Researchers studied the vulnerability of multi-agent trading systems built on large language models (LLMs) to black-box, input-only attacks. They found that even simple attacks can degrade risk-return profiles and reduce Sharpe ratios. However, they also discovered that suitably designed multi-agent topologies and coordinator prompts can improve average robustness under identical poisoning budgets.