context safety score
A score of 37/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
hidden instruction
high hidden content ratio detected in DOM
obfuscated code
The page contains heavily obfuscated JavaScript using hex-encoded function names (_0x5ec6, _0x5f38, _0x19b1, _0x6d98) with shuffled string arrays and integer arithmetic integrity checks. This pattern is characteristic of anti-bot/antidetection scripts that fingerprint visitors, detect headless browsers, and submit hidden forms — obscuring its true behavior from analysis. (location: page.html: <script> blocks containing _0x5ec6/_0x5f38 and _0x19b1/_0x6d98 function families)
prompt injection
The page title is 'Solar Space Antibot' while the domain is edna.ru. The visible content presents a CAPTCHA-style human verification challenge ('Please verify you are a human', 'I'm not a robot!'). This mismatch — a third-party antibot service (solarspace.pro) serving content on behalf of edna.ru — can mislead AI agents into believing the page is a legitimate CAPTCHA gate and interacting with it (clicking the checkbox), which triggers hidden JavaScript form submission including a CSRF token and navigator fingerprinting. (location: page.html: <title>, .header, .dfgsdfd checkbox, verifyCaptcha())
hidden content
The obfuscated JavaScript detects headless browsers and bots via navigator.webdriver, HeadlessChrome, puppeteer, phantomjs, node, wget, curl user-agent strings. It also tracks mouse movement timing, touch events, and click coordinates to distinguish humans from automated agents. Upon passing, it silently constructs and submits a hidden POST form containing window.__csrf token value without user awareness. (location: page.html: function N() — creates hidden <form method='POST'>, appends hidden input with name='csrf_token' and value=window.__csrf, submits to document.body)
credential harvesting
A CSRF token ('dEZZuBbiBw10l0Hit10k7EL6NuksWFcS') is embedded in the global window object and silently exfiltrated via a hidden form POST when the antibot challenge is satisfied. This token submission occurs without explicit user consent or visible indication of data transmission. (location: page.html: <script>window.__csrf="dEZZuBbiBw10l0Hit10k7EL6NuksWFcS"</script> and function N() form submission)
social engineering
The page impersonates a standard CAPTCHA/human-verification flow ('Please verify you are a human', 'I'm not a robot!') to socially engineer users and automated agents into clicking a checkbox. The interaction silently triggers JavaScript-based browser fingerprinting and hidden form submission rather than performing a genuine bot challenge. (location: page.html: .header text, .description text, #captcha checkbox with onclick='verifyCaptcha()')
brand impersonation
The antibot page renders the 'Solar Space' (solarspace.pro) brand logo and loads a custom font from img.solarspace.pro, presenting a third-party security brand while operating on the edna.ru domain. The policy link points to rt-solar.ru (Rostelecom Solar), implying affiliation with a known Russian cybersecurity brand to lend legitimacy to the challenge page. (location: page.html: font-face src url('https://img.solarspace.pro/forbidden/ONYOneBeta-Light.otf'), SVG logo paths, href='https://rt-solar.ru/about_company/information/pdib/ppopd.pdf')
curl https://api.brin.sh/domain/edna.ruCommon questions teams ask before deciding whether to use this domain in agent workflows.
edna.ru currently scores 37/100 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.
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