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decompiler-explorer
Decompiler Explorer! Compare tools on the forefront of static analysis, now in your web browser!
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Can I just say, thanks to the person who posted this for waiting until this week to do so. (Side note: I suspect it was due to the recent coverage from C++ Weekly which is a great resource: https://www.youtube.com/watch?v=h3F0Fw0R7ME)
As recently as last week we had some horrible performance problems but it looks like the queue (https://dogbolt.org/queue) is mostly still fine! Other than the long pole of a few of the decompilers being backed up, things are humming along quite smoothly! Josh + Glenn have done some great work on it! (https://github.com/decompiler-explorer/decompiler-explorer/c...)
Look no further than https://dogbolt.org/faq
> It's meant to be the reverse of the amazing Compiler Explorer.
With a link to https://godbolt.org/
It’s very obvious that Dogbolt Decompiler Explorer is primarily named after Godbolt Compiler Explorer.
Binary Ninja likewise is empty and keeps up just fine as well. It's not a coincidence that the two commercial products that are funding it are both confident enough to put their stuff online like this.
And it's no conspiracy theory or intentional sandbagging, you can see the implementation: https://github.com/decompiler-explorer/decompiler-explorer
and if anyone can improve the other tools performance we'd be happy to accept it. We reached out to the Ghidra devs: https://github.com/NationalSecurityAgency/ghidra/issues/5228 but they didn't have any silver bullets for us either.
Short answer: not where it counts.
My work focuses on recognizing known functions in obfuscated binaries, but there are some papers you might want to check out related to deobfuscation, if not necessarily using ML for deobfuscation or decompilation.
My take is that ML can soundly defeat the "easy" and more static obfuscation types (encodings, control flow flattening, splitting functions). It's low hanging fruit, and it's what I worked on most, but adoption is slow. On the other hand, "hard" obfuscations like virtualized functions or programs which embed JIT compilers to obfuscate at runtime... as far as I know, those are still unsolved problems.
This is a good overview of the subject, but pretty old and doesn't cover "hard" obfuscations: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1566145.
https://www.jinyier.me/papers/DATE19_Obf.pdf uses deobfuscation for RTL logic (FGPA/ASIC domain) with SAT solvers. Might be useful for a point of view from a fairly different domain.
https://advising.cs.arizona.edu/~debray/Publications/generic... uses "semantics-preserving transformations" to shed obfuscation. I think this approach is the way to go, especially when combined with dynamic/symbolic analysis to mitigate virt/jit types of transformations.
I'll mention this one as a cautionary tale: https://dl.acm.org/doi/pdf/10.1145/2886012 has some good general info but glosses over the machine learning approach. It considers Hex-rays' FLIRT to be "machine learning", but FLIRT just hashes signatures, can be spoofed (i.e. https://siliconpr0n.org/uv/issues_with_flirt_aware_malware.p...), and is useless against obfuscation.
Eventually I think SBOM tools like Black Duck[1] and SLSA[2] will incorporate ML to improve the accuracy of even figuring out what dependencies a piece of software actually has.
[1]: https://www.synopsys.com/software-integrity/software-composi...
[2]: https://slsa.dev/