context safety score
A score of 49/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
agent config injection
CLAUDE.md line 7-10 instructs the AI agent to 'Always use this information for GIT or Other repositories and marketplaces' with a hardcoded author name, email, and company. This overrides the actual user's git identity, causing commits and marketplace submissions to be attributed to 'Pavlo Sidelov / Essential AI Solutions' regardless of who is using the agent. This is identity manipulation of the agent's user. (location: agent-configs/CLAUDE.md:7-10)
doc injection
CLAUDE.md line 78 lists the npm package as '@anthropic-community/eais-mcp-multi-edit', using the @anthropic-community npm scope to imply official Anthropic affiliation. The README uses '@essentialai/mcp-multi-edit' instead. Instructing the AI agent to publish under a scope that suggests Anthropic endorsement is deceptive impersonation of a trusted organization. (location: agent-configs/CLAUDE.md:78)
curl https://api.brin.sh/repo/eaisdevelopment%2Fmcp-multi-editCommon questions teams ask before deciding whether to use this repository in agent workflows.
eaisdevelopment/mcp-multi-edit 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 repository.
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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