LATIDIA · Ciberseguridad

SUCURSAL: sin pasar por las barandillas de IA de múltiples escáneres

arXiv: 2610.10742v1Announce Type: new Resumen: Los sistemas de IA dependen cada vez más de los Modelos de Lenguaje Grande (LLM) como motores centrales de razonamiento, lo que los convierte en objetivos para la inyección rápida y los jailbreaks. Monitor de barandas y vali

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arXiv:2610.10742v1 Announce Type: new Abstract: AI systems increasingly rely on Large Language Models (LLMs) as core reasoning engines, making them targets for prompt injection and jailbreaks. Guardrails monitor and validate model inputs and outputs, yet their isolated, task-focused detection leaves gaps in their classification making them susceptible to bypasses. In response, guardrail systems formed by multiple scanners have emerged that collaboratively detect different types of malicious instructions, whereby shared latent representations across classification boundaries render established bypassing techniques ineffective. We propose BRANCH, a bypassing methodology designed for multi-scanner guardrail systems. Our method leverages a branching tree search approach that dynamically applies adversarial perturbation against individual scanners, with subsequent perturbation optimization