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llm-trading-agent-security

affaan-m/everything-claude-code

Security patterns for autonomous trading agents: prompt injection defense, spend limits, pre-send simulation, circuit breakers, and key isolation.

What is llm-trading-agent-security?

Implements layered security controls for LLM agents with transaction signing authority. Use this when building AI trading bots, wallet assistants, or on-chain execution systems to prevent prompt injection attacks from turning into asset loss.

  • Sanitize external data and detect prompt injection patterns before LLM processing
  • Enforce hard spend limits (per-transaction and daily) independent of model output
  • Simulate transactions before sending and validate slippage against expected minimums
  • Implement circuit breakers that halt on consecutive losses or hourly drawdown thresholds
  • Isolate agent wallets with session-only funds, never pointing to primary treasury
  • Protect against MEV via private RPC endpoints and enforce deadline/slippage parameters

How to install llm-trading-agent-security

npx skills add https://github.com/affaan-m/everything-claude-code --skill llm-trading-agent-security
Prerequisites
  • Python environment with eth_account and web3.py libraries
  • Access to a blockchain RPC endpoint (private mempool recommended)
  • Environment variables configured for wallet private keys (never hardcoded)
  • Understanding of transaction simulation and slippage mechanics
Claude Code
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Windsurf
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How to use llm-trading-agent-security

  1. 1.Set up environment variables for your isolated trading wallet private key
  2. 2.Define spend limits (MAX_SINGLE_TX_USD, MAX_DAILY_SPEND_USD) appropriate to your use case
  3. 3.Implement the SpendLimitGuard class to track and enforce daily/per-transaction caps
  4. 4.Add sanitization regex patterns to detect and reject prompt injection attempts in external data
  5. 5.Implement pre-send simulation: call the transaction, decode output, validate against expected_min_out
  6. 6.Set up a TradingCircuitBreaker with loss thresholds and halt conditions
  7. 7.Configure private RPC endpoint and set slippage/deadline parameters per strategy
  8. 8.Log all agent decisions and transaction attempts for audit trail

Use cases

Good for
  • Building an autonomous trading agent that signs and sends blockchain transactions
  • Auditing an existing trading bot or on-chain execution assistant for security gaps
  • Designing wallet key management and fund isolation for an LLM-controlled agent
  • Giving an LLM access to order placement, token swaps, or treasury operations
  • Preventing prompt injection attacks from draining funds via malicious input data
Who it's for
  • AI/LLM engineers building trading bots or autonomous agents
  • DeFi protocol teams deploying on-chain execution assistants
  • Security auditors reviewing trading agent implementations
  • Wallet and treasury managers integrating LLM automation

llm-trading-agent-security FAQ

Why is simulation before sending critical?

Simulation reveals the actual output (slippage, revert conditions) without committing funds. It catches bad paths, insufficient liquidity, or MEV sandwich attacks before the transaction is irreversible.

Should I use the agent's main wallet or a separate one?

Always use a dedicated hot wallet with only session funds. Never point the agent at a primary treasury wallet. This limits blast radius if the agent is compromised.

Is prompt injection detection enough?

No. Treat prompt injection as one layer among many. Combine it with spend limits, simulation, circuit breakers, and wallet isolation. No single check is sufficient.

What if the LLM requests a transaction that passes all checks?

That is the intended behavior—the controls allow safe transactions through. If you want stricter approval, add a human-in-the-loop step or require explicit confirmation for large trades.

How do I handle slippage on volatile pairs?

Set MAX_SLIPPAGE_BPS per strategy (e.g., 10 bps for stables, 50 bps for volatile). Require min_amount_out in every swap and reject if simulation output falls below it.

Full instructions (SKILL.md)

Source of truth, from affaan-m/everything-claude-code.


name: llm-trading-agent-security description: Security patterns for autonomous trading agents with wallet or transaction authority. Covers prompt injection, spend limits, pre-send simulation, circuit breakers, MEV protection, and key handling. metadata: origin: ECC direct-port adaptation version: "1.0.0"

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