Mario Kahlhofer blu3r4y

Methode zur Steuerberechnung für den Liquiditätsfonds

Dieses Dokument erklärt, wie das bei E-Trade im Morgan Stanley US Dollar Treasury Liquidity Fund gehaltene Guthaben in Österreich besteuert wird und wie es in der Beilage E 1kv zu erklären ist.

§0. TL;DR

Wenn du bei E-Trade Guthaben hältst, handelt es sich um einen Geldmarktfonds ohne steuerliche Meldung (Nichtmeldefonds), den du in deiner Steuererklärung angeben musst. Zwei Beträge kommen in die E 1kv, beide werden mit 27,5% besteuert:

1) KZ 898 — Ausschüttungen. Gehe zu E-Trade > Stock Plan > Holdings > Cash > lade die Excel-Datei herunter. Addiere alle Dividenden, die in diesem Steuerjahr ausbezahlt wurden. Rechne jede Zeile zum EZB-Referenzkurs des jeweiligen Zahlungsdatums in EUR um. Trage die Summe in 📝 KZ 898 ein.

Write-Up: Web - Log4j & Log4j2 from Google CTF 2022

This blog post explains three ways to exploit Log4j 2.17.2 from Google CTF 2022:

Level 1: Trigger an exception in Log4j that contains the flag
Level 2: Guessing the flag with the help of RegEx conversion patterns
Bonus: Guessing the flag with a time-based side channel using ReDoS

The bonus was not necessary to solve the challenge but fun to code ;)

Keybase proof

I hereby claim:

I am blu3r4y on github.
I am blu3r4y (https://keybase.io/blu3r4y) on keybase.
I have a public key ASAaCd4yddilhaf1QEv9GI40ZdcfMICJMpPkklewor035Ao

To claim this, I am signing this object:

	name: setup-poetry
	on:
	push:

	jobs:
	test:
	runs-on: ubuntu-latest
	steps:
	- name: Checkout repository
	uses: actions/checkout@v4

	# Copyright 2021
	# Dynatrace Research
	# SAL Silicon Austria Labs
	# LIT Artificial Intelligence Lab
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0

	# Copyright 2021
	# Dynatrace Research
	# SAL Silicon Austria Labs
	# LIT Artificial Intelligence Lab
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0

	# Copyright 2021
	# Dynatrace Research
	# SAL Silicon Austria Labs
	# LIT Artificial Intelligence Lab
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0

	import re, sys

	REGEX_ATTACHMENT = {"de": r"\u200E(.*?) \(Datei angehängt\)"}
	SUB_ATTACHMENT = r"<attached: \g<1>>"


	def convert(file: str, locale="de"):
	"""
	Convert the locale of attachments to the english one.
	The output is written to a file in the same location.

	import numpy as np


	def majority_vote(*arrays: np.array, n_classes: int = None) -> np.array:
	"""
	Given an arbitrary number of integer-based, one-dimensional input vectors
	that represent class labels, compute a new vector that will take the majority
	label of the input vectors. Ties are broken randomly for each observation.

	If you omit the `n_classes` argument, it will be inferred by counting

	from circus import get_arbiter

	alice = dict(
	name="alice",
	cmd="python -m flask run --port=81",
	env={"FLASK_APP": "flask_alice.py"},
	copy_env=True,
	copy_path=True,
	)