GAN-based Seed Generation for Efficient Fuzzing
Shyamili Toluchuri
1
, Aishwarya Upadhyay
1
, Smita Naval
1
, Vijay Laxmi
1
and Manoj Singh Gaur
2
1
Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, India
2
Department of Computer Science and Engineering, Indian Institute of Technology Jammu, India
Keywords:
Software Testing, Vulnerability Assessment, Fuzzing, Gray-Box, GAN.
Abstract:
Software vulnerabilities are a substantial concern in development, with testing being crucial for identifying
mistakes. Fuzzing, a prevalent technique, involves modifying a seed input to discover software bugs. Selecting
the right seed is pivotal, as indicated by recent research. In our study, we extensively analyze leading gray-box
fuzzing tools, applying them to identify bugs across 22 open-source applications. An innovative addition to
our approach is the integration of a Deep Learning Generative Model (DCGAN). This model offers a novel
method for generating seed files by learning from crash files in previous experiments. Notably, it excels in
generating images across various formats, enhancing flexibility in applications with consistent input formats.
The system’s primary advantages lie in its flexibility and improved fuzzing efficiency. It outperforms other
applications in identifying vulnerabilities swiftly, marking a significant advancement in the current state of
affairs.
1 INTRODUCTION
Fuzzing is an automated testing technique that ex-
cels at finding vulnerabilities in applications by ex-
ploring edge cases and maximizing code coverage
(Oehlert, 2005). This surpasses manual testing and
is crucial for vulnerability discovery (e.g., Microsoft
Windows TIFF image vulnerability and Trend Micro
zero-day). Traditional fuzzing, however, has limita-
tions due to random data usage, hindering bug identi-
fication (Payer, 2019). Techniques like seed schedul-
ing (Choi et al., 2023) and Machine Learning (ML)
integration are being explored to improve coverage.
Here, an ML model trained on crash data can gener-
ate targeted seeds, leading to more effective bug dis-
covery. However, challenges remain, such as limited
performance analysis of existing grey-box fuzzers and
the lack of image-specific seed generation models.
Our contributions to this paper are:
i A comprehensive evaluation of four leading gray
box fuzzers (AFL++, AFLfast, AFLgo, and
Honggfuzz) across 22 diverse open-source ap-
plications, aiming to identify the most effective
fuzzer in various software landscapes.
ii Beyond crash counts, a deeper assessment of
fuzzer effectiveness based on crash discovery and
achieved code coverage, offering a holistic under-
standing of vulnerability exposure.
iii Introduction of a novel seed generation model
using Generative Adversarial Networks (GANs),
crafting highly relevant input seeds to maximize
code coverage and crash discovery.
iv Exploration of fuzzing frontiers by applying
GAN-generated inputs to image-based applica-
tions, showcasing the effectiveness of our ap-
proach in enhancing the security of image pro-
cessing software.
The paper is organized as follows: The next sec-
tion contains a detailed survey of fuzzing techniques,
ways to improve fuzzers, and all the recent research
on using machine learning models to generate seeds
and consequently improve results. Moving on, Sec-
tion III contains information about the technical setup
for the experiment, fuzzers, target applications, de-
tails of the DCGAN model used, and the evaluation
metrics. In the end Section IV shows the results and
the analysis of all the experiments performed and in
Section V the work is concluded and its future aspects
and direction are discussed.
2 RELATED WORKS
In recent years, there has been an increase in the num-
ber of methods for detecting vulnerabilities, and one
686
Toluchuri, S., Upadhyay, A., Naval, S., Laxmi, V. and Gaur, M.
GAN-based Seed Generation for Efficient Fuzzing.
DOI: 10.5220/0012761900003767
In Proceedings of the 21st International Conference on Security and Cryptography (SECRYPT 2024), pages 686-691
ISBN: 978-989-758-709-2; ISSN: 2184-7711
Copyright © 2024 by Paper published under CC license (CC BY-NC-ND 4.0)
of these methods is known as fuzzing. This has re-
sulted in the growth of a large number of fuzzers that
can be utilized for various targets, including the web,
the network, the application, and the kernel. As a re-
sult, there is a requirement for the development of bet-
ter methods for evaluating fuzzing.
Seed selection plays a crucial role in fuzzing ef-
fectiveness (Herrera et al., 2021). Saha et al. pro-
pose that generating seed inputs beyond the fuzzer’s
algorithm can improve coverage of rare paths (Saha
et al., 2022). Machine learning (Ramadan et al.,
2022, Saavedra et al., 2019, Wang et al., 2020b) and
deep learning (Miao et al., 2022, Li et al., 2022) tech-
niques have shown promise in seed generation. Cheng
et al. use an RNN-based generative model for PDF
files (Cheng et al., 2019), while Godefroid et al. em-
ploy a sequence-to-sequence model (Godefroid et al.,
2017). Wang et al.. leverage various deep neural net-
work models for seed generation (Wang et al., 2020a,
Wang et al., 2017). NeUFuzz (Wang et al., 2019) and
MTFuzz (She et al., 2020) utilize deep learning for in-
telligent seed selection, while SmartSeed (Lyu et al.,
2018) leverages a WGAN model for seed genera-
tion across multimedia formats. These advancements
highlight the growing adoption of machine learning
for improved seed generation and fuzzing effective-
ness.
3 EXPERIMENTAL SETUP
3.1 Workflow
Our workflow utilizes AFL++ for grey-box fuzzing
with a standard seed set (Figure 1). Crashes and novel
code paths serve as training data for a DCGAN model.
Images are pre-processed for model training, allow-
ing pattern recognition. The model generates new
images based on learned patterns. These images are
then used to fuzz the applications again. Finally, crash
triage analyzes crashes from both seed sets to identify
exploitable vulnerabilities and pinpoint relevant code
locations.
Note that the process can be a closed-loop. The
machine learning model can make seed files that can
be used by the fuzzing tools to find new crashes and
paths. Then, we can improve our machine learning
model’s training set by putting in files that cause new
crashes or paths.
In order to facilitate experimentation the fuzzers
have been installed and executed in Kali Linux 2022
64bit Virtual Machines running on MacOS 13.3, Win-
dows 11, and a Dell Inc. Desktop running Ubuntu
22.04.2 LTS.We have chosen four well-known gray-
no
yes
End
Fuzzing
Start
Crash Triage
Deep Convolutional
GAN Model
Generated
Seed Set
Regular
Input Seed
Fuzzing Tool
AFL++
crash?
Valuable
Input Seed
Application
Crash File
Stack Frame and Location
Figure 1: Workflow of the Experiment.
box and mutational fuzzers for our experiments and
tested them on the target application which are
AFLplusplus, AFLfast,AFLgo and Honggfuzz. All
the fuzzers have a Command Line User Interface
(CLI), they are installed and used from the terminal
only. Table 1 provides a comprehensive analysis of
each of the target applications. Our experiments are
based on both instrumented and non-instrumented ap-
plications.
3.2 Deep Learning Model
Generative Adversarial Networks
(GANs) (Goodfellow et al., 2020) are a type of
machine learning algorithm capable of generating
realistic data, including images, text, and music.
GANs consist of two competing models: a generator
and a discriminator. The generator creates new
data, while the discriminator distinguishes between
real and fake data (Jabbar et al., 2021). Through
adversarial training, these models improve iteratively.
Deep Convolutional GANs (DCGANs) (Radford
et al., 2015) specifically utilize convolutional layers
in both the generator and discriminator. They have
demonstrated effectiveness in generating high-quality
images of faces, objects, and scenes, as well as other
types of data like text and music. Despite being
still under development, GANs have the potential to
revolutionize data generation and interaction.
Training Images: Low crash yield in image appli-
cations led to seed set upscaling (Figure 2). A Gen-
erative Adversarial Network (GAN) was trained on
crashes and hangs from the initial fuzz run. Images
were pre-processed to 32x32 pixels for training (93
total).
Generated Images: Some of the images that were
created are displayed in Figure 3. After the GAN has
been trained, it can be used to generate new images
in the format and the same size as before, which is
32x32x3. There have been 500 of these images uti-
lized.
GAN-based Seed Generation for Efficient Fuzzing
687
Table 1: Application Details.
S
No.
Category Apps Version Year Input type
1
Text
Xpdf 3.02 2007 pdf
2 Xpdf 3.03 2011 pdf
3 Libxml2 2.9.4 2012 xml
4 Libxml2 2.9.5 2017 xml
5 Jsonparse NA NA json
6 Mupdf 1.22.0 2023 pdf
7 Bloomy Sunday NA 2016 txt
8
Image
Libpng 1.6.39 2022 png
9 Libtiff 4.0.4 2012 tiff
10 Libtiff 4.0.5 2015 tiff
11 Libexif 0.6.14 2002 tiff, jpg
12 Libexif 0.6.22 2020 tiff, jpg
13 imgp 2.8 2020 png, jpg
14 Flameshot 12.1.0 2022 png, jpg
15 ImageMagick 7.0.11 2021 png, tiff, jpg
16 GIMP 2.8.16 2020 png, jpg
17
Multimedia
VLC 3.0.7.1 2019 wmv
18 FFmpeg N-110105-
g9bf1848acf
2022 mov, mkv,
mp4
19 mpv 0.35.0 2022 mpv
20
Network
Wireshark 3.6.0 2021 pcap
21 Crazy HTTP Server NA 2020 pcap
22 tcpdump 4.9.2 2017 pcap
Figure 2: Images on which the GAN model is trained.
3.2.1 Evaluation Metrics
We have evaluated our experiments using the follow-
ing metrics.
1. No. of Crashes: Crashes are the visible signs of a
trigger, which is when the program stops running
and can be easily discovered. This metric monitors
these events. It always guesses fewer bugs than
there are.
2. No. of Hangs:AFL++ tracks unique hangs, which
indicate the fuzzer has encountered new execution
paths in the target program. These new paths can
Figure 3: Generated Images from the GAN Model.
expose potential bugs. More unique hangs suggest
a more effective fuzzer in finding program bugs.
3. Vulnerability Detection Speed (VDS): This met-
ric aims to estimate the speed for finding crashes
computed as the ratio of the number of crashes
found during a period of time (Equation 1).
V
b
=
No. o f crashes
No. o f hours
(1)
4. Density: Density (Equation 2) measures crashes
per fuzzing cycle. A lower density suggests the
fuzzer efficiently finds bugs with fewer inputs.
SECRYPT 2024 - 21st International Conference on Security and Cryptography
688
Density =
No. o f crashes
No. o f cycles
(2)
5. Edge Coverage: Edge coverage reflects fuzzing’s
exploration depth for bugs. While high coverage
doesn’t guarantee finding all bugs, low coverage
suggests limited bug-finding potential.
6. Mutation Rate: Mutation rate (Equation 3) refers
to the portion of inputs mutated per fuzzing itera-
tion. A higher rate generates more varied inputs,
potentially finding more bugs, but also risks an in-
crease in false positives.
M
r
=
∆Corpus count
Initial corpus count
(3)
4 ANALYSIS
After compiling all applications and defining input
seed sets, gray-box fuzzers AFL++, AFLfast, AFLgo,
and Honggfuzz were employed to fuzz the applica-
tions. AFL++ detected the highest number of crashes
in Libxml2, Libtiff, and Xpdf compared to AFLgo
and AFLfast (Figure 4). Additionally, AFL++ exhib-
ited superior Edge Coverage compared to the other
fuzzers (Figure 5).
0%
25%
50%
75%
100%
Libpng Libxml2 2.9.4 Libtiff 4.0.4 Xpdf
AFL++ AFLGo AFLfast
Figure 4: AFLplusplus finds the most files that will crash.
Nine out of 22 applications were fuzzed with-
out instrumentation (a separate fuzzer plug-in). This
mode produced more hangs than crashes. These files
can be used as seeds for further fuzzing (refer to
Figure 6 for application-specific hangs). Notably,
Bloomy Sunday and VLC media player exhibited the
most hangs.
Focusing on the most efficient fuzzer, AFL++, we
analyzed its impact on edge coverage, a key metric for
bug detection. AFL++ effectively explores new code
paths (edges) by mutating input seeds.
Figure 7 (a) shows that both Libtiff versions
achieved higher edge coverage and mutation rates
0
10
20
30
40
50
60
70
80
90
100
Libpng Libxml2 2.9.4 Libtiff 4.0.4 Xpdf 3.02
Edge Coverage
AFL++ AFLgo AFLfast Honggfuzz
Figure 5: Edge Coverage.
Non-instrumented Applications
No. of Hangs
0
200
400
600
Mupdf Bloomy
Sunday
imgp VLC FFmpeg mpv Wireshark
Figure 6: Trend of No. of Hangs in Non-instrumented Ap-
plications.
compared to other applications using standard seeds.
Interestingly, image processing applications gener-
ally outperformed text-based ones in edge coverage.
Fuzzing time is another important factor. Our exper-
iments (Figure 7 (b)) revealed a correlation between
fuzzing time and crashes - more time resulted in more
crashes. However, hangs were not significantly im-
pacted by fuzzing time. The complex nature of im-
age processing applications made them more suscep-
tible to vulnerabilities, with faster vulnerability detec-
tion rates compared to other categories (Figure 8 (a)).
Mutation rates remained consistent across categories
(Figure 8 (b)).
AFL++ excelled in edge coverage, mutation rate,
and vulnerability detection for image processing ap-
plications like Libtiff. We further enhanced these met-
rics using crash data-based seeds. This new seed set
significantly improved crashes, hangs, and detection
speed for Libtiff (Table 2) and Libpng (Table 3), while
Imgp showed benefits in hangs. Limited improve-
ments in other image applications suggest factors like
insufficient fuzzing time, seed inefficiency, or fuzzer
limitations might be at play.
Post-fuzzing, crash triage is crucial to identify ex-
GAN-based Seed Generation for Efficient Fuzzing
689
0
0.2
0.4
0.6
0.8
1
Libtiff 4.0.4
Libtiff 4.0.5
Flameshot
tcpump
ImageMagick
Libxml2 2.9.5
Libexif 0.6.14
Libxml2 2.9.4
Xpdf 3.02
Xpdf 3.03
Crazy HTTP Server
Jsonparse
Libexif 0.6.22
Edge Coverage Mutation Rate
Figure 7: (a) Comparison of Edge Coverage and Mutation Rate in Applications (b) Shows the relation between unique crashes
and hangs with respect to the hours spent fuzzing the application.
Applications
Vulnerability detection speed
0
50
100
150
200
250
Libtiff 4.0.4
Xpdf 3.02
tcpump
Xpdf 3.03
VLC
Bloomy Sunday
Libtiff 4.0.5
Libexif 0.6.14
Jsonparse
Applications
Mutation Rate
0.00
0.25
0.50
0.75
1.00
Libexif 0.6.22
Libexif 0.6.14
Libtiff 4.0.4
Jsonparse
Libtiff 4.0.5
ImageMagick
Xpdf 3.02
Xpdf 3.03
Libxml2 2.9.4
Libxml2 2.9.5
Figure 8: (a) Shows the VDS (V
b
) in the applications (b) Shows the Mutation Rate (M
r
) of the Applications.
Table 2: Results of fuzzing Libtiff with the new input seed
set.
With regular seed input With the generated input seed
Libtiff 4.0.4 Libtiff 4.0.5 Libtiff 4.0.4 Libtiff 4.0.5
No. of Crashes 2 74 479 490
No. of Hangs 0 9 76 14
VDS (V
b
) 0.04 143.89 8860 1943.75
Density 0 0 12305.55 8481.81
Table 3: Results of fuzzing Libpng with the new input seed
set.
With the regular seed input With the generated seed input
Libpng Libpng
No. of Hangs 0 1
Edge Coverage 0% 6%
Mutation Rate 0% 29%
ploitable crashes. Not all crashes lead to vulnerabil-
ities. Our analysis revealed two main crash types in
the standard seed set: segmentation faults and buffer
overflows (Table 4). Segmentation faults often indi-
cate memory access issues and can stem from leaks,
out-of-bounds access, or uninitialized pointers. Crash
triage is focused on these crashes, with the remain-
der discarded. Libtiff 4.0.5 crashes with the standard
seed set revealed no exploitable vulnerabilities. These
crashes stemmed from a known heap buffer overflow
in tif print.c (Lhee and Chapin, 2003). The new seed
set, however, identified vulnerabilities in both Libtiff
4.0.5 and 4.0.4 (Table 5), demonstrating the model’s
effectiveness in enhancing fuzzing.
Table 4: Crash triage results with the regular seed input.
Application Error Method Used
Xpdf 3.03 Segmentation Fault AFLTriage
Xpdf 3.02 Segmentation Fault AFLTriage
Libtiff 4.0.5 None AFLTriage
Libtiff 4.0.4 None AFLTriage
Libexif 0.6.14 Segmentation Fault, Signal Abort AFLTriage
tcpdump Heap Buffer Overflow Used the crash file as an input
Libttiff 4.0.4 Heap Buffer Overflow Used the crash file as an input
Table 5: Crash triage results with the generated input seed.
Application Error Method Used
Libtiff 4.0.5 Heap Buffer Overflow AFLTriage + Used the crash file as an input
Libtiff 4.0.4 Heap Buffer Overflow AFLTriage
Crash analysis revealed a heap buffer overflow in
the tif print.c function’s fprintf() call. fprintf() was
allocated one byte, but attempted to read a second
character, causing the overflow. The format string
%s likely expected a null-terminated string, but po-
tentially encountered an unterminated pointer.
SECRYPT 2024 - 21st International Conference on Security and Cryptography
690
5 FUTURE WORK AND
CONCLUSIONS
This paper offers a thorough analysis of fuzzing, iden-
tifying AFL++ as the most effective gray-box fuzzer
compared to AFLfast, AFLgo, and Honggfuzz. It
covers fuzzing history, techniques, application instru-
mentation, and crash triage. AFL++ yields positive
results across various applications, with image format
crashes used to train a Deep Convolutional Generative
Adversarial Network for generating a new seed set.
This new seed set enhances fuzzing metrics and un-
covers critical vulnerabilities. Future research could
involve designing a seed generation model compati-
ble with additional fuzzers and incorporating all rele-
vant file formats.
REFERENCES
Cheng, L., Zhang, Y., Zhang, Y., Wu, C., Li, Z., Fu, Y., and
Li, H. (2019). Optimizing seed inputs in fuzzing with
machine learning. In 2019 IEEE/ACM 41st Interna-
tional Conference on Software Engineering: Compan-
ion Proceedings (ICSE-Companion), pages 244–245.
IEEE.
Choi, G., Jeon, S., Cho, J., and Moon, J. (2023). A seed
scheduling method with a reinforcement learning for
a coverage guided fuzzing. IEEE Access, 11:2048–
2057.
Godefroid, P., Peleg, H., and Singh, R. (2017). Learn&fuzz:
Machine learning for input fuzzing. In 2017 32nd
IEEE/ACM International Conference on Automated
Software Engineering (ASE), pages 50–59. IEEE.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B.,
Warde-Farley, D., Ozair, S., Courville, A., and Ben-
gio, Y. (2020). Generative adversarial networks. Com-
munications of the ACM, 63(11):139–144.
Herrera, A., Gunadi, H., Magrath, S., Norrish, M., Payer,
M., and Hosking, A. L. (2021). Seed selection for
successful fuzzing. In Proceedings of the 30th ACM
SIGSOFT International Symposium on Software Test-
ing and Analysis, pages 230–243.
Jabbar, A., Li, X., and Omar, B. (2021). A survey on gener-
ative adversarial networks: Variants, applications, and
training. ACM Computing Surveys (CSUR), 54(8):1–
49.
Lhee, K.-S. and Chapin, S. J. (2003). Buffer overflow
and format string overflow vulnerabilities. Software:
practice and experience, 33(5):423–460.
Li, S., Xie, X., Lin, Y., Li, Y., Feng, R., Li, X., Ge, W.,
and Dong, J. S. (2022). Deep learning for coverage-
guided fuzzing: How far are we? IEEE Transactions
on Dependable and Secure Computing.
Lyu, C., Ji, S., Li, Y., Zhou, J., Chen, J., and Chen, J.
(2018). Smartseed: Smart seed generation for effi-
cient fuzzing. arXiv preprint arXiv:1807.02606.
Miao, S., Wang, J., Zhang, C., Lin, Z., Gong, J., Zhang, X.,
et al. (2022). Deep learning in fuzzing: A literature
survey. In 2022 IEEE 2nd International Conference
on Electronic Technology, Communication and Infor-
mation (ICETCI), pages 220–223. IEEE.
Oehlert, P. (2005). Violating assumptions with fuzzing.
IEEE Security & Privacy, 3(2):58–62.
Payer, M. (2019). The fuzzing hype-train: How random
testing triggers thousands of crashes. IEEE Security
& Privacy, 17(1):78–82.
Radford, A., Metz, L., and Chintala, S. (2015). Unsu-
pervised representation learning with deep convolu-
tional generative adversarial networks. arXiv preprint
arXiv:1511.06434.
Ramadan, A. E.-R. K. E.-D., Bahaa, A., and Ghoneim,
A. (2022). A systematic literature review on soft-
ware vulnerability detection using machine learning
approaches. FCI-H Informatics Bulletin, 4(1):1–9.
Saavedra, G. J., Rodhouse, K. N., Dunlavy, D. M., and
Kegelmeyer, P. W. (2019). A review of machine
learning applications in fuzzing. arXiv preprint
arXiv:1906.11133.
Saha, S., Sarker, L., Shafiuzzaman, M., Shou, C., Li, A.,
Sankaran, G., and Bultan, T. (2022). Rare-seed gener-
ation for fuzzing. arXiv preprint arXiv:2212.09004.
She, D., Krishna, R., Yan, L., Jana, S., and Ray, B. (2020).
Mtfuzz: fuzzing with a multi-task neural network. In
Proceedings of the 28th ACM joint meeting on Euro-
pean software engineering conference and symposium
on the foundations of software engineering, pages
737–749.
Wang, J., Chen, B., Wei, L., and Liu, Y. (2017). Sky-
fire: Data-driven seed generation for fuzzing. In 2017
IEEE Symposium on Security and Privacy (SP), pages
579–594. IEEE.
Wang, X., Hu, C., Ma, R., Li, B., and Wang, X. (2020a).
Lafuzz: neural network for efficient fuzzing. In 2020
IEEE 32nd International Conference on Tools with
Artificial Intelligence (ICTAI), pages 603–611. IEEE.
Wang, Y., Jia, P., Liu, L., Huang, C., and Liu, Z. (2020b). A
systematic review of fuzzing based on machine learn-
ing techniques. PloS one, 15(8):e0237749.
Wang, Y., Wu, Z., Wei, Q., and Wang, Q. (2019). Neufuzz:
Efficient fuzzing with deep neural network. IEEE Ac-
cess, 7:36340–36352.
GAN-based Seed Generation for Efficient Fuzzing
691