Is fusionauth-develop1.databox.com safe?

cautionmedium confidence
56/100

context safety score

A score of 56/100 indicates minor risk signals were detected. The entity may be legitimate but has characteristics that warrant attention.

identity
100
behavior
100
content
17
graph
62

6 threat patterns detected

high

malicious redirect

JavaScript on-load redirect silently sends users from fusionauth-develop1.databox.com (FusionAuth login page) to https://app.databox.com before the page fully renders, bypassing the displayed login form entirely. This cross-origin redirect is injected at the very top of the HTML outside any normal page structure. (location: page.html:1-5)

high

brand impersonation

The page presents itself with the title 'FusionAuth' and uses FusionAuth branding (logo, CSS classes, JS from FusionAuth paths) while simultaneously loading Databox favicons, Databox CDN fonts, Databox logos, and app.databox.com assets. This dual-brand mixing on a subdomain (fusionauth-develop1.databox.com) impersonates FusionAuth identity infrastructure to lend legitimacy to the Databox login flow. (location: page.html:9,21-25,3915,3918)

high

credential harvesting

A fully styled login form (email, password inputs, 'remember me', forgot password, OAuth buttons for Google, LinkedIn, Facebook, SSO) is constructed entirely via CSS referencing #login-form and #login-form input[type='password'], yet the HTML body contains an empty #login-form div with no actual form elements. Combined with the JS redirect, this suggests the page is a staging/bait shell that either dynamically injects a credential form or redirects after session setup—consistent with a credential-harvesting relay pattern between FusionAuth and Databox. (location: page.html:3022-3928)

medium

malicious redirect

Two image URL references use malformed protocol strings ('https:app.databox.com/images/signin-google-logo.png' and 'https:app.databox.com/images/signin-sso-logo.png') missing the '//' separator. This can cause browsers to interpret them as relative paths or trigger fallback behavior, potentially routing requests unexpectedly. (location: page.html:3183,3198)

medium

brand impersonation

LinkedIn logo is loaded from a third-party domain (brandlogos.net) and Facebook logo from Wikimedia Commons rather than official brand CDNs. Loading social login button assets from non-official domains is a common technique in phishing kits to avoid takedowns of official assets while maintaining visual authenticity. (location: page.html:3188,3193)

low

hidden content

The page-text.txt extracted visible text contains only whitespace—no human-readable content is rendered. All login UI is defined purely in CSS with no visible rendered text, meaning the page presents as blank to scrapers and some security tools while displaying a full login interface to browsers that process the embedded stylesheet. (location: page-text.txt:1-21)

API

curl https://api.brin.sh/domain/fusionauth-develop1.databox.com

FAQ: how to interpret this assessment

Common questions teams ask before deciding whether to use this domain in agent workflows.

Is fusionauth-develop1.databox.com safe for AI agents to use?

fusionauth-develop1.databox.com currently scores 56/100 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.

How should I interpret the score and verdict?

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.

How does brin compute this domain score?

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.

What do identity, behavior, content, and graph mean for this domain?

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.

Why does brin scan packages, repos, skills, MCP servers, pages, and commits?

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.

Can I rely on a safe verdict as a full security guarantee?

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.

When should I re-check before using an entity?

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.

Last Scanned

March 26, 2026

Verdict Scale

safe80–100
caution50–79
suspicious20–49
dangerous0–19

Trust Graph

Disclaimer

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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