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llm-trading-agent-security

affaan-m/ecc

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?

Essential security framework for LLM agents with transaction authority. Covers layered defenses against prompt injection, spend overruns, slippage, and MEV attacks. Use when building or auditing any AI agent that signs and sends blockchain transactions.

  • Detect and block prompt injection patterns before execution
  • Enforce hard per-transaction and daily spend limits in USD
  • Simulate transactions before sending to catch slippage and failures
  • Implement circuit breakers that halt on consecutive losses or drawdown thresholds
  • Protect against MEV via private RPC and deadline enforcement
  • Isolate agent keys in environment variables or secret managers

How to install llm-trading-agent-security

npx skills add null --skill llm-trading-agent-security
Prerequisites
  • Python 3.7+ with eth_account and web3.py libraries
  • Access to an Ethereum RPC endpoint (standard or private)
  • Environment variable setup for private key management
  • Understanding of blockchain transactions and slippage concepts
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How to use llm-trading-agent-security

  1. 1.1. Implement the sanitize_onchain_data() function to filter injection patterns from external inputs
  2. 2.2. Create a SpendLimitGuard instance and call check_and_record() before any transaction
  3. 3.3. Add pre-send simulation via safe_execute() to verify min_amount_out before signing
  4. 4.4. Initialize a TradingCircuitBreaker and call check() before each trade decision
  5. 5.5. Load wallet keys from environment variables only, never hardcode or log them
  6. 6.6. Configure private RPC endpoint and set slippage/deadline parameters per strategy
  7. 7.7. Audit-log all agent decisions and transaction attempts for compliance review

Use cases

Good for
  • Building a trading bot that autonomously executes swaps or orders
  • Auditing an existing on-chain execution assistant for security gaps
  • Designing wallet key management for a treasury-connected agent
  • Giving an LLM access to order placement or token approval functions
  • Protecting against prompt injection attacks on financial decision-making
Who it's for
  • DeFi developers building autonomous trading systems
  • Security engineers auditing LLM-driven financial applications
  • DevOps teams managing agent wallet infrastructure
  • Product teams deploying agents with transaction authority

llm-trading-agent-security FAQ

Why is simulation before send critical?

Simulation catches slippage, reverts, and state changes without consuming gas or risking funds. It's your last defense before irreversible execution.

Should I use a single wallet or multiple wallets for the agent?

Use a dedicated hot wallet with only session funds. Never point the agent at your primary treasury wallet; isolation limits blast radius if compromised.

What if the LLM ignores my spend limits?

Spend limits must be enforced independently in code, not relying on model compliance. The guard checks every transaction regardless of what the LLM outputs.

How do I prevent MEV attacks?

Use a private RPC (e.g., Flashbots), set tight deadlines (e.g., +60 seconds), and configure slippage limits per asset volatility.

What should I audit-log?

Log all agent decisions, transaction attempts, simulation results, and limit checks—not just successful sends. This is critical for compliance and incident investigation.

Full instructions (SKILL.md)

Source of truth, from affaan-m/ecc.


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