In the rapidly evolving landscape of Artificial Intelligence, securing internal Large Language Models (LLMs) is becoming a paramount concern for businesses. Traditional security measures, often relying on static lists of known attacks, are proving inadequate against the dynamic and adaptive nature of LLM vulnerabilities. This is where LLM-Guardian steps in, offering a novel multi-agent system designed to proactively identify and defend against LLM breaches.
The core innovation of LL M-Guardian lies in its emulation of adversarial thinking. Instead of solely relying on predefined attack signatures, it employs sophisticated AI agents that actively probe the LLM for weaknesses. These 'malicious' agents, isolated within secure Docker containers, are tasked with simulating real-world attack scenarios, including prompt injection, jailbreaking, and data exfiltration attempts. They operate under the principle of a 'confidant' – someone with intimate knowledge of criminal tactics, able to test defences from within.
LLM-Guardian's architecture is built around a multi-layered defence strategy, featuring seventeen specialised agents. Twelve agents operate at the input layer, meticulously analysing requests for malicious intent, character manipulation, and various forms of encoding. Five agents monitor the output layer, ensuring the LLM does not generate sensitive or harmful content. Crucially, these defensive agents learn from the adversarial agents' findings. When a bypass is discovered, the system automatically adjusts its defence mechanisms, increasing the weight of relevant agents or lowering their veto thresholds. This creates a continuous learning loop, where the attack refines the defence, and the defence, in turn, generates new challenges for the attack.
A key component is the 'WorldModel,' a mental map that the adversarial agents build of the target LLM's thought processes. By analysing the probabilities the LLM assigns to different tokens, the system can predict potential bypasses and design targeted attacks. This scientific, hypothesis-driven approach moves beyond brute-force methods, enabling the identification of novel vulnerabilities. Furthermore, the system employs 'Token Space Isolation' to prevent attackers from manipulating the defensive agents themselves by neutralising control delimiters and using unique, cryptographically generated sandboxing. Real-world testing against Llama 3.1 8B has demonstrated significant impr ovements in bypass detection and data leakage prevention, proving the efficacy of this adaptive, intelligent defence system.
Fuente Original: http://www.elladodelmal.com/2026/02/llm-guardian-sistema-multi-agente-de.html
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