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 ```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: ...