AI and LLMs in DeFi: Research, Automation, and Risk Controls
Design safer AI-assisted DeFi workflows with verified data, bounded tool access, transaction simulation, prompt-injection defenses, and human review.
Read the guide →Probabilistic intelligence, deterministic controls
AI and LLM workflows for safer DeFi research and automation
AI can summarize governance, explain simulations, monitor positions, and prepare transactions. It can also hallucinate, mishandle units, and follow hostile instructions. Safe systems give models context and narrow authority—not keys and unlimited discretion.
Use models for interpretation
LLMs are strong at organizing language and weak as authoritative databases. Retrieve balances, prices, contract addresses, and transaction state from deterministic primary sources, then let the model explain the evidence.
Keep source URLs, timestamps, and raw data so conclusions can be reviewed when protocols change.
Bound every tool
Start read-only. Add transaction preparation only for approved chains, contracts, assets, amounts, and slippage. Place an independent policy engine and simulation between the model and the signer.
Never place seed phrases or private keys in model context. Use separate, low-value wallets for automation and keep treasury assets outside agent reach.
Test hostile conditions
Evaluate counterfeit tickers, stale prices, malicious retrieved text, failed simulations, bridge outages, wrong chain IDs, extreme decimals, and sudden liquidity loss.
A system should fail closed when data conflicts or a destination is unverified. Confidence in the wording is not a security control.
Go deeper
Related field guides.
Long-form explanations that expand the network, market, token, and risk layers introduced above.
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Write it down before you enter: asset, network, contract, dependency, liquidity, and the action you will take when assumptions change.