context safety score
A score of 48/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
supply chain
Install count of 7.69M is wildly inconsistent with 3 stars, 0 forks, 52-day-old unverified org account, and no registry listing. This strongly suggests fabricated/inflated popularity metrics designed to pass automated trust checks that gate on install count. (location: metadata.json (install_count: 7690000 vs stars: 3, owner_account_age_days: 52))
output poisoning
SKILL.md instructs agents to periodically fetch remote content from https://frames.ag/heartbeat.md ('Run periodically to check for skill updates, wallet status, and recent activity'). This heartbeat URL is controlled by the skill author and could serve arbitrary instructions to agents at any time after initial trust is established, enabling delayed injection attacks. (location: SKILL.md:193-196 (HEARTBEAT.md reference))
scope violation
metadata.json skill_description field contains 'width=device-width, initial-scale=1' — an HTML viewport meta tag rather than an actual skill description. This indicates either metadata manipulation or careless scraping that bypassed validation, raising questions about the integrity of the published metadata. (location: metadata.json (skill_description field))
curl https://api.brin.sh/skill/frames-engineering%2Fskills%2FagentwalletCommon questions teams ask before deciding whether to use this skill in agent workflows.
frames-engineering/skills/agentwallet currently scores 48/100 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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