hydra VS slsa

See https://github.com/NixOS/hydra/issues/838 for making content-addressed derivations supported by hydra.nixos.org. At that point, we can actually try out the XP feature at scale.

Also see https://github.com/NixOS/nix/issues/8919 for this accepted RFC

Once those things are done, we can get back to merging in the IPFS code.

Now that there is an Nix team and I am on it, there is much, much less of an issue of these experiments being caught in limbo :).

https://github.com/nix-community/infra, Community project builds https://github.com/NixOS/hydra, NixOS build server

Nix is pretty cool, and I would say comparisons to Earthly are apt. I may tackle that in a follow-up. If you did a monorepo setup written in nix and then used something like Hydra for building, it might be a pretty nice solution.

Nix seems great for build servers. This is a great introduction to the motivations behind it.

I'm not sold on using it for managing developer environments (another use case it is often used for). It "solves" the problem that developers might be using different versions of libraries or compilers on their machines... but it comes at the cost of having to learn a whole new programming language, a configuration language, a whole new jargon, and workflow. It's a bit like using Docker as a development environment. It introduces a non-trivial amount of friction.

Some folks get excited about package management and configuration. Personally I don't care for it enough to over-come such a high learning curve. And I don't particularly like the workflow it enforces.

However it is pretty great for reproducible CI/CD systems like Hydra: https://github.com/NixOS/hydra

Somewhat related to this, is there a good way to install something from a flake inside the configuration.nix? For example, the hydra flake, since it includes many derivations for dependencies that are not part of nixpkgs (or are at the wrong versions).

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