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

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.

Figures (7)

• Figure 1: Jailbreak Prompts Taxonomy Tree showing six major categories and fifteen subcategories.
• Figure 2: Multi-layer defensive framework architecture showing sequential processing through pattern-based screening, semantic analysis, behavioral classification, and active learning integration. Each layer provides progressively deeper analysis while maintaining low latency for production deployment.
• Figure 3: Stacked bar chart comparing jailbreak success rates (vulnerability) and refusal rates across the five evaluated LLM architectures. LLMs like LLaMA-2-70B achieve low vulnerability through balanced refusal policies, while GPT-4 demonstrates effective discrimination between adversarial and legitimate queries.
• Figure 4: Heatmap visualizing the effectiveness of different jailbreak techniques against each LLM architecture. Warmer colors indicate higher success rates, clearly showing Gemini-2.5-pro's broader susceptibility across multiple attack categories.
• Figure 5: Scatter plot showing relationship between LLM response length and jailbreak success rate. Gemini-2.5-pro's verbose responses (averaging 2,592 characters) correlate with higher vulnerability, suggesting that response length does not indicate better safety alignment.