Security Assessment and Mitigation Strategies for Large Language Models: A Comprehensive Defensive Framework

2026-03-17

AI Summary

This study addresses the absence of a unified safety evaluation benchmark for current large language models (LLMs), which hinders the quantification of deployment risks in critical applications. To bridge this gap, we propose the first standardized, cross-architecture safety evaluation framework that systematically assesses the vulnerability of five representative LLMs under six categories of adversarial attacks. Furthermore, we introduce a deployable multi-layered external defense mechanism. Experimental results reveal that existing models exhibit vulnerability rates ranging from 11.9% to 29.8%, whereas our defense framework achieves an average detection accuracy of 83% with only a 5% false positive rate. Notably, the findings demonstrate no direct correlation between a model’s general capabilities and its safety robustness, offering empirical evidence and a practical solution for safer LLM deployment.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Large Language Models increasingly power critical infrastructure from healthcare to finance, yet their vulnerability to adversarial manipulation threatens system integrity and user safety. Despite growing deployment, no comprehensive comparative security assessment exists across major LLM architectures, leaving organizations unable to quantify risk or select appropriately secure LLMs for sensitive applications. This research addresses this gap by establishing a standardized vulnerability assessment framework and developing a multi-layered defensive system to protect against identified threats. We systematically evaluate five widely-deployed LLM families GPT-4, GPT-3.5 Turbo, Claude-3 Haiku, LLaMA-2-70B, and Gemini-2.5-pro against 10,000 adversarial prompts spanning six attack categories. Our assessment reveals critical security disparities, with vulnerability rates ranging from 11.9\% to 29.8\%, demonstrating that LLM capability does not correlate with security robustness. To mitigate these risks, we develop a production-ready defensive framework achieving 83\% average detection accuracy with only 5\% false positives. These results demonstrate that systematic security assessment combined with external defensive measures provides a viable path toward safer LLM deployment in production environments.

Problem

• Large Language Models
• Security Assessment
• Adversarial Attacks
• Vulnerability
• Risk Quantification

Innovation

• adversarial robustness
• large language models
• security assessment
• defensive framework
• vulnerability evaluation