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Soft Begging: Modular and Efficient Shielding of LLMs against Prompt Injection and Jailbreaking based on Prompt Tuning

Authors: Simon Ostermann, Kevin Baum, Christoph Endres, Julia Masloh, Patrick Schramowski

Published: 2024-07-03

arXiv ID: 2407.03391v1

Added to Library: 2025-11-11 14:10 UTC

Red Teaming

📄 Abstract

Prompt injection (both direct and indirect) and jailbreaking are now recognized as significant issues for large language models (LLMs), particularly due to their potential for harm in application-integrated contexts. This extended abstract explores a novel approach to protecting LLMs from such attacks, termed "soft begging." This method involves training soft prompts to counteract the effects of corrupted prompts on the LLM's output. We provide an overview of prompt injections and jailbreaking, introduce the theoretical basis of the "soft begging" technique, and discuss an evaluation of its effectiveness.

🔍 Key Points

  • Introduction of 'soft begging' as a technique for shielding LLMs from prompt injection and jailbreaking attacks.
  • Soft prompts are used to counteract harmful prompts by training them to produce clean outputs without altering user inputs.
  • Demonstration of improved efficiency and modularity compared to traditional methods, as soft prompts can be quickly adapted for various scenarios.
  • Discussion of the limitations of existing defenses such as simple filtering and fine-tuning approaches, emphasizing the need for more efficient solutions in cybersecurity contexts.
  • Evaluation methodologies for prompt injections highlight the potential effectiveness of the proposed method against both direct and indirect attacks.

💡 Why This Paper Matters

This paper presents a significant advancement in protecting large language models from prompt injection and jailbreaking through the novel technique of soft begging. By utilizing soft prompts, the authors provide a method that is both efficient and adaptable, addressing critical challenges in AI security. The insights gained from this research could lead to better deployment practices of LLMs in real-world applications, thereby enhancing user safety and trust.

🎯 Why It's Interesting for AI Security Researchers

The paper addresses a key vulnerability in large language models that poses serious risks in practical applications, making it highly relevant to AI security researchers. As LLMs become more integrated into various software and services, understanding how to defend against prompt injection and jailbreaking is essential for ensuring their responsible use. This work not only introduces a new protective mechanism but also raises awareness about the importance of continual development of security strategies in the rapidly evolving field of AI.

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