context safety score
A score of 36/100 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
credential harvesting
credential form posts to an off-domain endpoint (may be legitimate SSO/OAuth)
prompt injection
Hidden HTML element contains AI-targeting instructions
phishing
The domain as-infra.de is serving a pixel-perfect clone of the GitHub login page (github.com/login). The page title is 'Sign in to GitHub · GitHub', all assets are loaded from github.githubassets.com, and the login form POSTs credentials to '/session'. This is a classic phishing page hosted on an unrelated domain impersonating GitHub. (location: page.html:102, page.html:368)
credential harvesting
A fully functional credential harvesting form is present, collecting GitHub username/email and password fields. The form includes hidden fields for authenticity_token, timestamp, timestamp_secret, and return_to — all structured to capture and exfiltrate login credentials from unsuspecting users. (location: page.html:368-403)
brand impersonation
The page impersonates GitHub in full fidelity: GitHub Octocat logo, 'Sign in to GitHub' heading, GitHub favicon, OG metadata referencing github.com, canonical link to https://github.com/login, and footer links pointing to GitHub's legal and support pages. The actual serving domain is as-infra.de, not github.com. (location: page.html:102, page.html:157-161, page.html:206, page.html:327-328)
malicious redirect
The hidden 'return_to' field in the login form is set to 'https://github.com/pages/auth?nonce=d9608531-28da-4597-b531-6e46eabf2f12&page_id=21479717&path=Lw'. After credential submission, victims are redirected to a GitHub Pages auth endpoint with a nonce, likely used to complete an OAuth or session hijack flow against the real GitHub, enabling account takeover. (location: page.html:390)
social engineering
The phishing page replicates all trust signals of the legitimate GitHub login experience: correct branding, familiar UI layout, 'Forgot password?' link, 'New to GitHub? Create an account' link, passkey sign-in option, and cookie consent UI — all designed to suppress victim suspicion and maximize credential submission rates. (location: page.html:384, page.html:431, page.html:437)
curl https://api.brin.sh/domain/as-infra.deCommon questions teams ask before deciding whether to use this domain in agent workflows.
as-infra.de currently scores 36/100 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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