context safety score
A score of 53/100 indicates minor risk signals were detected. The entity may be legitimate but has characteristics that warrant attention.
phishing
1 deceptive links where visible host does not match destination host
prompt injection
Hidden HTML element contains AI-targeting instructions
social engineering
The README presents this as a legitimate 'production-ready' arbitrage trading bot and instructs users to place highly sensitive credentials (RSA private key PEM for Kalshi API, Ethereum wallet private key for Polymarket) directly into a .env file. This pattern is commonly used in crypto theft campaigns to harvest private keys from users who run the bot. (location: page-text.txt:1063-1090, README section 'Configuration')
social engineering
The README includes a 'Questions? Telegram' link pointing to https://t.me/@tova_0x — an external Telegram account attributed to 'Alexei'. Directing users with live trading credentials and private keys to an unverified Telegram operator is a standard technique for follow-on credential harvesting or social engineering in crypto scam repos. (location: page.html:1742, page-text.txt:1301)
malicious redirect
The repository sidebar and README contain a direct link to https://t.me/@tova_0x (Telegram), which is the one deceptive/off-platform link flagged by Tier 2 heuristics. This redirects users off GitHub to an unverified third-party messaging account, consistent with the flagged 'deceptive link count: 1'. (location: page.html:1742)
curl https://api.brin.sh/page/github.com%2Finfraform%2Fpolymarket-kalshi-arbitrage-botCommon questions teams ask before deciding whether to use this web page in agent workflows.
github.com/infraform/polymarket-kalshi-arbitrage-bot currently scores 53/100 with a caution 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 web page.
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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