Cornelius Aschermann (Ruhr-Universität Bochum), Sergej Schumilo (Ruhr-Universität Bochum), Tim Blazytko (Ruhr-Universität Bochum), Robert Gawlik (Ruhr-Universität Bochum), Thorsten Holz (Ruhr-Universität Bochum)

Automated software testing based on fuzzing has experienced a revival in recent years. Especially feedback-driven fuzzing has become well-known for its ability to efficiently perform randomized testing with limited input corpora.
Despite a lot of progress, two common problems are magic numbers and (nested) checksums. Computationally expensive methods such as taint tracking and symbolic execution are typically used to overcome such roadblocks. Unfortunately, such methods often require access to source code, a rather precise description of the environment (e.g., behavior of library calls or the underlying OS), or the exact semantics of the platform's instruction set.

In this paper, we introduce a lightweight, yet very effective alternative to taint tracking and symbolic execution to facilitate and optimize state-of-the-art feedback fuzzing that easily scales to large binary applications and unknown environments.
We observe that during the execution of a given program, parts of the input often end up directly (i.e., nearly unmodified) in the program state. This input-to-state correspondence
can be exploited to create a robust method to
overcome common fuzzing roadblocks in a highly effective and efficient manner.
Our prototype implementation, called REDQUEEN, is able to solve magic bytes and (nested) checksum tests automatically for a given binary executable.
Additionally, we show that our techniques outperform various
state-of-the-art tools on a wide variety of targets across different privilege levels (kernel-space and userland) with no platform-specific code.
REDQUEEN is the first method to find more than 100% of the bugs planted in LAVA-M across all targets. Furthermore, we were able to discover 65 new bugs and obtained 16 CVEs in multiple programs and OS kernel drivers. Finally, our evaluation demonstrates that REDQUEEN is fast, widely applicable and outperforms concurrent approaches by up to three orders of magnitude.

View More Papers

maTLS: How to Make TLS middlebox-aware?

Hyunwoo Lee (Seoul National University), Zach Smith (University of Luxembourg), Junghwan Lim (Seoul National University), Gyeongjae Choi (Seoul National University), Selin Chun (Seoul National University), Taejoong Chung (Rochester Institute of Technology), Ted "Taekyoung" Kwon (Seoul National University)

Read More

One Engine To Serve 'em All: Inferring Taint Rules...

Zheng Leong Chua (National University of Singapore), Yanhao Wang (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences), Teodora Baluta (National University of Singapore), Prateek Saxena (National University of Singapore), Zhenkai Liang (National University of Singapore), Purui Su (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences)

Read More

Balancing Image Privacy and Usability with Thumbnail-Preserving Encryption

Kimia Tajik (Oregon State University), Akshith Gunasekaran (Oregon State University), Rhea Dutta (Cornell University), Brandon Ellis (Oregon State University), Rakesh B. Bobba (Oregon State University), Mike Rosulek (Oregon State University), Charles V. Wright (Portland State University), Wu-Chi Feng (Portland State University)

Read More

Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic...

Lea Schönherr (Ruhr University Bochum), Katharina Kohls (Ruhr University Bochum), Steffen Zeiler (Ruhr University Bochum), Thorsten Holz (Ruhr University Bochum), Dorothea Kolossa (Ruhr University Bochum)

Read More