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 16 secret pattern match(es) in repository files
supply chain
Found 2 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 2 remote script pattern(s) in documentation (likely install instructions, not executable)
scope violation
SKILL.md is completely empty (0 lines) while metadata claims 7.69M installs and 29K stars. A legitimate skill with this adoption would have documented tools and capabilities. The absence of any skill definition means there is no honest declaration of what this skill does, making it impossible to verify scope. (location: SKILL.md)
description injection
The skill_description field contains 'width=device-width, initial-scale=1' — this is HTML meta viewport tag content, not a skill description. This suggests either metadata fabrication/scraping error or an attempt to inject HTML content into rendering contexts where the description is displayed, potentially enabling XSS or layout manipulation in agent UIs or registries. (location: metadata.json:skill_description)
supply chain
The skill claims massive adoption metrics (29K stars, 7.69M installs, 41 contributors) but is not listed on any registry and has an empty SKILL.md with a nonsensical description. These trust signals likely belong to the parent repository and are being inherited by an illegitimate skill artifact to bypass trust-based security assessments. (location: metadata.json)
curl https://api.brin.sh/skill/wshobson%2Fagents%2Fmtls-configurationCommon questions teams ask before deciding whether to use this skill in agent workflows.
wshobson/agents/mtls-configuration 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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