llm-trading-agent-security
AI取引エージェントのセキュリティ脅威モデルを監査し、インジェクション攻撃や権限乱用による資産損失を防止します。
npx skills add https://github.com/affaan-m/everything-claude-code --skill llm-trading-agent-securityBefore / After 効果比較
1 组トランザクションプロキシコードを手動でレビューし、個人の経験に依存してセキュリティ脆弱性を特定するため、インジェクションポイントや権限バイパスの問題を見落としやすく、1つのプロキシシステムの監査に2-3日かかり、依然として高リスクの脆弱性が存在します。
トランザクションプロキシの脅威モデルを体系的に評価し、インジェクション攻撃面、権限境界、トランザクション署名ロジックを自動的に検出し、1時間で詳細なセキュリティレポートと修正チェックリストを生成します。
llm-trading-agent-security
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
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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
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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
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Transactions are simulated before send
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min_amount_outis mandatory -
Circuit breakers halt on drawdown or invalid state
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Keys come from env or a secret manager, never code or logs
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Private mempool or protected routing is used when appropriate
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Slippage and deadlines are set per strategy
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All agent decisions are audit-logged, not just successful sends
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