Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911138808070144 |
|---|---|
| author | Hill, Brennen Parla, Surendra Balabhadruni, Venkata Abhijeeth Padmalayam, Atharv Prajod Sharma, Sujay Chandra Shekara |
| author_facet | Hill, Brennen Parla, Surendra Balabhadruni, Venkata Abhijeeth Padmalayam, Atharv Prajod Sharma, Sujay Chandra Shekara |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04615 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs Hill, Brennen Parla, Surendra Balabhadruni, Venkata Abhijeeth Padmalayam, Atharv Prajod Sharma, Sujay Chandra Shekara Computation and Language Cryptography and Security Machine Learning 68T07, 68T50 I.2.7; I.2.6; K.6.5 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. |
| title | Breaking to Build: A Threat Model of Prompt-Based Attacks for Securing LLMs |
| topic | Computation and Language Cryptography and Security Machine Learning 68T07, 68T50 I.2.7; I.2.6; K.6.5 |
| url | https://arxiv.org/abs/2509.04615 |