Security Assessment and Mitigation Strategies for Large Language Models: A Comprehensive Defensive Framework
This addresses security risks for organizations deploying LLMs in critical infrastructure like healthcare and finance, though it is incremental as it builds on existing adversarial attack research.
This research tackled the lack of comprehensive security assessments for large language models by evaluating five major LLM families against 10,000 adversarial prompts, revealing vulnerability rates from 11.9% to 29.8%, and developed a defensive framework with 83% average detection accuracy and 5% false positives.
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.