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AFL
C2019–2024
google / AFL · Archived

AFL

american fuzzy lop - a security-oriented fuzzer

10Distinct people
10Mentions
0Explicit recommenders
4.2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

american fuzzy lop - a security-oriented fuzzer

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year10 dated mentions

Observed mentions: 2019-07-25 18:02:41 UTC to 2024-07-20 23:26:03 UTC.

0123452019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC110
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC505
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC202
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC202

What Hacker News says

10 sources
> It's just a mess, hence why I sort of gave up at some point and for some "esoteric" syscall I just hardcode them. Presuming you don't want to keep doing this forever, but would rather do insane amounts of up-front work if it would enable you to never have to touch this again: 1. Have you considered writing some code that takes a configured + built kernel source tree; finds the intermediate build artifacts pertaining to the code unit that contains the syscall handler; and parses those? And then taking the resulting IR data-structure / AST / whatever, and doing some symbolic interpretation of it — to enable you to essentially do an xpath-like expression match on "does something specific with a concrete syscall number that isn't already in the known set for the arch"? AFAICT you could generate your own syscall table from that, and it would
Read the rest
be exhaustive. 2. Have you considered dropping a little bit of driver-program code into the kernel source tree, that just "does syscall handling according to the passed-in paralemeters" — i.e. where the artifact built from compiling this file, would be an EFI-app pseudo-unikernel that naively pretends all kernel services were already initialized (they weren't); would do one syscall operation, calling directly into the syscall handler; and then would immediately halt afterward — and then feeding the resulting "executable" to https://github.com/google/AFL ?
In A Linux kernel syscall implementation tracker
QuickcCheck-type tools (generators for tests that know about the edge cases of a domain - e. g. for the domain of numbers considering things like 0, the infinities, various almost-and-just-over powers of two, NaN and mantissas for floats, etc.): * QuickCheck: https://hackage.haskell.org/package/QuickCheck * Hypothesis: https://hypothesis.readthedocs.io/en/latest/ * JUnit QuickCheck: https://github.com/pholser/junit-quickcheck Fuzz testing tools (tools which mutate the inputs to a program in order to find interesting / failing states in that program). Generally paired with code coverage: * American Fuzzy Lop (AFL): https://github.com/google/AFL * JQF: https://github.com/rohanpadhye/JQF Mutation / Fault based test tools (review your existing unit coverage and try to introduce changes to your _production_ code that none of your tests catch)
Read the rest
In GitHub Copilot available for JetBrains and Neovim
For anyone interested there is something called fuzzing that uses usually code coverage based heuristics to generate data to find bugs. For example LLVM's lib fuzzer uses instrumentation to track code coverage and mutates data to find invalid behaviour. https://llvm.org/docs/LibFuzzer.html It uses a compiler pass to insert code to branch points functions calls etc. I think it uses genetic algorithms to increase coverage by changing the data. There are others that work in similar ways one of them is. https://github.com/google/AFL
In Kids find a security flaw in Linux Mint by mashing keys

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