Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs

• URL: http://arxiv.org/abs/2509.04615v1
• Date: Thu, 04 Sep 2025 18:59:07 GMT
• Title: Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs
• Authors: Brennen Hill, Surendra Parla, Venkata Abhijeeth Balabhadruni, Atharv Prajod Padmalayam, Sujay Chandra Shekara Sharma,
• Abstract summary: Large Language Models (LLMs) have introduced critical security challenges.<n> adversarial actors can manipulate input prompts to cause significant harm and circumvent safety alignments.<n>This survey aims to inform the research community's efforts in building the next generation of secure LLMs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of Large Language Models (LLMs) has introduced critical security challenges, where adversarial actors can manipulate input prompts to cause significant harm and circumvent safety alignments. These prompt-based attacks exploit vulnerabilities in a model's design, training, and contextual understanding, leading to intellectual property theft, misinformation generation, and erosion of user trust. A systematic understanding of these attack vectors is the foundational step toward developing robust countermeasures. This paper presents a comprehensive literature survey of prompt-based attack methodologies, categorizing them to provide a clear threat model. By detailing the mechanisms and impacts of these exploits, this survey aims to inform the research community's efforts in building the next generation of secure LLMs that are inherently resistant to unauthorized distillation, fine-tuning, and editing.

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