1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
|
# Fuzzing binary-only programs with afl++
afl++, libfuzzer and others are great if you have the source code, and
it allows for very fast and coverage guided fuzzing.
However, if there is only the binary program and no source code available,
then standard `afl-fuzz -n` (dumb mode) is not effective.
The following is a description of how these binaries can be fuzzed with afl++
!!!!!
TL;DR: try DYNINST with afl-dyninst. If it produces too many crashes then
use afl -Q qemu_mode, or better: use both in parallel.
!!!!!
## QEMU
Qemu is the "native" solution to the program.
It is available in the ./qemu_mode/ directory and once compiled it can
be accessed by the afl-fuzz -Q command line option.
The speed decrease is at about 50%.
It is the easiest to use alternative and even works for cross-platform binaries.
Note that there is also honggfuzz: [https://github.com/google/honggfuzz](https://github.com/google/honggfuzz)
which now has a qemu_mode, but its performance is just 1.5%!
As it is included in afl++ this needs no URL.
## WINE+QEMU
Wine mode can run Win32 PE binaries with the QEMU instrumentation.
It needs Wine, python3 and the pefile python package installed.
As it is included in afl++ this needs no URL.
## UNICORN
Unicorn is a fork of QEMU. The instrumentation is, therefore, very similar.
In contrast to QEMU, Unicorn does not offer a full system or even userland
emulation. Runtime environment and/or loaders have to be written from scratch,
if needed. On top, block chaining has been removed. This means the speed boost
introduced in the patched QEMU Mode of afl++ cannot simply be ported over to
Unicorn. For further information, check out ./unicorn_mode.txt.
As it is included in afl++ this needs no URL.
## DYNINST
Dyninst is a binary instrumentation framework similar to Pintool and
Dynamorio (see far below). However whereas Pintool and Dynamorio work at
runtime, dyninst instruments the target at load time, and then let it run -
or save the binary with the changes.
This is great for some things, e.g. fuzzing, and not so effective for others,
e.g. malware analysis.
So what we can do with dyninst is taking every basic block, and put afl's
instrumention code in there - and then save the binary.
Afterwards we can just fuzz the newly saved target binary with afl-fuzz.
Sounds great? It is. The issue though - it is a non-trivial problem to
insert instructions, which change addresses in the process space, so that
everything is still working afterwards. Hence more often than not binaries
crash when they are run.
The speed decrease is about 15-35%, depending on the optimization options
used with afl-dyninst.
So if Dyninst works, it is the best option available. Otherwise it just
doesn't work well.
[https://github.com/vanhauser-thc/afl-dyninst](https://github.com/vanhauser-thc/afl-dyninst)
## INTEL-PT
If you have a newer Intel CPU, you can make use of Intels processor trace.
The big issue with Intel's PT is the small buffer size and the complex
encoding of the debug information collected through PT.
This makes the decoding very CPU intensive and hence slow.
As a result, the overall speed decrease is about 70-90% (depending on
the implementation and other factors).
There are two afl intel-pt implementations:
1. [https://github.com/junxzm1990/afl-pt](https://github.com/junxzm1990/afl-pt)
=> this needs Ubuntu 14.04.05 without any updates and the 4.4 kernel.
2. [https://github.com/hunter-ht-2018/ptfuzzer](https://github.com/hunter-ht-2018/ptfuzzer)
=> this needs a 4.14 or 4.15 kernel. the "nopti" kernel boot option must
be used. This one is faster than the other.
Note that there is also honggfuzz: https://github.com/google/honggfuzz
But its IPT performance is just 6%!
## CORESIGHT
Coresight is ARM's answer to Intel's PT.
There is no implementation so far which handle coresight and getting
it working on an ARM Linux is very difficult due to custom kernel building
on embedded systems is difficult. And finding one that has coresight in
the ARM chip is difficult too.
My guess is that it is slower than Qemu, but faster than Intel PT.
If anyone finds any coresight implementation for afl please ping me: vh@thc.org
## FRIDA
Frida is a dynamic instrumentation engine like Pintool, Dyninst and Dynamorio.
What is special is that it is written Python, and scripted with Javascript.
It is mostly used to reverse binaries on mobile phones however can be used
everywhere.
There is a WIP fuzzer available at [https://github.com/andreafioraldi/frida-fuzzer](https://github.com/andreafioraldi/frida-fuzzer)
## PIN & DYNAMORIO
Pintool and Dynamorio are dynamic instrumentation engines, and they can be
used for getting basic block information at runtime.
Pintool is only available for Intel x32/x64 on Linux, Mac OS and Windows
whereas Dynamorio is additionally available for ARM and AARCH64.
Dynamorio is also 10x faster than Pintool.
The big issue with Dynamorio (and therefore Pintool too) is speed.
Dynamorio has a speed decrease of 98-99%
Pintool has a speed decrease of 99.5%
Hence Dynamorio is the option to go for if everything fails, and Pintool
only if Dynamorio fails too.
Dynamorio solutions:
* [https://github.com/vanhauser-thc/afl-dynamorio](https://github.com/vanhauser-thc/afl-dynamorio)
* [https://github.com/mxmssh/drAFL](https://github.com/mxmssh/drAFL)
* [https://github.com/googleprojectzero/winafl/](https://github.com/googleprojectzero/winafl/) <= very good but windows only
Pintool solutions:
* [https://github.com/vanhauser-thc/afl-pin](https://github.com/vanhauser-thc/afl-pin)
* [https://github.com/mothran/aflpin](https://github.com/mothran/aflpin)
* [https://github.com/spinpx/afl_pin_mode](https://github.com/spinpx/afl_pin_mode) <= only old Pintool version supported
## Non-AFL solutions
There are many binary-only fuzzing frameworks.
Some are great for CTFs but don't work with large binaries, others are very
slow but have good path discovery, some are very hard to set-up ...
* QSYM: [https://github.com/sslab-gatech/qsym](https://github.com/sslab-gatech/qsym)
* Manticore: [https://github.com/trailofbits/manticore](https://github.com/trailofbits/manticore)
* S2E: [https://github.com/S2E](https://github.com/S2E)
* ... please send me any missing that are good
## Closing words
That's it! News, corrections, updates? Send an email to vh@thc.org
|
