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added pdf workflow automation and headers for 5 languages
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talesh committed Jul 24, 2024
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63 changes: 63 additions & 0 deletions .github/workflows/markdown-to-pdf.yml
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## Code

name: Markdown to PDF

on:
push:
branches:
- main
paths:
- '1_1_vulns/translations/**'
pull_request:
branches:
- main
paths:
- '1_1_vulns/translations/**'

env:
LANGUAGES: '["de", "it", "pt", "hi", "zh"]' # Add or remove language codes as needed

jobs:
convert-markdown-to-pdf:
runs-on: ubuntu-latest

steps:
- name: Checkout repository
uses: actions/checkout@v4

- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: '20' # Using Node.js version 20

- name: Configure locale
run: |
sudo locale-gen en_US.UTF-8
echo "LC_ALL=en_US.UTF-8" >> $GITHUB_ENV
echo "LANG=en_US.UTF-8" >> $GITHUB_ENV
echo "LANGUAGE=en_US.UTF-8" >> $GITHUB_ENV
- name: Install necessary fonts
run: |
sudo apt-get update
sudo apt-get install -y fonts-noto fonts-noto-cjk fonts-noto-color-emoji fonts-indic fonts-arphic-ukai fonts-arphic-uming fonts-ipafont-mincho fonts-ipafont-gothic fonts-unfonts-core
- name: Install md-to-pdf
run: npm install -g md-to-pdf

- name: Run markdown_to_pdf.sh for each language
run: |
for lang in $(echo $LANGUAGES | jq -r '.[]'); do
./markdown_to_pdf.sh --language $lang
done
working-directory: ./markdown-to-pdf

- name: Get current date and time
id: date
run: echo "date=$(date '+%Y-%m-%d-%H-%M-%S')" >> $GITHUB_ENV

- name: Upload generated PDFs as artifact
uses: actions/upload-artifact@v4
with:
name: pdf-translations-zipfile-${{ env.date }}
path: ./markdown-to-pdf/generated/*.pdf
20 changes: 19 additions & 1 deletion 1_1_vulns/translations/de/LLM00_Introduction.md
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<div class="frontpage">
<div class="smalllogo">
<img src="/img/OWASP-title-logo.svg"></img>
</div>
<div class="doctitle">
OWASP Top 10 für LLM-Applikationen
</div>
<div class="docversion">
VERSION 1.1
</div>
<div class="docdate">
<b>Veröffentlicht am</b>: 10. Juni 2024
</div>
<div class="doclink">
https://llmtop10.com
</div>
</div>

## Einleitung

### Die Entstehung der Liste
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![Abb_1](images/fig_5_2.jpg)

##### Abbildung 1: OWASP Top 10 für LLM-Applikationen visualisiert
##### Abbildung 1: OWASP Top 10 für LLM-Applikationen visualisiert
19 changes: 18 additions & 1 deletion 1_1_vulns/translations/hi/LLM00_Introduction.md
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<div class="frontpage">
<div class="smalllogo">
<img src="/img/OWASP-title-logo.svg"></img>
</div>
<div class="doctitle">
OWASP टॉप 10 फॉर LLM एप्लिकेशंस
</div>
<div class="docversion">
संस्करण 1.1
</div>
<div class="docdate">
<b>प्रकाशित:</b> 16 अक्टूबर, 2023
</div>
<div class="doclink">
https://llmtop10.com
</div>
</div>

## परिचय

### सूची की उत्पत्ति
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18 changes: 18 additions & 0 deletions 1_1_vulns/translations/it/LLM00_Introduction.md
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<div class="frontpage">
<div class="smalllogo">
<img src="/img/OWASP-title-logo.svg"></img>
</div>
<div class="doctitle">
OWASP Top 10 per le applicazioni LLM
</div>
<div class="docversion">
Versione 1.1
</div>
<div class="docdate">
<b>Pubblicato</b>: 16 Ottobre 2023
</div>
<div class="doclink">
https://llmtop10.com
</div>
</div>

## Introduzione

L'introduzione sul mercato di massa dei chatbot pre-addestrati a fine 2022 ha innescato un'ondata di frenetico interesse per i modelli di linguaggio a grandi dimensioni (LLM). Le aziende, desiderose di sfruttare il potenziale degli LLM, li stanno integrando rapidamente nei loro sistemi e nelle offerte destinate ai clienti. Tuttavia, la velocità con cui gli LLM vengono adottati ha superato il tempo necessario per stabilire protocolli di sicurezza esaustivi, lasciando molte applicazioni vulnerabili a seri problemi di sicurezza.
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1 change: 0 additions & 1 deletion 1_1_vulns/translations/pt/LLM00_Introduction.md
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<div class="frontpage">
<div class="smalllogo">
<img src="/img/OWASP-title-logo.svg"></img>
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18 changes: 18 additions & 0 deletions 1_1_vulns/translations/zh/LLM00_Introduction.md
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<div class="frontpage">
<div class="smalllogo">
<img src="/img/OWASP-title-logo.svg"></img>
</div>
<div class="doctitle">
OWASP 大语言模型人工智能应用Top 10 安全威胁
</div>
<div class="docversion">
版 1.1
</div>
<div class="docdate">
<b>发布日期<b>:2023 年 10 月 16 日
</div>
<div class="doclink">
https://llmtop10.com
</div>
</div>

## 介绍
2022 年底,随着ChatGPT进入大众市场,人们对大型语言模型 (LLM) 的关注尤为浓厚。渴望利用大语言模型潜力的企业正在迅速将其整合到其运营和面向客户的产品中。然而,大语言模型的采用速度已经超过了全面安全协议的建立速度,导致许多应用程序容易受到高风险问题的影响。很明显,大语言模型还没有统一的资源来解决这些安全问题。很多开发者对于与LLM相关的安全风险不够了解,所以相关资源很分散。而OWASP正好能够协助推进这项技术的更安全应用。

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