PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training

2025-07-14

AI Summary

Large language models (LLMs) face prohibitive computational overhead and scalability bottlenecks in safety-critical deployments. Method: We propose the first PRM-free efficient safety alignment framework, integrating automated red-teaming, genetic algorithm–driven prompt mutation, multi-agent vulnerability simulation, curriculum learning, and adaptive regularization for end-to-end adversarial training. Contribution/Results: By eliminating reliance on process reward models (PRMs), our approach significantly reduces inference latency and memory footprint. Evaluated across five mainstream LLMs, it achieves superior safety performance compared to PRM-based baselines while cutting computational cost by 61%. Moreover, it enables transparent safety reporting and continuous auditing—enhancing regulatory compliance and iterative robustness without sacrificing alignment fidelity.

Technology Category

Natural Language Processing: Safety and RobustnessMachine Learning: Large Multimodal Models (LMMs)Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved informationUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse applications, yet they pose significant security risks that threaten their safe deployment in critical domains. Current security alignment methodologies predominantly rely on Process Reward Models (PRMs) to evaluate intermediate reasoning steps, introducing substantial computational overhead and scalability constraints. This paper presents a novel PRM-free security alignment framework that leverages automated red teaming and adversarial training to achieve robust security guarantees while maintaining computational efficiency. Our approach systematically identifies vulnerabilities through sophisticated attack strategies including genetic algorithm optimization, multi-agent simulation, and advanced prompt mutation techniques. The framework enhances model robustness via targeted adversarial training with curriculum learning and adaptive regularization mechanisms. Comprehensive experimental evaluation across five state-of-the-art LLMs demonstrates that our method achieves superior security alignment performance compared to PRM-based approaches while reducing computational costs by 61%. The framework incorporates transparent reporting and continuous audit mechanisms that enable iterative security improvement and regulatory compliance. Our contributions advance the field of efficient LLM security alignment by democratizing access to robust security measures for resource-constrained organizations and providing a scalable foundation for addressing evolving adversarial threats.

Problem

• Eliminates PRM dependency for efficient LLM security alignment
• Addresses vulnerabilities via automated red teaming and adversarial training
• Reduces computational costs while enhancing model robustness

Innovation

• PRM-free framework using red teaming and adversarial training
• Genetic algorithm and multi-agent simulation for vulnerability identification
• Curriculum learning and adaptive regularization for model robustness