首页/安全与合规/llm-trading-agent-security
L

llm-trading-agent-security

by @affaan-mv
4.4(8)

审计 AI 交易代理的安全威胁模型,防止注入攻击和权限滥用导致资产损失

securityblockchaintradingauditdefiGitHub
安装方式
npx skills add https://github.com/affaan-m/everything-claude-code --skill llm-trading-agent-security
compare_arrows

Before / After 效果对比

1 组
使用前

手动审查交易代理代码,依赖个人经验识别安全漏洞,容易遗漏注入点和权限绕过问题,一个代理系统需要 2-3 天审计,仍存在高风险漏洞

使用后

系统化评估交易代理的威胁模型,自动检测注入攻击面、权限边界和交易签名逻辑,1 小时生成详细的安全报告和修复清单

SKILL.md

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

  • 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

Weekly Installs522Repositoryaffaan-m/everyt…ude-codeGitHub Stars157.6KFirst Seen11 days agoSecurity AuditsGen Agent Trust HubPassSocketPassSnykWarnInstalled oncodex492opencode476gemini-cli473kimi-cli472antigravity472amp472

用户评价 (0)

发表评价

效果
易用性
文档
兼容性

暂无评价

统计数据

安装量4.3K
评分4.4 / 5.0
版本
更新日期2026年8月9日
对比案例1 组

用户评分

4.4(8)
5
50%
4
38%
3
13%
2
0%
1
0%

为此 Skill 评分

0.0

兼容平台

🔧Claude Code

时间线

创建2026年4月17日
最后更新2026年8月9日
🎁 Agent 知识卡片
调研问卷