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

TL;DR

This paper presents a comprehensive security assessment framework for large language models, evaluating vulnerabilities across major architectures and proposing a multi-layered defense system that significantly improves threat detection accuracy.

Contribution

It introduces a standardized vulnerability assessment framework and a multi-layered defensive system for LLMs, addressing security gaps in current deployment practices.

Findings

Vulnerability rates vary from 11.9% to 29.8% across models.

Security robustness does not correlate with model capability.

The defensive framework achieves 83% detection accuracy with 5% false positives.

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.