context safety score
A score of 43/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
supply chain
Found 1 unexpected binary file(s) in source repository
typosquat
Skill 'xiaohongshu-note-analyzer' from 'softbread/xiaohongshu-doctor' has an empty SKILL.md, broken metadata (skill_description is HTML viewport meta tag content 'width=device-width, initial-scale=1'), no license, 1 star, 0 forks, and is not listed on any registry — yet claims 7.69M installs. This is a hollow placeholder occupying a plausible namespace (Xiaohongshu is a major platform) with fabricated engagement metrics, consistent with namespace squatting or typosquatting to intercept installs intended for legitimate Xiaohongshu analysis tools. (location: metadata.json, SKILL.md)
supply chain
Install count of 7,690,000 is wildly inconsistent with 1 star, 0 forks, 1 contributor, no registry listing, and an empty SKILL.md. This mismatch strongly suggests fabricated install metrics designed to create false trust signals, a common supply chain attack vector where inflated popularity convinces users/agents to trust a malicious or placeholder package. (location: metadata.json)
scope violation
The skill_description field contains 'width=device-width, initial-scale=1' — HTML viewport meta tag content rather than a legitimate skill description. This indicates the metadata was either scraped incorrectly from a web page or deliberately malformed. Combined with a completely empty SKILL.md, this skill has no legitimate functional definition, meaning any future content added could define arbitrary capabilities without users noticing a change. (location: metadata.json (skill_description field))
curl https://api.brin.sh/skill/softbread%2Fxiaohongshu-doctor%2Fxiaohongshu-note-analyzerCommon questions teams ask before deciding whether to use this skill in agent workflows.
softbread/xiaohongshu-doctor/xiaohongshu-note-analyzer currently scores 43/100 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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