context safety score
A score of 47/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
encoded payload
suspicious base64-like blobs detected in page content
social engineering
UNU is a micro-task exchange (crowd-marketing platform) that pays users to post fake comments, reviews, forum posts, buy social media followers/likes/reposts, and manipulate ratings across VK, Instagram, YouTube, Facebook, Twitter, Telegram, and Odnoklassniki. The platform explicitly advertises coordinated inauthentic behavior as a paid service, enabling astroturfing and artificial social proof at scale. (location: page.html:53-243, page-text.txt:16-205)
hidden content
Yandex Metrika tracking pixel is positioned off-screen using 'position:absolute; left:-9999px;' to silently track users without visible indication. This is a classic hidden tracker pattern used to collect behavioral data covertly. (location: page.html:385)
obfuscated code
A JavaScript block contains unresolved PHP template variables ('".$site_url."/include/ajax_nomodal.php' and '$loggedin_id') embedded as literal strings in the rendered HTML. This indicates server-side template code was leaked into the client-side output without being evaluated, which may expose backend implementation details or could be an injection artifact. (location: page.html:354)
social engineering
The platform explicitly markets itself as a source of fake social proof — 'only real people, every one verified' — while simultaneously selling followers, likes, comments, and reviews. This framing is designed to deceive both platform buyers and the end-users who encounter the manufactured engagement on third-party sites. (location: page.html:124-127)
hidden content
Several navigation links are commented out in the HTML (dashboard link, VK and Twitter social links, app install promotion) suggesting deliberately suppressed or conditionally hidden UI elements. While individually minor, this pattern combined with the leaked PHP variables suggests the page may serve different content to different audiences. (location: page.html:268,289-291)
curl https://api.brin.sh/domain/unu.imCommon questions teams ask before deciding whether to use this domain in agent workflows.
unu.im currently scores 47/100 with a suspicious verdict and medium 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.
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