context safety score
A score of 42/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
tls connection failed
Could not establish TLS connection
brand impersonation
The domain 'adobestats.io' impersonates Adobe, a well-known software brand, by incorporating the 'adobe' name into a non-official TLD (.io). This is a classic typosquat/brand-abuse pattern used to deceive users and AI agents into trusting the domain as an official Adobe property. (location: domain: adobestats.io)
phishing
The domain 'adobestats.io' combines Adobe brand name with a statistics/analytics-sounding suffix, consistent with phishing infrastructure designed to harvest Adobe credentials or redirect users to fake Adobe login or product pages. TLS is not connected and cert is invalid, which is atypical for a legitimate Adobe-affiliated service. (location: domain: adobestats.io, metadata.json tls.connected=false, tls.cert_valid=false)
credential harvesting
The combination of Adobe brand impersonation via 'adobestats.io', invalid/absent TLS, and empty page content is consistent with a credential harvesting operation — the site may serve login forms or redirect flows dynamically that were not captured at scan time, or may be in a dormant/rotational phase typical of phishing kit infrastructure. (location: domain: adobestats.io, metadata.json tls.cert_valid=false, san_match=false)
malicious redirect
The domain is 809 days old with unknown hosting reputation, no TLS, and completely empty page content at scan time. This pattern is consistent with a redirect node or parked phishing domain that serves malicious redirects conditionally (e.g., based on user-agent, referrer, or geolocation), evading static scanners while actively redirecting live targets. (location: domain: adobestats.io, metadata.json hosting.reputation=Unknown, tls.connected=false)
curl https://api.brin.sh/domain/adobestats.ioCommon questions teams ask before deciding whether to use this domain in agent workflows.
adobestats.io currently scores 42/100 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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