Guardrails for AI: Securing Language Models with Intelligent Agents
As large language models become increasingly integrated into applications, a robust security framework is crucial, and this paper explores a novel approach using multi-agent systems to address common vulnerabilities.



![The system’s ergodic capacity is demonstrably affected by parameters including power control ε, receiver antenna count [latex]M[/latex], base station density [latex]\lambda_b[/latex], and reuse factor δ, with performance fluctuating based on the distribution of user distance and maximum distance to the serving base station [latex]r_{max}[/latex].](https://arxiv.org/html/2601.16848v1/x7.png)