syzkaller VS gvisor

https://arxiv.org/abs/2402.09171 :

> This paper describes Meta's TestGen-LLM tool, which uses LLMs to automatically improve existing human-written tests. TestGen-LLM verifies that its generated test classes successfully clear a set of filters that assure measurable improvement over the original test suite, thereby eliminating problems due to LLM hallucination. [...] We believe this is the first report on industrial scale deployment of LLM-generated code backed by such assurances of code improvement.

Coverage-guided unit test improvement might [with LLMs] be efficient too.

https://github.com/topics/coverage-guided-fuzzing :

- e.g. Google/syzkaller is a coverage-guided syscall fuzzer: https://github.com/google/syzkaller

- Gitlab CI supports coverage-guided fuzzing: https://docs.gitlab.com/ee/user/application_security/coverag...

- oss-fuzz, osv

Additional ways to improve tests:

Hypothesis and pynguin generate tests from type annotations.

There are various tools to generate type annotations for Python code;

> pytype (Google) [1], PyAnnotate (Dropbox) [2], and MonkeyType (Instagram) [3] all do dynamic / runtime PEP-484 type annotation type inference [4] to generate type annotations. https://news.ycombinator.com/item?id=39139198

icontract-hypothesis generates tests from icontract DbC Design by Contract type, value, and invariance constraints specified as precondition and postcondition @decorators:

https://google.github.io/clusterfuzz/setting-up-fuzzing/libf...

> OSS-Fuzz runs CloudFuzz[Lite?] for many open source repos and feeds OSV OpenSSF Vulnerability Format: https://github.com/google/osv#current-data-sources

.

Google/syzkaller https://github.com/google/syzkaller :

>> syzkaller is an unsupervised coverage-guided kernel fuzzer. Supported OSes: Akaros, FreeBSD, Fuchsia, gVisor, Linux, NetBSD, OpenBSD, Windows

.

ghidra-patchdiff-correlator:

The key to modern fuzzing is feedback, usually some kind of coverage testing of the program under test. This allows the fuzzer to be much smarter about how it finds new code paths, and makes fuzzing find bugs a lot quicker.

Google have a project to do fuzzing on Linux system calls using coverage feedback: https://github.com/google/syzkaller

Utilities that try to exercise ("fuzz") an interface with the intent of discovering bugs are called "fuzzers". The tool that comes to mind is syzkaller.

It's true that USB is probably a less desirable attack surface than modems, because it actually requires the user to physically connect their device to a malicious device, but I wouldn't discount it as impractical and unlikely to happen in the wild. There's a reason some of the more famous malware and spyware used to spread/attack over USB. Google actually does USB driver fuzzing and the amount of potentially devastating vulnerabilities is staggering.

Isn't gVisor kind of this as well?

"gVisor is an application kernel for containers. It limits the host kernel surface accessible to the application while still giving the application access to all the features it expects. Unlike most kernels, gVisor does not assume or require a fixed set of physical resources; instead, it leverages existing host kernel functionality and runs as a normal process. In other words, gVisor implements Linux by way of Linux."

https://github.com/google/gvisor

This sort of feels like seeing someone riding a bike and saying: why don’t they just get a car? The simple fact is that containers and VMs are quite different. Whether something uses VMX and friends or not is also a red herring, as gVisor also “rolls it own VMM” [1].

[1] https://github.com/google/gvisor/tree/master/pkg/sentry/plat...

There's two major production-ready Go-based operating system(-ish) projects:

- Google's gVisor[1] (a re-implementation of a significant subset of the Linux syscall ABI for isolation, also mentioned in the article)

- USBArmory's Tamago[2] (a single-threaded bare-metal Go runtime for SOCs)

Both of these are security-focused with a clear trade off: sacrifice some performance for memory safe and excellent readability (and auditability). I feel like that's the sweet spot for low-level Go - projects that need memory safety but would rather trade some performance for simplicity.

[1]: https://github.com/google/gvisor

[2]: https://github.com/usbarmory/tamago

It uses gVisor to create a TCP/IP stack in userspace, and starts a wireguard interface on it, which the HTTP server from http.Serve listens on. The library will print a URL after startup, where you can access your server. You can create multiple listeners in one binary.

The playground compiles the program with GOOS=linux, GOARCH=amd64 and runs the program with gVisor. Detailed documentation is available at the gVisor site.

You could use a container sandbox like gVisor, light virtual machines as containers (Kata containers, firecracker + containerd) or full virtual machines (virtlet as a CRI).