If you find an interesting or important question missing, submit it via https://github.com/AFLplusplus/AFLplusplus/discussions.
What is the difference between AFL and AFL++?
AFL++ is a superior fork to Google's AFL - more speed, more and better mutations, more and better instrumentation, custom module support, etc.
American Fuzzy Lop (AFL) was developed by Michał "lcamtuf" Zalewski starting in 2013/2014, and when he left Google end of 2017 he stopped developing it.
At the end of 2019, the Google fuzzing team took over maintenance of AFL, however, it is only accepting PRs from the community and is not developing enhancements anymore.
In the second quarter of 2019, 1 1/2 years later, when no further development of AFL had happened and it became clear there would none be coming, AFL++ was born, where initially community patches were collected and applied for bug fixes and enhancements. Then from various AFL spin-offs - mostly academic research - features were integrated. This already resulted in a much advanced AFL.
Until the end of 2019, the AFL++ team had grown to four active developers which then implemented their own research and features, making it now by far the most flexible and feature rich guided fuzzer available as open source. And in independent fuzzing benchmarks it is one of the best fuzzers available, e.g., Fuzzbench Report.
Is AFL++ a whitebox, graybox, or blackbox fuzzer?
The definition of the terms whitebox, graybox, and blackbox fuzzing varies from one source to another. For example, "graybox fuzzing" could mean binary-only or source code fuzzing, or something completely different. Therefore, we try to avoid them.
The Fuzzing Book describes the original AFL to be a graybox fuzzer. In that sense, AFL++ is also a graybox fuzzer.
What is an "edge"?
A program contains functions, functions contain the compiled machine code.
The compiled machine code in a function can be in a single or many basic blocks. A basic block is the largest possible number of subsequent machine
code instructions that has exactly one entry point (which can be be entered by
multiple other basic blocks) and runs linearly without branching or jumping to
other addresses (except at the end).
function() {
A:
some
code
B:
if (x) goto C; else goto D;
C:
some code
goto E
D:
some code
goto B
E:
return
}
Every code block between two jump locations is a basic block.
An edge is then the unique relationship between two directly connected
basic blocks (from the code example above):
Block A
|
v
Block B <------+
/ \ |
v v |
Block C Block D --+
\
v
Block E
Every line between two blocks is an edge. Note that a few basic block loop
to itself, this too would be an edge.
How can I fuzz a binary-only target?
AFL++ is a great fuzzer if you have the source code available.
However, if there is only the binary program and no source code available, then the standard non-instrumented mode is not effective.
To learn how these binaries can be fuzzed, read fuzzing_binary-only_targets.md.
How can I fuzz a network service?
The short answer is - you cannot, at least not "out of the box".
For more information on fuzzing network services, see best_practices.md#fuzzing-a-network-service.
How can I fuzz a GUI program?
Not all GUI programs are suitable for fuzzing. If the GUI program can read the fuzz data from a file without needing any user interaction, then it would be suitable for fuzzing.
For more information on fuzzing GUI programs, see best_practices.md#fuzzing-a-gui-program.
What makes a good performance?
Good performance generally means "making the fuzzing results better". This can be influenced by various factors, for example, speed (finding lots of paths quickly) or thoroughness (working with decreased speed, but finding better mutations).
How can I improve the fuzzing speed?
There are a few things you can do to improve the fuzzing speed, see best_practices.md#improving-speed.
Why is my stability below 100%?
Stability is measured by how many percent of the edges in the target are "stable". Sending the same input again and again should take the exact same path through the target every time. If that is the case, the stability is 100%.
If, however, randomness happens, e.g., a thread reading other external data, reaction to timing, etc., then in some of the re-executions with the same data the edge coverage result will be different across runs. Those edges that change are then flagged "unstable".
The more "unstable" edges there are, the harder it is for AFL++ to identify valid new paths.
If you fuzz in persistent mode (AFL_LOOP or LLVMFuzzerTestOneInput()
harnesses, a large number of unstable edges can mean that the target keeps
internal state and therefore it is possible that crashes cannot be replayed.
In such a case do either not fuzz in persistent mode (remove AFL_LOOP()
from your harness or call LLVMFuzzerTestOneInput() harnesses with @@),
or set a low AFL_LOOP value, e.g. 100, and enable AFL_PERSISTENT_RECORD
in config.h with the same value.
A value above 90% is usually fine and a value above 80% is also still ok, and even a value above 20% can still result in successful finds of bugs. However, it is recommended that for values below 90% or 80% you should take countermeasures to improve stability.
For more information on stability and how to improve the stability value, see best_practices.md#improving-stability.
What are power schedules?
Not every item in our queue/corpus is the same, some are more interesting, others provide little value. A power schedule measures how "interesting" a value is, and depending on the calculated value spends more or less time mutating it.
AFL++ comes with several power schedules, initially ported from AFLFast, however, modified to be more effective and several more modes added.
The most effective modes are -p fast (default) and -p explore.
If you fuzz with several parallel afl-fuzz instances, then it is beneficial
to assign a different schedule to each instance, however the majority should
be fast and explore.
It does not make sense to explain the details of the calculation and reasoning behind all of the schedules. If you are interested, read the source code and the AFLFast paper.
FATAL: forkserver is already up but an instrumented dlopen library loaded afterwards
It can happen that you see this error on startup when fuzzing a target:
[-] FATAL: forkserver is already up, but an instrumented dlopen() library
loaded afterwards. You must AFL_PRELOAD such libraries to be able
to fuzz them or LD_PRELOAD to run outside of afl-fuzz.
To ignore this set AFL_IGNORE_PROBLEMS=1.
As the error describes, a dlopen() call is happening in the target that is loading an instrumented library after the forkserver is already in place. This is a problem for afl-fuzz because when the forkserver is started, we must know the map size already and it can't be changed later.
The best solution is to simply set AFL_PRELOAD=foo.so to the libraries that
are dlopen'ed (e.g., use strace to see which), or to set a manual forkserver
after the final dlopen().
If this is not a viable option, you can set AFL_IGNORE_PROBLEMS=1 but then
the existing map will be used also for the newly loaded libraries, which
allows it to work, however, the efficiency of the fuzzing will be partially
degraded. Note that there is additionally AFL_IGNORE_PROBLEMS_COVERAGE to
additionally tell AFL++ to ignore any coverage from the late loaded libaries.
I got a weird compile error from clang.
If you see this kind of error when trying to instrument a target with afl-cc/afl-clang-fast/afl-clang-lto:
/prg/tmp/llvm-project/build/bin/clang-13: symbol lookup error: /usr/local/bin/../lib/afl//cmplog-instructions-pass.so: undefined symbol: _ZNK4llvm8TypeSizecvmEv
clang-13: error: unable to execute command: No such file or directory
clang-13: error: clang frontend command failed due to signal (use -v to see invocation)
clang version 13.0.0 (https://github.com/llvm/llvm-project 1d7cf550721c51030144f3cd295c5789d51c4aad)
Target: x86_64-unknown-linux-gnu
Thread model: posix
InstalledDir: /prg/tmp/llvm-project/build/bin
clang-13: note: diagnostic msg:
********************
Then this means that your OS updated the clang installation from an upgrade package and because of that the AFL++ llvm plugins do not match anymore.
Solution: git pull ; make clean install of AFL++.
AFL++ map size warning.
When you run a large instrumented program stand-alone or via afl-showmap you might see a warning like the following:
Warning: AFL++ tools might need to set AFL_MAP_SIZE to 223723 to be able to run this instrumented program if this crashes!
Depending how the target works it might also crash afterwards.
Solution: just do an export AFL_MAP_SIZE=(the value in the warning).
Linker errors.
If you compile C++ harnesses and see undefined reference errors for
variables named __afl_..., e.g.:
/usr/bin/ld: /tmp/test-d3085f.o: in function `foo::test()':
test.cpp:(.text._ZN3fooL4testEv[_ZN3fooL4testEv]+0x35): undefined reference to `foo::__afl_connected'
clang: error: linker command failed with exit code 1 (use -v to see invocation)
Then you use AFL++ macros like __AFL_LOOP within a namespace and this
will not work.
Solution: Move that harness portion to the global namespace, e.g. before:
#include <cstdio>
namespace foo {
static void test() {
while(__AFL_LOOP(1000)) {
foo::function();
}
}
}
int main(int argc, char** argv) {
foo::test();
return 0;
}
after:
#include <cstdio>
static void mytest() {
while(__AFL_LOOP(1000)) {
foo::function();
}
}
namespace foo {
static void test() {
mytest();
}
}
int main(int argc, char** argv) {
foo::test();
return 0;
}