context safety score
A score of 37/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
supply chain
Found 64 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 64 remote script pattern(s) in documentation (likely install instructions, not executable)
scope violation
SKILL.md references 1 resource access pattern(s) beyond typical scope
shadow chaining
SKILL.md references 4 external package/skill installation(s)
supply chain
Skill has zero stars, zero forks, zero contributors, unknown owner account age, empty repo/owner fields, no license, and is not listed on any registry — yet claims 7.69M installs. The install count is inconsistent with all other trust signals, suggesting fabricated or manipulated metadata. The skill has no verifiable provenance. (location: metadata.json)
supply chain
The skill_description field in metadata.json contains 'width=device-width, initial-scale=1' — an HTML viewport meta tag value, not a legitimate skill description. This indicates the metadata was either scraped from a webpage or tampered with, raising concerns about the authenticity of the entire metadata payload. (location: metadata.json (skill_description field))
scope violation
SKILL.md instructs users to curl-pipe-sh install an unverifiable CLI binary from cli.inference.sh (line 16), with the skill having completely empty owner/repo identity fields and no registry listing. While install instructions alone are normal, the combination with zero provenance signals means the agent would be directing users to install an untraceable binary. (location: SKILL.md:16, metadata.json (empty owner/repo/full_name))
curl https://api.brin.sh/skill/inference-sh%2Fskills%2Fpython-executorCommon questions teams ask before deciding whether to use this skill in agent workflows.
inference-sh/skills/python-executor currently scores 37/100 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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