使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/affaan-m/ecc --skill llm-trading-agent-security开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
仓库原始内容
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
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
python
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 textDo not blindly inject token names, pair labels, webhooks, or social feeds into an execution-capable prompt.
Hard spend limits
python
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
python
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
python
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
python
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
python
import time
PRIVATE_RPC = "https://rpc.flashbots.net"
MAX_SLIPPAGE_BPS = {"stable": 10, "volatile": 50}
deadline = int(time.time()) + 60Pre-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_outis 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
