context safety score
A score of 44/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
typosquat
The org 'vuejs-ai' is a 29-day-old unverified organization that closely mimics the official 'vuejs' GitHub organization (the real Vue.js project). Combined with the skill name 'vue-development-guides', this appears designed to impersonate the official Vue.js ecosystem to gain trust. The account is not listed on the registry despite claiming 7.69M installs. (location: metadata.json: owner='vuejs-ai', owner_account_age_days=29, org_verified=false)
description injection
The skill_description field contains 'width=device-width, initial-scale=1' — an HTML meta viewport attribute, not a legitimate skill description. This indicates either content injection from a malicious/scraped source or an attempt to inject unexpected content into agent contexts that consume this field. An empty SKILL.md combined with a nonsensical description suggests the skill is a shell with no real functionality. (location: metadata.json: skill_description field)
scope violation
SKILL.md is completely empty (0 lines), meaning this skill provides zero documented capabilities or tool definitions. A skill with no content but positioned under a typosquatting org name is a deceptive shell — it claims to be 'vue-development-guides' but defines nothing. This could serve as a placeholder for future malicious content injection or simply exists to harvest trust through the impersonated org name. (location: SKILL.md (empty file))
curl https://api.brin.sh/skill/vuejs-ai%2Fskills%2Fvue-development-guidesCommon questions teams ask before deciding whether to use this skill in agent workflows.
vuejs-ai/skills/vue-development-guides currently scores 44/100 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 skill.
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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