onefuzz
American Fuzzy Lop
onefuzz | American Fuzzy Lop | |
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4 | 21 | |
2,780 | 2,903 | |
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0.0 | 0.0 | |
6 months ago | almost 3 years ago | |
C# | C | |
MIT License | Apache License 2.0 |
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onefuzz
- Microsoft OneFuzz to Be Archived
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Ask HN: What are some worthy non-cryto uses of excess home compute nowadays?
Learning how to is half the fun!
There's a bunch of good tutorials out there on [dumb] fuzzing (presumably where you'll start). One starting point I'd recommend is taking a binary that accepts input from stdin and making some proof-of-concepts with AFL (https://lcamtuf.coredump.cx/afl/).
If you'd rather start from a code/library perspective (and not CLI), I'd recommend libfuzzer (https://github.com/Dor1s/libfuzzer-workshop/).
There's a lot of other fuzzers, techniques, and depth to the field, but I'd recommend inch worming through (speed up as you gain more comfort). The Fuzzing Book is good to help you understand the logic behind techniques and strategies (https://www.fuzzingbook.org/)
As for some management, there's a few decent "monitoring" systems out there; personally I just SSH in and check the fuzzer manually (I leave it running in a tmux pane), but if that's not your cup of tea I've heard good things about OneFuzz (https://github.com/microsoft/onefuzz) and LuckyCat (https://github.com/fkie-cad/LuckyCAT).
Happy to answer any specifics of the sort :)
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What Is Fuzz Testing?
Microsoft’s OneFuzz is tackling some of these issues
https://github.com/microsoft/onefuzz
The biggest problem with fuzzing when it comes to “developer friendliness” isn’t just how to setup the fuzzer and the fact that you need to often write quite a bit of additional code to support fuzzing but that the results aren’t easily consumable.
Getting a fuzzer to cause a crash or some unhandled exception isn’t particularly difficult understanding the actual implication of such crash is where these tools “fail”.
SAST / DAST tools with all their issues such as false positives and relatively limited coverage at least provide actionable results.
Fuzzing not only requires a much higher understanding of the code itself and of its execution but the results are often useless for many developers.
Basically it doesn’t help you breach the gap between seeing a BSOD or a kernel panic and getting a working zero day.
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Rnetsecs Q1 2021 Information Security Hiring
To get a taste of our work, a few of the projects our group published recently: * Freta, a project to democratize full system memory forensics with trusted sensorsfor the cloud. * OneFuzz, a self hosted fuzzing as a service platform, used to scale fuzzing for multiple teams within Microsoft including Windows. * RESTler, the first stateful REST api fuzzer * RAFT, a self-hosted API testing orchestration engine, enabling developers to use RESTler and other api scanning & fuzzing tools in their CICD pipelines.
American Fuzzy Lop
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Prefer table driven tests (2019)
There's some efforts to guide test generation for property based testing to make the instruction pointer explore as large a space as possible.
This effort is more mature in the fuzzing community. See eg American Fuzzy Lop https://github.com/google/AFL
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C++ Faker library
What you're describing, just generating random input to test a program, is sometimes called "blind fuzzing" but the state-of-the-art is far beyond that. Maybe try reading through the documentation of e.g. https://github.com/google/AFL to see what a fuzzer does and why just producing random input isn't even scratching the surface.
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Hyperpom: An Apple Silicon Fuzzer for 64-bit ARM Binaries
for general riscv I used to use this https://github.com/google/AFL I dont know if it supports x64 tho.
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How to fuzz java code with jazzar?
Ex ( AFL, WinAFL, HonggFuzz, LibFuzzer, Jazzer )
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One year ago I wrote a buddy memory allocator - project update
I wrote this little fuzz test target in order to fuzz it with afl (under ASan and UBSan):
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Beariish/little: A small, easily embedded language implemented in a single .c file
afl, which is trivial to apply to this program:
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TCL like interpreter suitable for embedded use
I made my own version of a TCL interpreter (well, a very TCL like langauge) derived from "picol" available at https://github.com/howerj/pickle. There are many different re-implementations and derivatives of this interpreter but they all seem very "crashy", this one has been significantly hardened by using a fuzzer on it which ran for months called American Fuzzy Lop https://lcamtuf.coredump.cx/afl/ . It is also more suitable for embedded use whilst still not having arbitrary restrictions like many other implementations.
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What's in your tool belt?
On Linux afl is a very powerful bug-finding tool, and it's a great companion when doing code review. Composes well with ASan and UBSan.
- Afl - American fuzzy lop - a security-oriented fuzzer
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Difficulty of CSCA48 compared to other first year cs/math courses
b-, https://lcamtuf.coredump.cx/afl/
What are some alternatives?
radamsa
boofuzz - A fork and successor of the Sulley Fuzzing Framework
cryptofuzz - Fuzzing cryptographic libraries. Magic bug printer go brrrr.
honggfuzz - Security oriented software fuzzer. Supports evolutionary, feedback-driven fuzzing based on code coverage (SW and HW based)
LuckyCAT - A distributed fuzzing management framework
Cppcheck - static analysis of C/C++ code
beacon-fuzz - Differential Fuzzer for Ethereum 2.0
HTTP Parser - http request/response parser for c
fishnet - Distributed Stockfish analysis for lichess.org
PHP CPP - Library to build PHP extensions with C++
doubleback - Doubleback provides round-trip parsing and printing of 64-bit double-precision floating-point numbers using the Ryu algorithm implemented in multiple programming languages. Doubleback is biased towards "human-friendly" output which round-trips consistently between binary and decimal.
ZXing - ZXing ("Zebra Crossing") barcode scanning library for Java, Android