context safety score
A score of 65/100 indicates minor risk signals were detected. The entity may be legitimate but has characteristics that warrant attention.
encoded payload
suspicious base64-like blobs detected in page content
brand impersonation
The site operates as 'DownloadTik' at downloadtik.to, leveraging TikTok's brand, logo references, and trademark to attract TikTok users. The domain name incorporates 'tiktok' implicitly through 'tik', and the site heavily uses TikTok branding throughout. While a non-affiliation disclaimer exists, the site is designed to be mistaken for an official or semi-official TikTok utility. The apple-mobile-web-app-title is set to 'TikSaver' (different from the site name 'Downloadtik'), suggesting possible identity confusion. (location: page.html:18-20, page.html:1200-1201)
hidden content
Yandex Metrika tracker (ID 96965057) is loaded conditionally with only a 5% probability (Math.random() < 0.05), meaning it fires silently on a fraction of visits and injects a tracking pixel positioned off-screen at left:-9999px. This stochastic loading makes the tracker invisible to most automated scanners and is a known technique to evade detection while still collecting user telemetry including webvisor (session recording) and clickmap data. (location: page.html:1298-1317)
hidden content
Yandex Metrika webvisor is enabled (webvisor:true), which records full user sessions including mouse movements, clicks, and keystrokes. Combined with the stochastic 5% load rate designed to evade scanners, this constitutes covert surveillance-grade data collection on a fraction of users without prominent disclosure. (location: page.html:1310-1315)
social engineering
The site displays fabricated or unverifiable user reviews (Ava, Ethan, Isabella, James, Liam, Mason, Mia, Noah, Sophia, Emma) with uniform 5-star ratings using a single shared stars.png image. All reviews are generic, positive, and lack any platform verification. This manufactured social proof is designed to lower user suspicion and encourage engagement with an unaffiliated third-party service that receives TikTok URLs submitted by users. (location: page.html:794-901)
social engineering
The schema.org SoftwareApplication markup claims 47,532 ratings with a 4.8/5 average rating. This fabricated aggregate rating is embedded in structured data to influence both users and AI agents/crawlers that parse schema data, artificially inflating perceived trustworthiness in search results and AI-assisted browsing contexts. (location: page.html:1337-1347, page.html:1429-1433)
curl https://api.brin.sh/domain/downloadtik.toCommon questions teams ask before deciding whether to use this domain in agent workflows.
downloadtik.to currently scores 65/100 with a caution 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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