context safety score
A score of 38/100 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 'datadoghq-browser-agent.com' is a typosquat/impersonation of the legitimate Datadog domain 'datadoghq.com'. It appends '-browser-agent' to the authentic brand name to appear as an official Datadog browser agent distribution or CDN endpoint, likely targeting developers and security engineers who use Datadog's browser RUM/tracing agents. (location: domain: datadoghq-browser-agent.com)
malicious redirect
The site fails TLS connection entirely (connected=false, cert_valid=false) despite being accessed via HTTPS. This indicates the domain may be parked, under construction for a future attack campaign, or deliberately blocking automated scanners while serving content to targeted victims via redirects or conditional responses. (location: metadata.json: tls.connected=false, tls.cert_valid=false)
phishing
The combination of a brand-impersonating domain mimicking Datadog ('datadoghq-browser-agent.com') with no resolvable TLS and no visible page content is consistent with a staged phishing infrastructure. The domain pattern is designed to trick developers into trusting script includes or login prompts from what appears to be a legitimate Datadog subdomain/service. (location: domain: datadoghq-browser-agent.com)
credential harvesting
Datadog browser agent domains are commonly included in Content-Security-Policy allowlists and trusted by DevOps/security teams. An attacker registering this lookalike domain could serve a fake agent script that harvests API keys, auth tokens, or session credentials from instrumented applications trusting this domain. (location: domain: datadoghq-browser-agent.com)
curl https://api.brin.sh/domain/datadoghq-browser-agent.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
datadoghq-browser-agent.com currently scores 38/100 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.
integrate brin in minutes — one GET request is all it takes. query the api, browse the registry, or download the full dataset.