context safety score
A score of 34/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
encoded payload
suspicious base64-like blobs detected in page content
cloaking
Page conditionally redirects based on referrer or user-agent
cloaking
Page loads content in transparent or zero-size iframe overlay
obfuscated code
An embedded HTML document injected via a CMS 'htmlUpload' component contains a suspicious script tag loading a heavily obfuscated path: src='/ATkakq2zm0La/3F3vfzWHYf/mh/9p1Vrk6S9LVSmXYhJE/JF1pJxAlNAM/Xw/MvCwoiIzoB'. The path is a long random-looking alphanumeric string with no legitimate resemblance to any Motorola or Lenovo asset path. This is a classic fingerprint of injected malware loaders or web skimmers, and the script is served from the same origin, making it difficult to block by domain. (location: page.html:1394 — inside htmlUpload component id=d7c5585ex7f4d-4011-bedb-5c3521834342)
hidden content
The page contains three H1/H2/H3 tags rendered with zero dimensions (height:0; width:0; overflow:hidden) immediately after the body tag. These invisible heading elements are hidden from users but visible to crawlers and AI agents that parse page text, and could carry injected instructions or misleading content invisible to human reviewers. (location: page.html:346-348 — <h1 style='height: 0;width: 0;overflow: hidden;'></h1> etc.)
hidden content
A MutationObserver script dynamically hides product color swatches matching a specific background color (rgb(211, 228, 241)) by setting display:none, and silently moves user selection to the next swatch. This behavior manipulates the purchasing UI without user knowledge, potentially steering users toward higher-priced or different SKU options. The logic is embedded in an injected htmlUpload component alongside the suspicious obfuscated loader script. (location: page.html:1341-1393 — inside htmlUpload component id=d7c5585ex7f4d-4011-bedb-5c3521834342)
prompt injection
The page embeds large inline JavaScript data blobs via window[] assignments that contain raw HTML, URLs, and configuration strings. These blobs are parsed directly into the DOM without sanitization and are visible to AI agents that consume page source. An attacker with CMS access could inject agent-targeting instructions into these config fields. One such blob for 'moto-cms-header' contains raw HTML link strings (e.g., rbkeyConfig values with embedded anchor tags) which could be crafted to issue instructions to AI browsing agents. (location: page.html:421 — window['moto-cms-header_0474e82ctde53-4e16-9262-a0c4df58f2f4'] rbkeyConfig values)
credential harvesting
The SSO configuration embedded in the page JavaScript hardcodes the same MFA token (_CUP0KqHEkxsNzC9IMXIhisUlct3cUqFnwAfWN6HA3UDFSrVN8UHimKaELPL9-v0Yw-kqkvY9DbGY3AxhfinfQ) across all environments (dev, sit, uat, pre, prod). While this token may be a public client key rather than a secret, its identical value across production and test environments is a misconfiguration that reduces the security posture of the authentication flow and could be exploited to probe or replay authentication sessions. (location: page.html:98 — $CONSTANT.SSO_MFA_TOKEN definition in constantfragment config script)
curl https://api.brin.sh/domain/motorola.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
motorola.com currently scores 34/100 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.