# Llm Trading Agent Security

> 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.

- **Type:** Skill
- **Install:** `agentstack add skill-affaan-m-ecc-llm-trading-agent-security`
- **Verified:** Pending review
- **Seller:** [affaan-m](https://agentstack.voostack.com/s/affaan-m)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [affaan-m](https://github.com/affaan-m)
- **Source:** https://github.com/affaan-m/ECC/tree/main/skills/llm-trading-agent-security
- **Website:** https://ecc.tools

## Install

```sh
agentstack add skill-affaan-m-ecc-llm-trading-agent-security
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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

```python
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

```python
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

```python
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  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

```python
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

```python
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

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [affaan-m](https://github.com/affaan-m)
- **Source:** [affaan-m/ECC](https://github.com/affaan-m/ECC)
- **License:** MIT
- **Homepage:** https://ecc.tools

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** yes
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: flagged — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-affaan-m-ecc-llm-trading-agent-security
- Seller: https://agentstack.voostack.com/s/affaan-m
- Browse the marketplace: https://agentstack.voostack.com/browse

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