Reasoning Enhances Robustness to Prompt Injection in LLM-Based Consensus
Researchers investigated how large language models used for digital democracy consensus generation are vulnerable to prompt-injection attacks. They tested different models and found that attacks manipulating viewpoints succeed when opinions are finely balanced. A defense pipeline combining injection detection, structured opinion mapping, and reinforcement learning significantly reduces these directional failures when the underlying consensus has a clear positive or negative bias.