Skip to content

Instantly share code, notes, and snippets.

View kiwamizamurai's full-sized avatar
🤌
Open to work opportunities

きわみざむらい kiwamizamurai

🤌
Open to work opportunities
View GitHub Profile
@hackermondev
hackermondev / research.md
Last active August 20, 2025 19:40
Unique 0-click deanonymization attack targeting Signal, Discord and hundreds of platform

hi, i'm daniel. i'm a 15-year-old high school junior. in my free time, i hack billion dollar companies and build cool stuff.

3 months ago, I discovered a unique 0-click deanonymization attack that allows an attacker to grab the location of any target within a 250 mile radius. With a vulnerable app installed on a target's phone (or as a background application on their laptop), an attacker can send a malicious payload and deanonymize you within seconds--and you wouldn't even know.

I'm publishing this writeup and research as a warning, especially for journalists, activists, and hackers, about this type of undetectable attack. Hundreds of applications are vulnerable, including some of the most popular apps in the world: Signal, Discord, Twitter/X, and others. Here's how it works:

Cloudflare

By the numbers, Cloudflare is easily the most popular CDN on the market. It beats out competitors such as Sucuri, Amazon CloudFront, Akamai, and Fastly. In 2019, a major Cloudflare outage k

How to add an image to a gist

  1. Create a gist if you haven't already.
  2. Clone your gist:
    # make sure to replace `<hash>` with your gist's hash
    git clone https://gist.github.com/<hash>.git # with https
    git clone [email protected]:<hash>.git     # or with ssh
@fabianp
fabianp / partial_corr.py
Last active March 21, 2024 09:00
Partial Correlation in Python (clone of Matlab's partialcorr)
"""
Partial Correlation in Python (clone of Matlab's partialcorr)
This uses the linear regression approach to compute the partial
correlation (might be slow for a huge number of variables). The
algorithm is detailed here:
http://en.wikipedia.org/wiki/Partial_correlation#Using_linear_regression
Taking X and Y two variables of interest and Z the matrix with all the variable minus {X, Y},