glog VS slsa

I started a poll on r/golang with these four candidates, but also came to know about glog which was a go port of a C++ project by Google. I used that option in the poll conducted on LinkedIn.

Other than print and formatted print to stdout and stderr, what more do you need? I adapted much of the glog rationale into a logging wrapper. Allowing many thousands of unneeded lines from Logrus to be avoided. https://github.com/golang/glog

check out glog, it supports flags out out the box

By globals, I mean global resources outside of the codes namespace. It may not even be a resource in the process, such as a log file or temporary directory or a database. If you have two versions of a crate in their completely separate worlds, and call both of their init_logging() functions to log to a file specified by an environment variable, things are likely to go pear shaped when they stomp over each others log file.

I'm a Rust novice, but the example I tripped over in Go was https://github.com/golang/glog. It has a module level init() initialization routine that makes calls to the stdlib flags package, manipulating the default command line flags (a global resource). If you ended up with multiple versions of glog via transient dependencies, your program would panic on startup as the second version's init() would make calls only allowed to be called once. Rust thankfully avoids this particular one by requiring initialization to be called by main() (apart from the hack described in the article).

There is no better way than looking at your older brothers and learning from them: stdlib log, glog, logrus, zerolog, log15 (eth fork)...

Also glog is the name of Google logging library which is confusing. https://github.com/golang/glog

the 5th hit looks like something made 6-8 yrs ago which would have worked: https://github.com/golang/glog

Read similar repos, compare, learn: https://github.com/rs/zerolog https://christine.website/blog/ln-the-natural-logger-2020-10-17 https://github.com/sirupsen/logrus https://github.com/golang/glog https://github.com/nikandfor/tlog (this one is mine)

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/

The dependency chain is certified! SLSA!

The things you mentioned are not solved by a typical "SBOM" but e.g. CycloneDX has extra fields to record provenance and pedigree and things like in-toto (https://in-toto.io/) or SLSA (https://slsa.dev/) also aim to work in this field.

I've spent the last six months in this field and people will tell you that this or that is an industry best practice or "a standard" but in my experience none of that is true. Everyone is still trying to figure out how best to protect the software supply chain security and things are still very much in flux.

It's multi-pronged and I imagine adopters may use a subset of features. Broadly, I think folks are going to be interested in a) branch/tag/reference protection rules, b) file protection rules (monorepo or otherwise, though monorepos do pose a very apt usecase for gittuf), and c) general key management for those who primarily care about Git signing.

For those who care about a and b, I think the work we want to do to support [in-toto attestations](https://github.com/in-toto/attestation) for [SLSA's upcoming source track](https://github.com/slsa-framework/slsa/issues/956) could be very interesting as well.

It doesn't have to be. Corporations which are FedRAMP[1] compliant, have to build software reproducibly in a fully isolated environment, only from reviewed code.[2]

[1] https://en.wikipedia.org/wiki/FedRAMP

[2] https://slsa.dev/

SLSA stands for Supply chain Levels for Software Artifacts, and it is a framework that aims to provide a set of best practices for the software supply chain, with a focus on OSS. It was created by Google, and it is now part of the OpenSSF. It consists of four levels of assurance, from Level 1 to Level 4, that correspond to different degrees of protection against supply chain attacks. Our CTO Paolo Mainardi mentioned SLSA in a very good article on software supply chain security, and we also mentioned it in another article about securing OCI Artifacts on Kubernetes.

SLSA.dev