arXiv:2605.22786v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems increasingly rely on intermediate communication to coordinate complex tasks. While most existing systems communicate through natural language, recent work shows that latent communication, particularly through transformer key-value (KV) caches, can improve efficiency and preserve richer task-relevant information. However, KV caches also encode contextual inputs, intermediate reasoning states, and agent-specific information, creating an opaque channel through which sensitive content may propaga
The increasing sophistication and interconnectedness of LLM-based multi-agent systems necessitate robust security measures for internal communication channels, which are becoming more complex and opaque.
Securing latent communication within multi-agent AI systems is critical to prevent sensitive data leakage, ensure task integrity, and establish trust in autonomous AI operations.
The development of specific 'guards' like LCGuard shifts the focus from securing external AI interactions to also securing the internal, often 'latent,' communication pathways within AI systems.
- · AI-powered enterprise solutions
- · Cybersecurity providers for AI
- · Governments developing secure AI systems
- · Malicious actors targeting AI systems
- · Organizations with inadequate AI security protocols
Enhances the overall security posture and trustworthiness of sensitive multi-agent AI deployments.
Accelerates the adoption of multi-agent AI in highly regulated or critical sectors by addressing key security concerns.
Drives the creation of new security standards and regulatory frameworks specifically for internal AI communication and data flow.
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Read at arXiv cs.LG