FATH: Authentication-based Test-time Defense against Indirect Prompt Injection Attacks

• URL: http://arxiv.org/abs/2410.21492v1
• Date: Mon, 28 Oct 2024 20:02:47 GMT
• Title: FATH: Authentication-based Test-time Defense against Indirect Prompt Injection Attacks
• Authors: Jiongxiao Wang, Fangzhou Wu, Wendi Li, Jinsheng Pan, Edward Suh, Z. Morley Mao, Muhao Chen, Chaowei Xiao,
• Abstract summary: Large language models (LLMs) have been widely deployed as the backbone with additional tools and text information for real-world applications. prompt injection attacks are particularly threatening, where malicious instructions injected in the external text information can exploit LLMs to generate answers as the attackers desire. This paper introduces a novel test-time defense strategy, named AuThentication with Hash-based tags (FATH)
• Score: 45.65210717380502
• License:
• Abstract: Large language models (LLMs) have been widely deployed as the backbone with additional tools and text information for real-world applications. However, integrating external information into LLM-integrated applications raises significant security concerns. Among these, prompt injection attacks are particularly threatening, where malicious instructions injected in the external text information can exploit LLMs to generate answers as the attackers desire. While both training-time and test-time defense methods have been developed to mitigate such attacks, the unaffordable training costs associated with training-time methods and the limited effectiveness of existing test-time methods make them impractical. This paper introduces a novel test-time defense strategy, named Formatting AuThentication with Hash-based tags (FATH). Unlike existing approaches that prevent LLMs from answering additional instructions in external text, our method implements an authentication system, requiring LLMs to answer all received instructions with a security policy and selectively filter out responses to user instructions as the final output. To achieve this, we utilize hash-based authentication tags to label each response, facilitating accurate identification of responses according to the user's instructions and improving the robustness against adaptive attacks. Comprehensive experiments demonstrate that our defense method can effectively defend against indirect prompt injection attacks, achieving state-of-the-art performance under Llama3 and GPT3.5 models across various attack methods. Our code is released at: https://github.com/Jayfeather1024/FATH

Related papers

• Defense Against Prompt Injection Attack by Leveraging Attack Techniques [66.65466992544728]
  Large language models (LLMs) have achieved remarkable performance across various natural language processing (NLP) tasks. As LLMs continue to evolve, new vulnerabilities, especially prompt injection attacks arise. Recent attack methods leverage LLMs' instruction-following abilities and their inabilities to distinguish instructions injected in the data content.
  arXiv  Detail & Related papers  (2024-11-01T09:14:21Z)
• Aligning LLMs to Be Robust Against Prompt Injection [55.07562650579068]
  We show that alignment can be a powerful tool to make LLMs more robust against prompt injection attacks. Our method -- SecAlign -- first builds an alignment dataset by simulating prompt injection attacks. Our experiments show that SecAlign robustifies the LLM substantially with a negligible hurt on model utility.
  arXiv  Detail & Related papers  (2024-10-07T19:34:35Z)
• Defending Against Indirect Prompt Injection Attacks With Spotlighting [11.127479817618692]
  In common applications, multiple inputs can be processed by concatenating them together into a single stream of text. Indirect prompt injection attacks take advantage of this vulnerability by embedding adversarial instructions into untrusted data being processed alongside user commands. We introduce spotlighting, a family of prompt engineering techniques that can be used to improve LLMs' ability to distinguish among multiple sources of input.
  arXiv  Detail & Related papers  (2024-03-20T15:26:23Z)
• AdaShield: Safeguarding Multimodal Large Language Models from Structure-based Attack via Adaptive Shield Prompting [54.931241667414184]
  We propose textbfAdaptive textbfShield Prompting, which prepends inputs with defense prompts to defend MLLMs against structure-based jailbreak attacks. Our methods can consistently improve MLLMs' robustness against structure-based jailbreak attacks.
  arXiv  Detail & Related papers  (2024-03-14T15:57:13Z)
• ASETF: A Novel Method for Jailbreak Attack on LLMs through Translate Suffix Embeddings [58.82536530615557]
  We propose an Adversarial Suffix Embedding Translation Framework (ASETF) to transform continuous adversarial suffix embeddings into coherent and understandable text. Our method significantly reduces the computation time of adversarial suffixes and achieves a much better attack success rate to existing techniques.
  arXiv  Detail & Related papers  (2024-02-25T06:46:27Z)
• Signed-Prompt: A New Approach to Prevent Prompt Injection Attacks Against LLM-Integrated Applications [0.0]
  This paper introduces the 'Signed-Prompt' method as a novel solution for prompt injection attacks. The study involves signing sensitive instructions within command segments by authorized users, enabling the LLM to discern trusted instruction sources. Experiments demonstrate the effectiveness of the Signed-Prompt method, showing substantial resistance to various types of prompt injection attacks.
  arXiv  Detail & Related papers  (2024-01-15T11:44:18Z)
• Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection [64.67495502772866]
  Large Language Models (LLMs) are increasingly being integrated into various applications. We show how attackers can override original instructions and employed controls using Prompt Injection attacks. We derive a comprehensive taxonomy from a computer security perspective to systematically investigate impacts and vulnerabilities.
  arXiv  Detail & Related papers  (2023-02-23T17:14:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.