context safety score
A score of 48/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
typosquat
Owner 'vuejs-ai' is a 29-day-old unverified organization mimicking the official Vue.js GitHub organization ('vuejs'). The name is designed to imply official Vue.js affiliation. Combined with not being listed on the skills.sh registry despite claiming 7.69M installs, this strongly indicates impersonation of the Vue.js project. (location: metadata.json: owner='vuejs-ai', owner_account_age_days=29, org_verified=false, listed_on_registry=false)
supply chain
The skill_description field contains 'width=device-width, initial-scale=1' — an HTML meta viewport attribute value, not a legitimate skill description. This is either metadata corruption or an attempt at HTML/content injection into systems that render the skill description without sanitization. (location: metadata.json: skill_description field)
shadow chaining
SKILL.md directs agents to 'use vue-best-practices' — another skill presumably from the same impersonating 'vuejs-ai' org. Given the typosquatting context, this cross-reference could chain trust from this skill to additional malicious skills controlled by the same actor. (location: SKILL.md:7)
curl https://api.brin.sh/skill/vuejs-ai%2Fskills%2Fvue-debug-guidesCommon questions teams ask before deciding whether to use this skill in agent workflows.
vuejs-ai/skills/vue-debug-guides currently scores 48/100 with a suspicious 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 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.
integrate brin in minutes — one GET request is all it takes. query the api, browse the registry, or download the full dataset.