사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/affaan-m/ecc --skill llm-trading-agent-security시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
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
