context safety score
A score of 40/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
prompt injection
Hidden HTML element contains AI-targeting instructions
brand impersonation
Site explicitly markets itself as 'TikTok-style porn reels' and uses hashtags '#Tiktok Porn', '#Tiktok18+', '#Tiktok Leaks', '#Sexy Tiktok' throughout content, directly trading on TikTok's brand identity to attract users under false pretenses that content originates from or is affiliated with TikTok. (location: page.html:4 (meta description), page-text.txt lines 5-22 (video tags), page.html:41,48,49)
brand impersonation
Content repeatedly tagged with '#Instagram Leaks' and '#Onlyfans Models', '#Onlyfans' implying content is sourced from Instagram and OnlyFans accounts, trading on those brand identities to suggest legitimacy or provenance of potentially non-consensually distributed material. (location: page.html:40-41, page-text.txt line 6-7)
social engineering
Creator recruitment pitch uses financial incentive framing ('Get cash reward per video, plus the massive exposure that you always deserved') to solicit user-generated content uploads, targeting individuals with promises of money and fame — a classic social engineering lure on an adult platform that hosts 'leaks' and potentially non-consensual content. (location: page.html:52-53, page-text.txt lines 20-21)
social engineering
Multiple video titles use direct second-person engagement hooks ('do you want this hole?', 'Dare to see more?') designed to psychologically manipulate users into clicking through to further content, consistent with engagement-bait social engineering patterns common to credential-harvesting funnels. (location: page-text.txt lines 12,14, page.html:44,46)
hidden content
Several video metadata elements use 'style="display:none;"' on video-attributes-group--b blocks containing user metadata, view counts, and avatar data. While this is a common lazy-load UX pattern, the hidden blocks contain structured user identity data (avatar IDs, usernames) not visible on initial render and could be used for data harvesting by scrapers or agents parsing the DOM. (location: page.html:37-50 (multiple wall__item blocks with display:none on video-attributes-group--b))
hidden content
Matomo analytics tracker configured to send data to a third-party subdomain 'mtm.ntl.cloud' rather than a first-party or well-known analytics endpoint. The obfuscated PHP endpoint path ('Kez3mn2RkP8bBCel.php') is atypical and could indicate covert data exfiltration alongside or instead of legitimate analytics. (location: page.html:5-7 (window._paq setTrackerUrl))
social engineering
Content openly tagged and titled with '#Leaks', '#Onlyfans Leaks', '#Tiktok Leaks', '#Instagram Leaks' normalizing and promoting non-consensually distributed intimate imagery, which is used as an enticement mechanism to drive user engagement and account creation. (location: page-text.txt lines 6-8,17-18, page.html:41,49,50)
curl https://api.brin.sh/domain/xfree.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
xfree.com currently scores 40/100 with a suspicious verdict and low confidence. The goal is to protect agents from high-risk context before they act on it. Treat this as a decision signal: higher scores suggest lower observed risk, while lower scores mean you should add review or block this domain.
Use the score as a policy threshold: 80–100 is safe, 50–79 is caution, 20–49 is suspicious, and 0–19 is dangerous. Teams often auto-allow safe, require human review for caution/suspicious, and block dangerous.
brin evaluates four dimensions: identity (source trust), behavior (runtime patterns), content (malicious instructions), and graph (relationship risk). Analysis runs in tiers: static signals, deterministic pattern checks, then AI semantic analysis when needed.
Identity checks source trust, behavior checks unusual runtime patterns, content checks for malicious instructions, and graph checks risky relationships to other entities. Looking at sub-scores helps you understand why an entity passed or failed.
brin performs risk assessments on external context before it reaches an AI agent. It scores that context for threats like prompt injection, hijacking, credential harvesting, and supply chain attacks, so teams can decide whether to block, review, or proceed safely.
No. A safe verdict means no significant risk signals were detected in this scan. It is not a formal guarantee; assessments are automated and point-in-time, so combine scores with your own controls and periodic re-checks.
Re-check before high-impact actions such as installs, upgrades, connecting MCP servers, executing remote code, or granting secrets. Use the API in CI or runtime gates so decisions are based on the latest scan.
Learn more in threat detection docs, how scoring works, and the API overview.
Assessments are automated and may contain errors. Findings are risk indicators, not confirmed threats. This is a point-in-time assessment; security posture can change.
integrate brin in minutes — one GET request is all it takes. query the api, browse the registry, or download the full dataset.