Right before the holidays, I, along with my co-authors of the journal article The Art, Science, and Engineering of Fuzzing: A Survey, received an early holiday present!

Congratulations!

On behalf of Vice President for Publications, David Ebert, I am writing to inform you that your paper, "The Art, Science, and Engineering of Fuzzing: A Survey," has been awarded the 2021 Best Paper Award from IEEE Transactions on Software Engineering by the IEEE Computer Society Publications Board.

This was quite unexpected, as our article was accepted back in 2019 -- four years ago! But it only "appeared" in the November 2021 editions of the journal.

You can access this article here or, as always, on my publications page.

It's been an exciting year so far. I'm happy to announce that two papers I co-authored received awards. Congratulations to the students who did all the heavy lifting -- Jeremy, Qibin, and Alex!

Distinguished Paper Award: Augmenting Decompiler Output with Learned Variable Names and Types

Qibin Chen, Jeremy Lacomis, Edward J. Schwartz, Claire Le Goues, Graham Neubig, and Bogdan Vasilescu. Augmenting Decompiler Output with Learned Variable Names and Types, (PDF) Proceedings of the 2022 USENIX Security Symposium. Received distinguished paper award.

This paper follows up on some of our earlier work in which we show how to improve decompiler. Decompiler output is often substantially more readable compared to the lower-level alternative of reading disassembly code. But decompiler output still has a lot of shortcomings when it comes to information that is removed during the compilation process, such as variable names and type information. In our previous work, we showed that it is possible to recover meaningful variable names by learning appropriate variable names based on the context of the surrounding code.

In the new paper, Jeremy, Qibin and my coauthors explored whether it is also possible to recover high-level types via learning. There is a rich history of binary analysis work in the area of type inference, but this work generally focuses on syntactic types, such as struct {float; float}. These type inference algorithms are generally already built into decompilers. In our paper, we try to recover semantic types, such as struct {float x; float y} point which includes the type and field names, which are more valuable to a reverse engineer. It turns out that we can recover semantic types even more accurately than variable names. This is in part because types are constrained by the way in which they are used. For example, an int can't be confused with a char because they are different sizes.

Best Paper Award: Learning to Superoptimize Real-world Programs

Alex Shypula, Pengcheng Yin, Jeremy Lacomis, Claire Le Goues, Edward Schwartz, and Graham Neubig. Learning to Superoptimize Real-world Programs, (Arxiv) (PDF) Proceedings of the 2022 Deep Learning for Code Workshop at the International Conference on Learning Representations. Received best paper award.

In this paper, Alex and our co-authors investigate whether neural models are able to learn and improve on optimizations at the assembly code level by looking at unoptimized and optimized code pairings that are generated from an optimizing compiler. The short answer is that they can, and by employing reinforcement learning on top, can learn to outperform an optimizing compiler in some cases! Superoptimization is an interesting problem in its own right, but what really excites me about this paper is it demonstrates that neural models can learn very complex optimizations such as register allocation just by looking at the textual representation of assembly code. The optimizations the model can perform clearly indicate that the model is learning a substantial portion of x86 assembly code semantics merely by looking at examples. To me, this clearly signals that, with the right data, neural models are likely able to solve many binary analysis problems. I look forward to future work in which we combine traditional binary analysis techniques, such as explicit semantic decodings of instructions, with neural learning.

I'm happy to announce that a paper written with my colleagues, A Generic Technique for Automatically Finding Defense-Aware Code Reuse Attacks, will be published at ACM CCS 2020. This paper is based on some ideas I had while I was finishing my degree that I did not have time to finish. Fortunately, I was able to find time to work on it again at SEI, and this paper is the result. A pre-publication copy is available from the publications page.

Variable Recovery

As far back as I can remember, one of the accepted dogmas of reverse engineering is that when a program is compiled, some information about the program is lost, and there is no way to recover it. Variable names are one of the most frequently cited casualties of this idea. In fact, this argument is used by countless authors in the introductions of their papers to explain some of the unique challenges that binary analysis has compared to source analysis.

You can imagine how shocking (and cool!) it was then when my colleagues and I found that it's possible to recover a large percentage of variable names. Our key insight is that the semantics of the binary code that accesses a variable is actually a surprisingly good signal for what the variable was named. In other words, we took a huge amount of source code from github, compiled it, and then decompiled it. We then trained a model to predict the variable names, which we can't see in an executable, based on the way that the code accesses those variables, which we can see in an executable. In Meaningful Variable Names for Decompiled Code: A Machine Translation Approach, we showed that this was possible using Statistical Machine Translation, which is one of the techniques used to translate between natural languages.

I'm happy to announce that our latest paper on this subject, DIRE: A Neural Approach to Decompiled Identifier Renaming, was accepted to ASE 2019. In this paper, we found that neural network models work really well for recovering variable names in decompiled code. I'll post our camera ready as soon as its finished.

Code Reuse

I'll be speaking at the 2019 Central Pennsylvania Open Source Conference (CPOSC) in September. I attended CPOSC for the first time last year, and was very impressed by the quality of the talks. The name is a little misleading; talks are not necessarily related to open source software. I'll actually be giving a primer on code reuse attacks.