context safety score
A score of 46/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
supply chain
Found 2 unexpected binary file(s) in source repository
typosquat
Skill named 'angular-best-practices' from unverified 152-day-old org 'boise-state-development' (mimicking Boise State University affiliation) with fabricated trust signals: claims 7.69M installs but has only 4 stars, 3 forks, is not listed on any registry, and org is unverified. The generic authoritative name is designed to attract installs through false credibility. (location: metadata.json (skill_name, owner, install_count, stars))
scope violation
SKILL.md is completely empty (0 lines) providing no capability description whatsoever, while skill_description in metadata.json contains 'width=device-width, initial-scale=1' — an HTML viewport meta tag, not a skill description. This indicates the metadata was scraped from a webpage rather than authored legitimately. An agent installing this skill would have no way to understand what capabilities it grants, yet it would be loaded into the agent's context. (location: SKILL.md (empty), metadata.json (skill_description))
curl https://api.brin.sh/skill/boise-state-development%2Fagentcore-public-stack%2Fangular-best-practicesCommon questions teams ask before deciding whether to use this skill in agent workflows.
boise-state-development/agentcore-public-stack/angular-best-practices currently scores 46/100 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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