before the instrumentation has a chance to run).

    Note that this setting inhibits some of the user-friendly diagnostics normally done when starting up the forkserver and causes a pretty significant performance drop.

  • AFL_EXIT_WHEN_DONE causes afl-fuzz to terminate when all existing paths have been fuzzed and there were no new finds for a while. This would be normally indicated by the cycle counter in the UI turning green. May be convenient for some types of automated jobs.

  • Setting AFL_NO_AFFINITY disables attempts to bind to a specific CPU core on Linux systems. This slows things down, but lets you run more instances of afl-fuzz than would be prudent (if you really want to).

  • AFL_SKIP_CRASHES causes AFL to tolerate crashing files in the input queue. This can help with rare situations where a program crashes only intermittently, but it’s not really recommended under normal operating conditions.

  • Setting AFL_HANG_TMOUT allows you to specify a different timeout for deciding if a particular test case is a “hang”. The default is 1 second or the value of the -t parameter, whichever is larger. Dialing the value down can be useful if you are very concerned about slow inputs, or if you don’t want AFL to spend too much time classifying that stuff and just rapidly put all timeouts in that bin.

  • AFL_NO_ARITH causes AFL to skip most of the deterministic arithmetics. This can be useful to speed up the fuzzing of text-based file formats.

  • AFL_SHUFFLE_QUEUE randomly reorders the input queue on startup. Requested by some users for unorthodox parallelized fuzzing setups, but not advisable otherwise.

  • When developing custom instrumentation on top of afl-fuzz, you can use AFL_SKIP_BIN_CHECK to inhibit the checks for non-instrumented binaries and shell scripts; and AFL_DUMB_FORKSRV in conjunction with the -n setting to instruct afl-fuzz to still follow the fork server protocol without expecting any instrumentation data in return.

  • When running in the -M or -S mode, setting AFL_IMPORT_FIRST causes the fuzzer to import test cases from other instances before doing anything else. This makes the “own finds” counter in the UI more accurate. Beyond counter aesthetics, not much else should change.

  • Setting AFL_POST_LIBRARY allows you to configure a postprocessor for mutated files - say, to fix up checksums. See experimental/post_library/ for more.

  • AFL_FAST_CAL keeps the calibration stage about 2.5x faster (albeit less precise), which can help when starting a session against a slow target.

  • The CPU widget shown at the bottom of the screen is fairly simplistic and may complain of high load prematurely, especially on systems with low core counts. To avoid the alarming red color, you can set AFL_NO_CPU_RED.

  • In QEMU mode (-Q), AFL_PATH will be searched for afl-qemu-trace.

  • Setting AFL_PRELOAD causes AFL to set LD_PRELOAD for the target binary without disrupting the afl-fuzz process itself. This is useful, among other things, for bootstrapping

  • Setting AFL_NO_UI inhibits the UI altogether, and just periodically prints some basic stats. This behavior is also automatically triggered when the output from afl-fuzz is redirected to a file or to a pipe.

  • If you are Jakub, you may need AFL_I_DONT_CARE_ABOUT_MISSING_CRASHES. Others need not apply.

  • Benchmarking only: AFL_BENCH_JUST_ONE causes the fuzzer to exit after processing the first queue entry; and AFL_BENCH_UNTIL_CRASH causes it to exit soon after the first crash is found.

4) Settings for afl-qemu-trace

The QEMU wrapper used to instrument binary-only code supports several settings:

  • It is possible to set AFL_INST_RATIO to skip the instrumentation on some of the basic blocks, which can be useful when dealing with very complex binaries.
  • Setting AFL_INST_LIBS causes the translator to also instrument the code inside any dynamically linked libraries (notably including glibc).
  • The underlying QEMU binary will recognize any standard “user space emulation” variables (e.g., QEMU_STACK_SIZE), but there should be no reason to touch them.

5) Settings for afl-cmin

The corpus minimization script offers very little customization:

  • Setting AFL_PATH offers a way to specify the location of afl-showmap and afl-qemu-trace (the latter only in -Q mode).
  • AFL_KEEP_TRACES makes the tool keep traces and other metadata used for minimization and normally deleted at exit. The files can be found in the <out_dir>/.traces/*.
  • AFL_ALLOW_TMP permits this and some other scripts to run in /tmp. This is a modest security risk on multi-user systems with rogue users, but should be safe on dedicated fuzzing boxes.

6) Settings for afl-tmin

Virtually nothing to play with. Well, in QEMU mode (-Q), AFL_PATH will be searched for afl-qemu-trace. In addition to this, TMPDIR may be used if a temporary file can’t be created in the current working directory.

You can specify AFL_TMIN_EXACT if you want afl-tmin to require execution paths to match when minimizing crashes. This will make minimization less useful, but may prevent the tool from “jumping” from one crashing condition to another in very buggy software. You probably want to combine it with the -e flag.

7) Settings for afl-analyze

You can set AFL_ANALYZE_HEX to get file offsets printed as hexadecimal instead of decimal.

8) Settings for

The library honors three environmental variables:

  • AFL_LD_LIMIT_MB caps the size of the maximum heap usage permitted by the library, in megabytes. The default value is 1 GB. Once this is exceeded, allocations will return NULL.
  • AFL_LD_HARD_FAIL alters the behavior by calling abort() on excessive allocations, thus causing what AFL would perceive as a crash. Useful for programs that are supposed to maintain a specific memory footprint.
  • AFL_LD_VERBOSE causes the library to output some diagnostic messages that may be useful for pinpointing the cause of any observed issues.
  • AFL_LD_NO_CALLOC_OVER inhibits abort() on calloc() overflows. Most of the common allocators check for that internally and return NULL, so it’s a security risk only in more exotic setups.

9) Settings for

This library accepts AFL_TOKEN_FILE to indicate the location to which the discovered tokens should be written.

10) Third-party variables set by afl-fuzz & other tools

Several variables are not directly interpreted by afl-fuzz, but are set to optimal values if not already present in the environment:

  • By default, LD_BIND_NOW is set to speed up fuzzing by forcing the linker to do all the work before the fork server kicks in. You can override this by setting LD_BIND_LAZY beforehand, but it is almost certainly pointless.

  • By default, ASAN_OPTIONS are set to:

    abort_on_error=1 detect_leaks=0 symbolize=0 allocator_may_return_null=1

    If you want to set your own options, be sure to include abort_on_error=1 - otherwise, the fuzzer will not be able to detect crashes in the tested app. Similarly, include symbolize=0, since without it, AFL may have difficulty telling crashes and hangs apart.

  • In the same vein, by default, MSAN_OPTIONS are set to:

    exit_code=86 (required for legacy reasons) abort_on_error=1 symbolize=0 msan_track_origins=0 allocator_may_return_null=1

    Be sure to include the first one when customizing anything, since some MSAN versions don’t call abort() on error, and we need a way to detect faults.

Tips for parallel fuzzing

This document talks about synchronizing afl-fuzz jobs on a single machine or across a fleet of systems. See README for the general instruction manual.

1) Introduction

Every copy of afl-fuzz will take up one CPU core. This means that on an n-core system, you can almost always run around n concurrent fuzzing jobs with virtually no performance hit (you can use the afl-gotcpu tool to make sure).

In fact, if you rely on just a single job on a multi-core system, you will be underutilizing the hardware. So, parallelization is usually the right way to go.

When targeting multiple unrelated binaries or using the tool in “dumb” (-n) mode, it is perfectly fine to just start up several fully separate instances of afl-fuzz. The picture gets more complicated when you want to have multiple fuzzers hammering a common target: if a hard-to-hit but interesting test case is synthesized by one fuzzer, the remaining instances will not be able to use that input to guide their work.

To help with this problem, afl-fuzz offers a simple way to synchronize test cases on the fly.

2) Single-system parallelization

If you wish to parallelize a single job across multiple cores on a local system, simply create a new, empty output directory (“sync dir”) that will be shared by all the instances of afl-fuzz; and then come up with a naming scheme for every instance - say, “fuzzer01”, “fuzzer02”, etc.

Run the first one (“master”, -M) like this:

$ ./afl-fuzz -i testcase_dir -o sync_dir -M fuzzer01 [...other stuff...]

…and then, start up secondary (-S) instances like this:

$ ./afl-fuzz -i testcase_dir -o sync_dir -S fuzzer02 [...other stuff...]
$ ./afl-fuzz -i testcase_dir -o sync_dir -S fuzzer03 [...other stuff...]

Each fuzzer will keep its state in a separate subdirectory, like so:


Each instance will also periodically rescan the top-level sync directory for any test cases found by other fuzzers - and will incorporate them into its own fuzzing when they are deemed interesting enough.

The difference between the -M and -S modes is that the master instance will still perform deterministic checks; while the secondary instances will proceed straight to random tweaks. If you don’t want to do deterministic fuzzing at all, it’s OK to run all instances with -S. With very slow or complex targets, or when running heavily parallelized jobs, this is usually a good plan.

Note that running multiple -M instances is wasteful, although there is an experimental support for parallelizing the deterministic checks. To leverage that, you need to create -M instances like so:

$ ./afl-fuzz -i testcase_dir -o sync_dir -M masterA:1/3 [...]
$ ./afl-fuzz -i testcase_dir -o sync_dir -M masterB:2/3 [...]
$ ./afl-fuzz -i testcase_dir -o sync_dir -M masterC:3/3 [...]

…where the first value after ‘:’ is the sequential ID of a particular master instance (starting at 1), and the second value is the total number of fuzzers to distribute the deterministic fuzzing across. Note that if you boot up fewer fuzzers than indicated by the second number passed to -M, you may end up with poor coverage.

You can also monitor the progress of your jobs from the command line with the provided afl-whatsup tool. When the instances are no longer finding new paths, it’s probably time to stop.

WARNING: Exercise caution when explicitly specifying the -f option. Each fuzzer must use a separate temporary file; otherwise, things will go south. One safe example may be:

$ ./afl-fuzz [...] -S fuzzer10 -f file10.txt ./fuzzed/binary @@
$ ./afl-fuzz [...] -S fuzzer11 -f file11.txt ./fuzzed/binary @@
$ ./afl-fuzz [...] -S fuzzer12 -f file12.txt ./fuzzed/binary @@

This is not a concern if you use @@ without -f and let afl-fuzz come up with the file name.

3) Multi-system parallelization

The basic operating principle for multi-system parallelization is similar to the mechanism explained in section 2. The key difference is that you need to write a simple script that performs two actions:

  • Uses SSH with authorized_keys to connect to every machine and retrieve a tar archive of the /path/to/sync_dir/<fuzzer_id>/queue/ directories for every <fuzzer_id> local to the machine. It’s best to use a naming scheme that includes host name in the fuzzer ID, so that you can do something like:

    for s in {1..10}; do
      ssh user@host${s} "tar -czf - sync/host${s}_fuzzid*/[qf]*" >host${s}.tgz
  • Distributes and unpacks these files on all the remaining machines, e.g.:

    for s in {1..10}; do
      for d in {1..10}; do
        test "$s" = "$d" && continue
        ssh user@host${d} 'tar -kxzf -' <host${s}.tgz

There is an example of such a script in experimental/distributed_fuzzing/; you can also find a more featured, experimental tool developed by Martijn Bogaard at:

Another client-server implementation from Richo Healey is:

Note that these third-party tools are unsafe to run on systems exposed to the Internet or to untrusted users.

When developing custom test case sync code, there are several optimizations to keep in mind:

  • The synchronization does not have to happen very often; running the task every 30 minutes or so may be perfectly fine.

  • There is no need to synchronize crashes/ or hangs/; you only need to copy over queue/* (and ideally, also fuzzer_stats).

  • It is not necessary (and not advisable!) to overwrite existing files; the -k option in tar is a good way to avoid that.

  • There is no need to fetch directories for fuzzers that are not running locally on a particular machine, and were simply copied over onto that system during earlier runs.

  • For large fleets, you will want to consolidate tarballs for each host, as this will let you use n SSH connections for sync, rather than n*(n-1).

    You may also want to implement staged synchronization. For example, you could have 10 groups of systems, with group 1 pushing test cases only to group 2; group 2 pushing them only to group 3; and so on, with group eventually 10 feeding back to group 1.

    This arrangement would allow test interesting cases to propagate across the fleet without having to copy every fuzzer queue to every single host.

  • You do not want a “master” instance of afl-fuzz on every system; you should run them all with -S, and just designate a single process somewhere within the fleet to run with -M.

It is not advisable to skip the synchronization script and run the fuzzers directly on a network filesystem; unexpected latency and unkillable processes in I/O wait state can mess things up.

4) Remote monitoring and data collection

You can use screen, nohup, tmux, or something equivalent to run remote instances of afl-fuzz. If you redirect the program’s output to a file, it will automatically switch from a fancy UI to more limited status reports. There is also basic machine-readable information always written to the fuzzer_stats file in the output directory. Locally, that information can be interpreted with afl-whatsup.

In principle, you can use the status screen of the master (-M) instance to monitor the overall fuzzing progress and decide when to stop. In this mode, the most important signal is just that no new paths are being found for a longer while. If you do not have a master instance, just pick any single secondary instance to watch and go by that.

You can also rely on that instance’s output directory to collect the synthesized corpus that covers all the noteworthy paths discovered anywhere within the fleet. Secondary (-S) instances do not require any special monitoring, other than just making sure that they are up.

Keep in mind that crashing inputs are not automatically propagated to the master instance, so you may still want to monitor for crashes fleet-wide from within your synchronization or health checking scripts (see afl-whatsup).

5) Asymmetric setups

It is perhaps worth noting that all of the following is permitted:

  • Running afl-fuzz with conjunction with other guided tools that can extend coverage (e.g., via concolic execution). Third-party tools simply need to follow the protocol described above for pulling new test cases from out_dir/<fuzzer_id>/queue/* and writing their own finds to sequentially numbered id:nnnnnn files in out_dir/<ext_tool_id>/queue/*.

  • Running some of the synchronized fuzzers with different (but related) target binaries. For example, simultaneously stress-testing several different JPEG parsers (say, IJG jpeg and libjpeg-turbo) while sharing the discovered test cases can have synergistic effects and improve the overall coverage.

    (In this case, running one -M instance per each binary is a good plan.)

  • Having some of the fuzzers invoke the binary in different ways. For example, ‘djpeg’ supports several DCT modes, configurable with a command-line flag, while ‘dwebp’ supports incremental and one-shot decoding. In some scenarios, going after multiple distinct modes and then pooling test cases will improve coverage.

  • Much less convincingly, running the synchronized fuzzers with different starting test cases (e.g., progressive and standard JPEG) or dictionaries. The synchronization mechanism ensures that the test sets will get fairly homogeneous over time, but it introduces some initial variability.