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Hacker Factor: Home Page — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Information And Network Security →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
yubico.com ↗	70 match shared topic	Yubico \| YubiKey Strong Two Factor Authentication	en	information-and-network-security WordPress	robotsllmsaihumans	emailphone	none
h1.community ↗	70 match shared topic	H1 \| HackerOne Community	en	information-and-network-security	robots llmsaihumans	emailphone	none
exploit-db.com ↗	70 match shared topic	Exploit Database - Exploits for Penetration Testers, Researchers, and Ethical Hackers	en	information-and-network-security	robotsllmsaihumans	emailphone	none
huntress.com ↗	70 match shared topic	Huntress Managed Security Platform: Wrecking Hackers 24/7 \| Huntress	en	information-and-network-security	robots llmsaihumans	emailphone	none
authy.com ↗	70 match shared topic	Authy: Two-factor Authentication (2FA) App & Guides \| Authy	en	information-and-network-security Adobe Experience Manager	robotsllmsaihumans	emailphone	none
vanishingpointsecurity.com ↗	70 match shared topic	VanishingPoint - Make Yourself Invisible to Hackers & Surveillance	en	information-and-network-security WordPress	robotsllmsaihumans	emailphone	none
rublon.com ↗	70 match shared topic	Rublon Multi-Factor Authentication (MFA) - Secure Remote Access	en	information-and-network-security WordPress	robotsllmsaihumans	emailphone	partial · 2
rublon.net ↗	70 match shared topic	Rublon Multi-Factor Authentication (MFA) - Secure Remote Access	en	information-and-network-security WordPress	robotsllmsaihumans	emailphone	none
multifactor.com ↗	70 match shared topic	Multifactor \| Secure Online Account Sharing for Humans & AI	en	information-and-network-security	robotsllmsaihumans	emailphone	none
mfchf.com ↗	70 match shared topic	Multi-Factor Credential Hashing Function (MFCHF)	en	information-and-network-security	robotsllmsaihumans	emailphone	none
mfkdf.com ↗	70 match shared topic	Multi-Factor Key Derivation Function (MFKDF2) by Multifactor	en	information-and-network-security	robotsllmsaihumans	emailphone	none
mfdpg.com ↗	70 match shared topic	Multi-Factor Key Derivation Function (MFKDF2) by Multifactor	en	information-and-network-security	robotsllmsaihumans	emailphone	none
hackervalley.com ↗	70 match shared topic	Hacker Valley Media \| Cybersecurity Podcasts & Creative Agency	en	information-and-network-security	robotsllmsaihumans	emailphone	none
hackers.blue ↗	70 match shared topic	Anthony Kava: hacker, speaker, cyber crime, infosec, digital forensics \| Tony Kava \| karver	en	information-and-network-security	robotsllmsaihumans	emailphone	none
impostor.domains ↗	70 match shared topic	Anthony Kava: hacker, speaker, cyber crime, infosec, digital forensics \| Tony Kava \| karver	en	information-and-network-security	robotsllmsaihumans	emailphone	none
dmca.com ↗	55 match shared topic	Content Protection and Takedown Company Since 2006 \| DMCA.com	en	information-and-network-security	robotsllmsaihumans	emailphone	none
wpguardian.io ↗	55 match shared topic	Effective Security for WordPress Sites- WPGuardian	en	information-and-network-security WordPress	robotsllmsaihumans	emailphone	none
domaintools.com ↗	55 match shared topic	Threat Intelligence & Domain Security Platform \| DomainTools	en	information-and-network-security	robotsllmsaihumans	emailphone	none

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.