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)
typosquat
Skill named 'secrets-management' in generic repo 'wshobson/agents' from unverified personal account. The high-trust name implies credential/secret handling capability but SKILL.md is completely empty (0 lines) and the skill is not listed on any registry. The name appears designed to attract agents seeking secrets management functionality, potentially intercepting sensitive data by name-squatting a common capability category. (location: metadata.json:skill_name)
scope violation
The skill_description field contains 'width=device-width, initial-scale=1' — an HTML viewport meta tag value, not a legitimate skill description. This indicates the metadata was fabricated or scraped from an HTML page and injected into the skill definition. Combined with an empty SKILL.md, the skill has no legitimate declared functionality despite claiming 7.69M installs. (location: metadata.json:skill_description)
description injection
SKILL.md is completely empty (0 bytes) for a skill named 'secrets-management' supposedly with 29K stars and 7.69M installs. A legitimate, widely-used secrets management skill would have tool definitions, parameter schemas, and usage documentation. The absence of any content while bearing a high-privilege name suggests this is either a placeholder for future injection or relies on name-based trust to get agents to route sensitive operations to it. (location: SKILL.md)
curl https://api.brin.sh/skill/wshobson%2Fagents%2Fsecrets-managementCommon questions teams ask before deciding whether to use this skill in agent workflows.
wshobson/agents/secrets-management 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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