context safety score
A score of 20/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
supply chain
Found 31 unexpected binary file(s) in source repository
typosquat
Repository owned by 'shin-bot-litellm' (27-day-old unverified org, 2 stars) impersonates BerriAI, the legitimate LiteLLM organization. The README contains an HTML comment '<!-- mcp-name: io.github.BerriAI/litellm-mcp -->' claiming BerriAI authorship, and the git clone URL points to 'github.com/BerriAI/litellm-agent-mcp' (the real org, not this repo's actual owner). Links to official BerriAI docs create false association with the trusted LiteLLM project (50k+ stars). (location: README.md:4,60)
supply chain
README instructs users to 'pip install litellm-agent-mcp' but the repository contains zero source code — no Python files, no setup.py, no pyproject.toml. Combined with the BerriAI impersonation, this suggests a potential PyPI package name squat designed to capture users who trust the LiteLLM brand. A malicious package could be published under this name to harvest API keys (the MCP config example includes OPENAI_API_KEY and ANTHROPIC_API_KEY in env vars). (location: README.md:54)
curl https://api.brin.sh/repo/shin-bot-litellm%2Flitellm-agent-mcpCommon questions teams ask before deciding whether to use this repository in agent workflows.
shin-bot-litellm/litellm-agent-mcp currently scores 20/100 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 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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