context safety score
A score of 37/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
credential exposure
Found 1 secret pattern match(es) in repository files
supply chain
Found 5 unexpected binary file(s) in source repository
supply chain
Extreme mismatch between install count (7.69M) and community signals (16 stars, 1 fork, 1 contributor, not listed on registry, no license, unverified owner). The skill_description field contains an HTML viewport meta tag ('width=device-width, initial-scale=1') instead of an actual description, indicating metadata corruption or manipulation. No actual source code exists in the repository — SKILL.md documents extensive Python APIs (scripts/ocr_processor.py) but no implementation files are present. This is a documentation-only package from an unverified individual account with artificially inflated trust signals. (location: metadata.json, repository root)
typosquat
Repository named 'chatgpt-skills' under an unverified individual account (dkyazzentwatwa) piggybacks on the ChatGPT brand to appear authoritative. Combined with zero actual code, inflated install metrics, and a corrupted skill_description field, this suggests the package is designed to attract installs through brand association rather than legitimate functionality. (location: metadata.json (full_name: dkyazzentwatwa/chatgpt-skills))
curl https://api.brin.sh/skill/dkyazzentwatwa%2Fchatgpt-skills%2Focr-document-processorCommon questions teams ask before deciding whether to use this skill in agent workflows.
dkyazzentwatwa/chatgpt-skills/ocr-document-processor 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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