syzkaller
AlphaCodium
syzkaller | AlphaCodium | |
---|---|---|
7 | 12 | |
5,166 | 3,194 | |
0.8% | 4.5% | |
9.8 | 8.4 | |
5 days ago | 17 days ago | |
Go | Python | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
syzkaller
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Automated Unit Test Improvement Using Large Language Models at Meta
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:
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Differ: Tool for testing and validating transformed programs
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
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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
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ghidra-patchdiff-correlator:
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Fuzz Testing Is the Best Thing to Happen to Our Application Tests
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
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Is there a Linux user-space program that causes execution through every kernel function path and context?
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.
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Those scary warnings of juice jacking in airports and hotels? They’re nonsense
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.
- Linux System Call Table – Chromiumos
- Audit of Linux kernel code
AlphaCodium
- The Next Generation of Claude (Claude 3)
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Tools that Make Me Productive as a Software Engineer
Other AI tools like Copilot and Codium.ai integrate well with popular editors and IDEs, such as VSCode, enhancing your skills and productivity.
- Code Generation with AlphaCodium
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Automated Unit Test Improvement Using Large Language Models at Meta
Thanks for sharing this. By far the best tool I've seen in the market centered around Code Integrity is CodiumAI (https://www.codium.ai/). They generate unit test based on entire code repos. Also integrates into SDLC through a PR Agent on GitHub or GitLab. My whole team uses them.
- Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
What are some alternatives?
AFLplusplus - The fuzzer afl++ is afl with community patches, qemu 5.1 upgrade, collision-free coverage, enhanced laf-intel & redqueen, AFLfast++ power schedules, MOpt mutators, unicorn_mode, and a lot more!
tunnelmole-client - Tunnelmole - Connect to local servers from anywhere
vuls - Agent-less vulnerability scanner for Linux, FreeBSD, Container, WordPress, Programming language libraries, Network devices
fuzz-introspector - Fuzz Introspector -- introspect, extend and optimise fuzzers
wtf - wtf is a distributed, code-coverage guided, customizable, cross-platform snapshot-based fuzzer designed for attacking user and / or kernel-mode targets running on Microsoft Windows and Linux user-mode (experimental!).
icontract-hypothesis - Combine contracts and automatic testing.
ipa-medit - Memory modification tool for re-signed ipa supports iOS apps running on iPhone and Apple Silicon Mac without jailbreaking.
gvisor - Application Kernel for Containers
xpid - Linux Process Discovery. C Library, Go bindings, Runtime.
cfuzzer - url-fuzzer
clusterfuzzlite - ClusterFuzzLite - Simple continuous fuzzing that runs in CI.
Fuzzing101 - An step by step fuzzing tutorial. A GitHub Security Lab initiative