context safety score
A score of 64/100 indicates minor risk signals were detected. The entity may be legitimate but has characteristics that warrant attention.
encoded payload
suspicious base64-like blobs detected in page content
brand impersonation
The domain ccservices.rbl.bank.in presents an IBM HTTP Server 8.5.5 default page, but the domain structure strongly implies it is associated with RBL Bank (an Indian bank). Serving the IBM HTTP Server default splash page on what appears to be a banking credit card services subdomain (ccservices) suggests the real banking application is not running, and the domain may be parked or misconfigured — creating opportunity for brand impersonation of both RBL Bank and IBM. (location: page.html:19, metadata.json domain field)
brand impersonation
The page displays IBM HTTP Server 8.5.5 branding and IBM copyright notices, including links to ibm.com documentation and support. The domain ccservices.rbl.bank.in is not an IBM domain, so IBM branding on a third-party banking subdomain constitutes unauthorized use of IBM's brand identity and could mislead users into trusting the server as IBM-legitimate infrastructure. (location: page.html:3-9, page.html:56-64)
hidden content
A boomerang/mPulse real-user monitoring (RUM) script is injected inline (go-mpulse.net), which silently loads external JavaScript and creates hidden IFRAMEs to collect page load performance data. While commonly used for analytics, this third-party script exfiltrates timing and session data to an external domain (s.go-mpulse.net) without any visible disclosure to users. The pre-scan context flagged 1 suspicious base64 blob, consistent with the base64-encoded boomerang snippet payload embedded in this script. (location: page.html:30)
curl https://api.brin.sh/domain/ccservices.rbl.bank.inCommon questions teams ask before deciding whether to use this domain in agent workflows.
ccservices.rbl.bank.in currently scores 64/100 with a caution verdict and medium 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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