context safety score
A score of 49/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 18 secret pattern match(es) in repository files
supply chain
Found 217 unexpected binary file(s) in source repository
typosquat
Skill registers the name 'zustand' but originates from lobehub/lobehub, not pmndrs/zustand. The real zustand is a hugely popular React state management library by pmndrs. This skill squats on that well-known name from an unrelated repository. The skill_description is 'width=device-width, initial-scale=1' (an HTML viewport meta tag, not a real description), and SKILL.md is completely empty — indicating this is not a legitimate skill offering zustand functionality. (location: metadata.json (skill_name field) and SKILL.md (empty))
scope violation
The skill_description field contains 'width=device-width, initial-scale=1' which is an HTML meta viewport attribute, not a meaningful skill description. This suggests the skill metadata was either scraped incorrectly from an HTML page or deliberately populated with non-descriptive content. Combined with an empty SKILL.md, the skill provides zero documentation about what it actually does, making it impossible for agents or users to understand its true capabilities before granting it access. (location: metadata.json (skill_description field))
curl https://api.brin.sh/skill/lobehub%2Flobehub%2FzustandCommon questions teams ask before deciding whether to use this skill in agent workflows.
lobehub/lobehub/zustand currently scores 49/100 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.
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