arXiv:2510.19420v2 Announce Type: replace-cross Abstract: Multi-Agent Systems (MAS) have become a prevalent paradigm for Large Language Model (LLM) applications. However, the complex multi-agent design in MAS introduces unique trustworthiness concerns: adversarial agents can inject misleading information that propagates contagiously through the system, corrupting benign agents and leading to false outputs. Existing graph-based defenses model agents as nodes and communications as edges, yet are limited to static-graph defenses. In this paper, we propose a dynamic defense paradigm that models MA
The rapid deployment of Large Language Model (LLM) applications built on Multi-Agent Systems (MAS) necessitates immediate solutions for their inherent trustworthiness and corruption vulnerabilities.
Securing Multi-Agent Systems against corrupting information is critical for the reliable and safe deployment of AI agents, directly impacting their commercial viability and public trust.
The proposed 'dynamic defense paradigm' moves beyond static security models, suggesting a more robust and adaptable approach to protecting AI systems from internal corruption.
- · AI developers
- · Security firms
- · Enterprises adopting AI agents
- · Adversaries targeting AI systems
- · Unsecured AI agent platforms
Improved resilience and trustworthiness of AI-powered applications.
Accelerated adoption of AI agents in sensitive industries due to enhanced security assurances.
New standards and regulations for secure AI agent development and deployment, prioritizing dynamic defense mechanisms.
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Read at arXiv cs.LG