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.
- Kind
- Skill
- Works with
- Claude Code, Codex
- Source
- affaan-m/ECC @ d3b8a3e90890
- Path
- skills/llm-trading-agent-security/SKILL.md
- License
- MIT
- Jobs
- Guardrails, Review
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# 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
```
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