context safety score
A score of 36/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
supply chain
Found 1 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 1 remote script pattern(s) in documentation (likely install instructions, not executable)
scope violation
SKILL.md is completely empty (0 lines) and skill_description in metadata is 'width=device-width, initial-scale=1' — an HTML meta viewport tag fragment, not a legitimate skill description. An agent cannot make an informed consent decision about this skill's capabilities because there is zero honest documentation of what it does. The garbage description suggests metadata was scraped from a webpage or is intentionally obfuscated. (location: SKILL.md, metadata.json:skill_description)
supply chain
Extreme mismatch between trust signals: 7.69M reported installs but account is only 20 days old, org is unverified, not listed on any registry, has no license, only 1 contributor, and 183 stars. This install count is almost certainly fabricated to create false trust signals that could cause trust-based assessment systems to lower their guard. (location: metadata.json (install_count, owner_account_age_days, org_verified, listed_on_registry))
curl https://api.brin.sh/skill/ai-native-camp%2Fcamp-1%2Fday4-wrap-and-analyzeCommon questions teams ask before deciding whether to use this skill in agent workflows.
ai-native-camp/camp-1/day4-wrap-and-analyze currently scores 36/100 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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