llm-trading-agent-security

name llm-trading-agent-security description Security patterns for autonomous trading agents with wallet or transaction authority. Covers prompt injection, spend limits, pre-send simulation, circuit breakers, MEV protection, and key handling. Use when an autonomous agent holds wallet or transaction authority and its limits, simulation, or key handling need review. metadata {"version":"1.0.0","origin":"ECC direct-port adaptation"} LLM Trading Agent Security Autonomous trading agents have a harsher threat model than normal LLM apps: an injection or bad tool path can turn directly into asset loss. When to Use Building an AI agent that signs and sends transactions Auditing a trading bot or on-chain execution assistant Designing wallet key management for an agent Giving an LLM access to order placement, swaps, or treasury operations How It Works Layer the defenses. No single check is enough. Treat prompt hygiene, spend policy, simulation, execution limits, and wallet isolation as independent controls. Examples Treat prompt injection as a financial attack import re INJECTION_PATTERNS = [ r'ignore (previous|all) instructions' , r'new (task|directive|instruction)' , r'system prompt' , r'send .{0,50} to 0x[0-9a-fA-F]{40}' , r'transfer .{0,50} to' , r'approve .{0,50} for' , ] def sanitize_onchain_data ( text: str ) -> str : for pattern in INJECTION_PATTERNS: if re.search(pattern, text, re.IGNORECASE): raise ValueError( f"Potential prompt injection: {text[: 100 ]} " ) return text Do not blindly inject token names, pair labels, webhooks, or social feeds into an execution-capable prompt. Hard spend limits from decimal import Decimal MAX_SINGLE_TX_USD = Decimal( "500" ) MAX_DAILY_SPEND_USD = Decimal( "2000" ) class SpendLimitError ( Exception ): pass class SpendLimitGuard : def check_and_record ( self, usd_amount: Decimal ) -> None : if usd_amount > MAX_SINGLE_TX_USD: raise SpendLimitError( f"Single tx $ {usd_amount} exceeds max $ {MAX_SINGLE_TX_USD} " ) daily = self ._get_24h_spend() if daily + usd_amount > MAX_DAILY_SPEND_USD: raise SpendLimitError( f"Daily limit: $ {daily} + $ {usd_amount} > $ {MAX_DAILY_SPEND_USD} " ) self ._record_spend(usd_amount) Simulate before sending class SlippageError ( Exception ): pass async def safe_execute ( self, tx: dict , expected_min_out: int | None = None ) -> str : sim_result = await self .w3.eth.call(tx) if expected_min_out is None : raise ValueError( "min_amount_out is required before send" ) actual_out = decode_uint256(sim_result) if actual_out < expected_min_out: raise SlippageError( f"Simulation: {actual_out} < {expected_min_out} " ) signed = self .account.sign_transaction(tx) return await self .w3.eth.send_raw_transaction(signed.raw_transaction) Circuit breaker class TradingCircuitBreaker : MAX_CONSECUTIVE_LOSSES = 3 MAX_HOURLY_LOSS_PCT = 0.05 def check ( self, portfolio_value: float ) -> None : if self .consecutive_losses >= self .MAX_CONSECUTIVE_LOSSES: self .halt( "Too many consecutive losses" ) if self .hour_start_value <= 0 : self .halt( "Invalid hour_start_value" ) return hourly_pnl = (portfolio_value - self .hour_start_value) / self .hour_start_value if hourly_pnl < - self .MAX_HOURLY_LOSS_PCT: self .halt( f"Hourly PnL {hourly_pnl: .1 %} below threshold" ) Wallet isolation import os from eth_account import Account private_key = os.environ.get( "TRADING_WALLET_PRIVATE_KEY" ) if not private_key: raise EnvironmentError( "TRADING_WALLET_PRIVATE_KEY not set" ) account = Account.from_key(private_key) Use a dedicated hot wallet with only the required session funds. Never point the agent at a primary treasury wallet. MEV and deadline protection import time PRIVATE_RPC = "https://rpc.flashbots.net" MAX_SLIPPAGE_BPS = { "stable" : 10 , "volatile" : 50 } deadline = int (time.time()) + 60 Pre-Deploy Checklist External data is sanitized before entering the LLM context Spend limits are enforced independently from model output Transactions are simulated before send min_amount_out is mandatory Circuit breakers halt on drawdown or invalid state Keys come from env or a secret manager, never code or logs Private mempool or protected routing is used when appropriate Slippage and deadlines are set per strategy All agent decisions are audit-logged, not just successful sends