context safety score
A score of 30/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
hidden instruction
high hidden content ratio detected in DOM
brand impersonation
The page is served from appspot.com but renders a pixel-perfect replica of the Google Accounts sign-in page (accounts.google.com/v3/signin/) using a <base href> tag pointing to accounts.google.com. This impersonates Google's brand on a third-party domain to deceive users into believing they are on a legitimate Google login page. (location: page.html:1 — <base href="https://accounts.google.com/v3/signin/">)
phishing
A fully functional Google sign-in form (email/phone input, 'Next' button, 'Create account' link) is hosted on appspot.com rather than accounts.google.com. The domain appspot.com is Google's App Engine platform and can be used by any developer to host arbitrary content, making this a credential-phishing page targeting Google account holders. (location: page.html:91 — form with identifierNext button and email/phone input)
credential harvesting
JavaScript code initializes a PassiveLoginProber that periodically reads the APISID cookie value via Ha.get('APISID') and sends it via XHR to /PassiveLoginProber, effectively exfiltrating session cookie data to the page's server on appspot.com. (location: page.html:89 — Cb function: Ha.get('APISID') and e.send(c,...) to /PassiveLoginProber)
malicious redirect
On successful login probe response ('OK'), the code redirects window.location.href to /ServiceLogin, chaining the user through an attacker-controlled redirect flow. Additionally, the signup link encodes a 'continue' parameter pointing to console.cloud.google.com/appengine, which may be used to redirect victims after credential submission. (location: page.html:88 — Db.prototype.l: c.href = a (redirect to /ServiceLogin on OK response))
obfuscated code
The page contains heavily obfuscated JavaScript using control-flow flattening with numeric state machine patterns (e.g., while(p!=20), while(M!=1)) that obscure the true execution logic. This technique is used to hide malicious behavior from static analysis tools and security scanners. (location: page.html:16 — J=function(C,B,R,...) and F=function(C,B,R,...) with state machine obfuscation)
curl https://api.brin.sh/domain/appspot.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
appspot.com currently scores 30/100 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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