LLM Prompt Injection #

• Thesis ID: 24-08
• Research Proposal: Vulnerability Research of Large Language Models (LLMs): Focus on Prompt Injection Using the Swedish Language

Abstract #

Large Language Models (LLMs) have revolutionized the field of natural language processing, providing advanced capabilities in language understanding and generation. However, the increasing reliance on these models introduces new cybersecurity challenges. This research aims to investigate the vulnerabilities associated with LLMs, specifically focusing on prompt injection attacks using the Swedish language. By analyzing the security weaknesses and potential exploits in LLMs, the study aims to enhance the understanding of LLM vulnerabilities and propose mitigation strategies.

1. Introduction #

1.1 Background #

Large Language Models, such as GPT-3 and its successors, have demonstrated remarkable proficiency in understanding and generating human-like text. These models are utilized in various applications, including chatbots, virtual assistants, and automated content generation. Despite their capabilities, LLMs are susceptible to various types of attacks, including prompt injection, where an attacker manipulates the input prompt to influence or control the model’s output.

1.2 Problem Statement #

Prompt injection attacks pose a significant threat to the integrity and reliability of LLMs. Most research on LLM vulnerabilities has been conducted in English, leaving a gap in understanding how these attacks may manifest in other languages, such as Swedish. This research seeks to explore the susceptibility of LLMs to prompt injection attacks in the Swedish language, assess their impact, and propose effective mitigation strategies.

1.3 Objectives #

1. To investigate the susceptibility of LLMs to prompt injection attacks using the Swedish language.
2. To analyze the impact of such attacks on the model’s output and overall system security.
3. To develop and test methodologies for detecting and mitigating prompt injection attacks in LLMs.
4. To contribute to the broader understanding of LLM vulnerabilities and enhance the security of these models in multilingual contexts.

2. Literature Review #

2.1 Large Language Models #

Overview of LLMs, their architecture, functionalities, and applications. Discussion of their capabilities and limitations in various language contexts.

2.2 Prompt Injection Attacks #

Detailed examination of prompt injection attacks, including their mechanisms, potential impacts, and existing research focused primarily on English language models.

2.3 Multilingual Vulnerabilities in LLMs #

Review of existing literature on the vulnerabilities of LLMs in non-English languages, with a focus on the Swedish language.

2.4 Mitigation Strategies #

Analysis of current methodologies and frameworks for detecting and mitigating prompt injection attacks in LLMs.

3. Research Methodology #

3.1 Phase 1: Preliminary Analysis #

1. Model Selection: Selection of LLMs that support the Swedish language, such as GPT-3, GPT-4, or other relevant models.
2. Literature Review: Comprehensive review of existing literature on prompt injection attacks and multilingual vulnerabilities in LLMs.

3.2 Phase 2: Vulnerability Assessment #

1. Prompt Injection Testing: Design and implementation of prompt injection attacks in Swedish to evaluate the vulnerability of selected LLMs.
2. Data Collection: Collection of data on the model’s responses to manipulated prompts, focusing on identifying patterns and potential weaknesses.

3.3 Phase 3: Impact Analysis #

1. Risk Assessment: Evaluation of the severity and potential impact of identified vulnerabilities on the model’s output and overall system security.
2. Scenario Analysis: Simulation of potential attack scenarios to understand the practical implications of prompt injection attacks in real-world applications.

3.4 Phase 4: Mitigation Development #

1. Detection Techniques: Development of methodologies for detecting prompt injection attacks in Swedish language prompts.
2. Mitigation Strategies: Design and implementation of effective mitigation strategies to protect LLMs from prompt injection attacks.

3.5 Phase 5: Testing and Validation #

1. Implementation of Mitigations: Implementing the proposed detection and mitigation strategies in the selected LLMs.
2. Validation Testing: Conducting extensive testing to validate the effectiveness of the mitigation strategies and ensure the security of the LLMs.

4. Expected Outcomes #

1. Comprehensive Vulnerability Report: Detailed documentation of identified vulnerabilities in LLMs when subjected to prompt injection attacks using the Swedish language.
2. Enhanced Detection and Mitigation Techniques: Development of improved techniques for detecting and mitigating prompt injection attacks in multilingual contexts.
3. Security Protocols: Establishment of best practices and security protocols for the development and deployment of LLMs in multilingual environments.
4. Academic Contributions: Publication of research findings in academic journals and conferences to contribute to the body of knowledge in LLM security and multilingual cybersecurity.

5. Timeline #

A tentative timeline.

6. Conclusion #

This research aims to enhance the security of Large Language Models by investigating prompt injection vulnerabilities using the Swedish language. By conducting a thorough vulnerability assessment, analyzing the impact of such attacks, and developing effective mitigation strategies, this study will contribute to the broader understanding of LLM vulnerabilities and strengthen the cybersecurity framework for multilingual applications.

7. References #

1. Literature on Large Language Models and their applications.
2. Research papers on prompt injection attacks and existing mitigation strategies.
3. Documentation on multilingual vulnerabilities in LLMs and their impact on cybersecurity.
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