Main

related bits

0

processing priority

4

site type

0 (generic, awaiting analysis)

review version

11

html import

20 (imported)

Events

first seen date

2024-02-22 19:49:21

expired found date

-

created at

2024-05-31 14:53:14

updated at

2026-02-08 12:27:06

Domain name statistics

length

12

crc

14155

tld

2211

nm parts

0

nm random digits

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nm rare letters

0

Connections

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-

previous id

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related id

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dns primary id

173561409

dns alternative id

0

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0 (unclassified, or currently active)

Subdomains and pages

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page imported products

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page imported random

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page imported parking

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Error counters

count skipped due to recent timeouts on the same server IP

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count content received but rejected due to 11-799

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count dns errors

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count cert errors

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count timeouts

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count http 429

0

count http 404

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count http 403

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count http 5xx

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next operation date

-

Server

server bits

server ip

-

Mainpage statistics

mp import status

20

mp rejected date

-

mp saved date

-

mp size orig

22426

mp size raw text

5596

mp inner links count

1

mp inner links status

20 (imported)

Open Graph

title

OWASP Top 10 for LLM Applications

description

Learn more at llmtop10.com

site name

author

updated

2025-12-12 19:28:33

raw text

OWASP | Top 10 for Large Language Model Applications Top 10 List Intro Resources News Contributors References OWASP on GitHub LLM Project Page OWASP Foundation OWASP Top 10 for LLM Applications This document is the latest exciting chapter in the ongoing efforts to enhance security in the rapidly evolving field of artificial intelligence. Download Now LLM01 Prompt Injection Crafty inputs can manipulate a Large Language Model, causing unintended actions. Direct injections overwrite system prompts, while indirect ones manipulate inputs from external sources. LLM02 Insecure Output Handling This vulnerability occurs when an LLM output is accepted without scrutiny, exposing backend systems. Misuse may lead to severe consequences like XSS, CSRF, SSRF, privilege escalation, or remote code execution. LLM03 Training Data Poisoning This occurs when LLM training data is tampered, introducing vulnerabilities or biases that compromise security, effectiveness...

Text analysis

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block type

0 (no issues)

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1 (English)

index version

2025110801

spam phrases

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Text statistics

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text cyrillic

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text characters

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text words

806

text unique words

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text lines

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text sentences

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text paragraphs

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text words per sentence

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text matched dictionaries

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RSS

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32 (unknown)

rss found date

2025-01-10 21:21:52

rss size orig

102216

rss items

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rss spam phrases

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rss detected language

1 (English)

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Sitemap

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34 (reserved: import paused pending content quality/relevance assessment after importing first 500 pages)

sitemap review version

2

sitemap urls count

56

sitemap urls adult

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sitemap filtered videos

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2024-03-02 09:49:20

sitemap process date

2025-04-27 01:22:13

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2026-02-08 12:27:06