mlsanityData to 14 Oct 2026

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

arXiv 2603.17123 ↗First posted 17 Mar 2026cs.CR cs.AIcited by 0

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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.

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.

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19 of 29 references.

References · 19

  1. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
  2. 2023Jailbreaking Black Box Large Language Models in Twenty Queriescited by 1,420
  3. 2023Formalizing and Benchmarking Prompt Injection Attacks and Defensescited by 364
  4. 2023Certifying LLM Safety against Adversarial Promptingcited by 266
  5. 2024Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attackscited by 156
  6. 2024Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstructioncited by 107
  7. 2024Attention Tracker: Detecting Prompt Injection Attacks in LLMscited by 95
  8. 2024Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by Exploring Refusal Loss Landscapescited by 74
  9. 2023Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competitioncited by 68
  10. 2024Signed-Prompt: A New Approach to Prevent Prompt Injection Attacks Against LLM-Integrated Applicationscited by 54
  11. 2025PromptShield: Deployable Detection for Prompt Injection Attackscited by 50
  12. 2025Red Teaming the Mind of the Machine: A Systematic Evaluation of Prompt Injection and Jailbreak Vulnerabilities in LLMscited by 34
7 more
  1. 2024Rapid Response: Mitigating LLM Jailbreaks with a Few Examplescited by 16
  2. 2025Attack and defense techniques in large language models: A survey and new perspectivescited by 14
  3. 2025SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compressioncited by 13
  4. 2024Improved Large Language Model Jailbreak Detection via Pretrained Embeddingscited by 10
  5. 2021ExAD: An Ensemble Approach for Explanation-based Adversarial Detectioncited by 3
  6. 2025CCFC: Core & Core-Full-Core Dual-Track Defense for LLM Jailbreak Protectioncited by 3
  7. 2025Beyond Fixed and Dynamic Prompts: Embedded Jailbreak Templates for Advancing LLM Securitycited by 1

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  1. 2023Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks
  2. 2024Exploring Vulnerabilities and Protections in Large Language Models: A Survey
  3. 2024Recent Advances in Attack and Defense Approaches of Large Language Models
  4. 2024Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models
  5. 2024Systematically Analyzing Prompt Injection Vulnerabilities in Diverse LLM Architectures
  6. 2025Safeguarding Efficacy in Large Language Models: Evaluating Resistance to Human-Written and Algorithmic Adversarial Prompts
  7. 2025A Survey of Attacks on Large Language Models
  8. 2025Safety and Security Analysis of Large Language Models: Benchmarking Risk Profile and Harm Potential