context safety score
A score of 44/100 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
js obfuscation
JavaScript uses eval() with String.fromCharCode — common obfuscation
js obfuscation
JavaScript uses eval(atob()) — base64-encoded payload execution
obfuscated code
The page contains a heavily obfuscated JavaScript payload (~16KB) using a large string array rotation technique with encoded identifiers (e.g., a0T, a0N, YK functions). The script uses eval(), atob(), XMLHttpRequest, and dynamic script loading. This is the NGENIX bot-challenge system, but the obfuscation prevents static analysis of the full execution path including what is fetched from /ngenix-aGVsbG8sIGh1bWFu/bot-challenge/challenge and dynamically eval'd. (location: page.html, line 2, <script> block)
hidden content
The challenge URL path embeds a base64-encoded string 'aGVsbG8sIGh1bWFu' which decodes to 'hello, human' — an anthropomorphic message embedded in machine-readable infrastructure paths. While likely benign (NGENIX CDN humor), it represents non-visible encoded content in the page's operational context. (location: page.html, line 2, window.ctx challenge_modules_location and challenge_url parameters)
obfuscated code
The script dynamically fetches external challenge modules from /ngenix-aGVsbG8sIGh1bWFu/bot-challenge/modules/ and eval()s the returned code at runtime. The eval() call is gated on a condition (g[YK(606)]) but falls through to eval(g[YK(437)]) — meaning arbitrary remote code is executed in the page context. An AI agent browsing this page with JavaScript execution capability could have attacker-controlled code evaluated. (location: page.html, line 2, eval() at character offset ~12022)
curl https://api.brin.sh/domain/rivegauche.ruCommon questions teams ask before deciding whether to use this domain in agent workflows.
rivegauche.ru currently scores 44/100 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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