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Nebula Security · AI security research lab — 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
medullainstitute.org ↗	73 match 2 shared topics	Medulla AI Security Research Institute	United States~ en	artificial-intelligence	robots llmsaihumans	emailphone	partial · 8
0xdkay.me ↗	73 match 2 shared topics	Dongkwan Kim \| Security Researcher	en	information-and-network-security	robotsllmsaihumans	emailphone	none
0ximu.dev ↗	72 match 2 shared topics	0ximu \| Security Researcher	en	information-and-network-security	robotsllmsaihumans	emailphone	none
lampsec.dev ↗	71 match 2 shared topics	LAMPSEC — Autonomous AI Security Research	en	information-and-network-security	robotsllmsaihumans	emailphone	none
shujaatmirza.com ↗	70 match 2 shared topics	Shujaat Mirza \| AI Safety and Security Researcher	en	information-and-network-security	robotsllmsaihumans	emailphone	none
astrix.security ↗	70 match 2 shared topics	Identity Security for AI Agents & NHIs \| Astrix Security	en	information-and-network-security WordPress	robots llmsaihumans	emailphone	none
connectwithsweetsecurity.com ↗	69 match 2 shared topics	Sweet Security: Cloud Security Built for Today's AI Era	en	information-and-network-security Webflow	robotsllmsaihumans	emailphone	none
mend.io ↗	69 match 2 shared topics	Mend.io \| Application Security and AI Security, Unified	United States~ en	information-and-network-security WordPress	robotsllmsaihumans	emailphone	none
vmsecops.com ↗	69 match 2 shared topics	VM SecOps — Vasu Melipaka \| Senior Pentester & AI Security Researcher	en	information-and-network-security	robotsllmsaihumans	emailphone	none
algorithmicresearchgroup.com ↗	68 match 2 shared topics	Algorithmic Research Group — Open infrastructure for AI security research	en	information-and-network-security	robotsllmsaihumans	emailphone	none
simeongarratt.com ↗	68 match 2 shared topics	Simeon Garratt \| Systems Architect & Security Researcher	en	artificial-intelligence	robots llmsaihumans	emailphone	partial · 5
composedsecurity.com ↗	68 match 2 shared topics	Composed Security — Verified product security for AI engineering teams	en	information-and-network-security	robots llmsaihumans	emailphone	none
herdsecurityio.com ↗	68 match 2 shared topics	Herd Security — The AI Creative Platform for Security Training	en	artificial-intelligence Sanity	robotsllmsaihumans	emailphone	partial · 8
herdsecurity.io ↗	68 match 2 shared topics	Herd Security — The AI Creative Platform for Security Training	en	artificial-intelligence Sanity	robotsllmsaihumans	emailphone	none
eugenetawiah.com ↗	68 match 2 shared topics	Eugene Tawiah \| Cybersecurity Executive, Advisor & AI Security Leader	United States~ en	information-and-network-security	robotsllmsaihumans	emailphone	none
ai-security-consultant.ch ↗	68 match 2 shared topics	AI Security Consultant — Automated Cybersecurity for SMEs	Switzerland en	artificial-intelligence	robotsllmsaihumans	emailphone	none
sidmalik.dev ↗	68 match 2 shared topics	Siddhant Malik — AI Research · Bioinformatics · Security	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
hermesecurity.com ↗	67 match 2 shared topics	Hermes Security Solutions - Cybersecurity Offensive	en	information-and-network-security Webflow	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.