ホーム/セキュリティ/llm-trading-agent-security
L

llm-trading-agent-security

by @affaan-mv
4.4(8)

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。

securityblockchaintradingauditdefiGitHub
インストール方法
npx skills add https://github.com/affaan-m/everything-claude-code --skill llm-trading-agent-security
compare_arrows

Before / After 効果比較

1 组
使用前

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。

使用後

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。

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

ユーザーレビュー (0)

レビューを書く

効果
使いやすさ
ドキュメント
互換性

レビューなし

統計データ

インストール数4.3K
評価4.4 / 5.0
バージョン
更新日2026年8月9日
比較事例1 件

ユーザー評価

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

この Skill を評価

0.0

対応プラットフォーム

🔧Claude Code

タイムライン

作成2026年4月17日
最終更新2026年8月9日
🎁 Agent 知識カード
アンケート