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

arXiv: https://arxiv.org/abs/2603.17123 · HTML: https://mlsanity.com/paper/2603.17123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 19 of 19)

- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Jailbreaking Black Box Large Language Models in Twenty Queries](https://mlsanity.com/paper/2310.08419/) (2023)
- [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://mlsanity.com/paper/2310.12815/) (2023)
- [Certifying LLM Safety against Adversarial Prompting](https://mlsanity.com/paper/2309.02705/) (2023)
- [Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks](https://mlsanity.com/paper/2401.17263/) (2024)
- [Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction](https://mlsanity.com/paper/2402.18104/) (2024)
- [Attention Tracker: Detecting Prompt Injection Attacks in LLMs](https://mlsanity.com/paper/2411.00348/) (2024)
- [Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapes](https://mlsanity.com/paper/2403.00867/) (2024)
- [Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition](https://mlsanity.com/paper/2311.16119/) (2023)
- [Signed-Prompt: A New Approach to Prevent Prompt Injection Attacks Against LLM-Integrated Applications](https://mlsanity.com/paper/2401.07612/) (2024)
- [PromptShield: Deployable Detection for Prompt Injection Attacks](https://mlsanity.com/paper/2501.15145/) (2025)
- [Red Teaming the Mind of the Machine: A Systematic Evaluation of Prompt Injection and Jailbreak Vulnerabilities in LLMs](https://mlsanity.com/paper/2505.04806/) (2025)
- [Rapid Response: Mitigating LLM Jailbreaks with a Few Examples](https://mlsanity.com/paper/2411.07494/) (2024)
- [Attack and defense techniques in large language models: A survey and new perspectives](https://mlsanity.com/paper/2505.00976/) (2025)
- [SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression](https://mlsanity.com/paper/2506.12707/) (2025)
- [Improved Large Language Model Jailbreak Detection via Pretrained Embeddings](https://mlsanity.com/paper/2412.01547/) (2024)
- [ExAD: An Ensemble Approach for Explanation-based Adversarial Detection](https://mlsanity.com/paper/2103.11526/) (2021)
- [CCFC: Core & Core-Full-Core Dual-Track Defense for LLM Jailbreak Protection](https://mlsanity.com/paper/2508.14128/) (2025)
- [Beyond Fixed and Dynamic Prompts: Embedded Jailbreak Templates for Advancing LLM Security](https://mlsanity.com/paper/2511.14140/) (2025)

## Abstract (arXiv)

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.