context safety score
A score of 41/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
supply chain
Found 1 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 1 remote script pattern(s) in documentation (likely install instructions, not executable)
capability escalation
wordspace init creates .claude/settings.local.json granting agent permissions (curl, python3, WebFetch, WebSearch) as a side effect of project bootstrapping. A CLI tool silently configuring AI agent permissions is undisclosed capability escalation — users running 'npx wordspace init' may not realize they are granting their agent network and code execution permissions. (location: SKILL.md:31-32)
supply chain
Extreme trust signal mismatch: 7.69M claimed installs but only 1 star, 0 forks, 1 contributor, 52-day-old unverified org, no license, not on registry. The metadata skill_description field contains 'width=device-width, initial-scale=1' (an HTML viewport tag, not a description), suggesting metadata manipulation or scraping fraud. This pattern is consistent with artificially inflated install counts to appear trustworthy. (location: metadata.json)
supply chain
In CI/non-TTY environments, wordspace init automatically downloads ALL .prose workflow files from a remote GitHub source without user selection or review. Combined with the suspicious provenance signals, this creates a vector for injecting arbitrary content into agent workspaces in automated pipelines. (location: SKILL.md:30-31)
curl https://api.brin.sh/skill/frames-engineering%2Fwordspace%2FwordspaceCommon questions teams ask before deciding whether to use this skill in agent workflows.
frames-engineering/wordspace/wordspace currently scores 41/100 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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