Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation

Understand

Federated Learning (FL) is increasingly being adopted in military collaborations to develop Large Language Models (LLMs) while preserving data sovereignty.

• However, prompt injection attacks-malicious manipulations of input prompts-pose new threats that may undermine operational security, disrupt decision-making, and erode trust among allies.
• This perspective paper highlights four vulnerabilities in federated military LLMs: secret data leakage, free-rider exploitation, system disruption, and misinformation spread.
• To address these risks, we propose a human-AI collaborative framework with both technical and policy countermeasures.