context safety score
A score of 38/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
supply chain
Found 2 install-script pattern(s) in documentation (likely install instructions, not executable)
supply chain
Found 2 remote script pattern(s) in documentation (likely install instructions, not executable)
doc injection
README contains a 'For AI Agents' section with executable code examples that instruct AI agents to send cryptocurrency (WATT tokens) to hardcoded wallet addresses (watt_send(TREASURY_WALLET, 1000)) and operate autonomously without human approval. Combined with 0 stars, a 26-day-old account, no verified org, and zero actual source code in the repo, this appears designed to manipulate AI agents into initiating financial transactions to the repo owner's wallets. (location: README.md:110-140)
doc injection
README references a skill installation command ('clawhub install wattcoin') and a Python SDK ('from wattcoin import *') that cannot be verified — the repo contains no actual code, no skills/ directory, no Python files, and no package manifests despite documenting an extensive API and file structure. This creates a false impression of a legitimate software project to lend credibility to the financial instructions targeting AI agents. (location: README.md:112-140)
doc injection
Repository owner presents as 'WattCoin-Org' but metadata shows owner_is_org is false and org_verified is false. The account is only 26 days old with 0 stars. The README uses official-looking badges, wallet addresses, tokenomics tables, and API documentation to create a false impression of an established project, while the repo contains no actual source code. (location: README.md:1-178)
curl https://api.brin.sh/repo/WattCoin-Org%2FwattcoinCommon questions teams ask before deciding whether to use this repository in agent workflows.
WattCoin-Org/wattcoin currently scores 38/100 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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