UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models

• URL: http://arxiv.org/abs/2502.13141v1
• Date: Tue, 18 Feb 2025 18:59:00 GMT
• Title: UniGuardian: A Unified Defense for Detecting Prompt Injection, Backdoor Attacks and Adversarial Attacks in Large Language Models
• Authors: Huawei Lin, Yingjie Lao, Tong Geng, Tan Yu, Weijie Zhao,
• Abstract summary: Large Language Models (LLMs) are vulnerable to attacks like prompt injection, backdoor attacks, and adversarial attacks. We propose UniGuardian, the first unified defense mechanism designed to detect prompt injection, backdoor attacks, and adversarial attacks in LLMs.
• Score: 30.139590566956077
• License:
• Abstract: Large Language Models (LLMs) are vulnerable to attacks like prompt injection, backdoor attacks, and adversarial attacks, which manipulate prompts or models to generate harmful outputs. In this paper, departing from traditional deep learning attack paradigms, we explore their intrinsic relationship and collectively term them Prompt Trigger Attacks (PTA). This raises a key question: Can we determine if a prompt is benign or poisoned? To address this, we propose UniGuardian, the first unified defense mechanism designed to detect prompt injection, backdoor attacks, and adversarial attacks in LLMs. Additionally, we introduce a single-forward strategy to optimize the detection pipeline, enabling simultaneous attack detection and text generation within a single forward pass. Our experiments confirm that UniGuardian accurately and efficiently identifies malicious prompts in LLMs.

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)
• FATH: Authentication-based Test-time Defense against Indirect Prompt Injection Attacks [45.65210717380502]
  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)
  arXiv  Detail & Related papers  (2024-10-28T20:02:47Z)
• Human-Interpretable Adversarial Prompt Attack on Large Language Models with Situational Context [49.13497493053742]
  This research explores converting a nonsensical suffix attack into a sensible prompt via a situation-driven contextual re-writing. We combine an independent, meaningful adversarial insertion and situations derived from movies to check if this can trick an LLM. Our approach demonstrates that a successful situation-driven attack can be executed on both open-source and proprietary LLMs.
  arXiv  Detail & Related papers  (2024-07-19T19:47:26Z)
• Learning diverse attacks on large language models for robust red-teaming and safety tuning [126.32539952157083]
  Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe deployment of large language models. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. We propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts.
  arXiv  Detail & Related papers  (2024-05-28T19:16:17Z)
• 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)
• BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning [85.2564206440109]
  This paper reveals the threats in this practical scenario that backdoor attacks can remain effective even after defenses. We introduce the emphtoolns attack, which is resistant to backdoor detection and model fine-tuning defenses.
  arXiv  Detail & Related papers  (2023-11-20T02:21:49Z)
• Attention-Enhancing Backdoor Attacks Against BERT-based Models [54.070555070629105]
  Investigating the strategies of backdoor attacks will help to understand the model's vulnerability. We propose a novel Trojan Attention Loss (TAL) which enhances the Trojan behavior by directly manipulating the attention patterns.
  arXiv  Detail & Related papers  (2023-10-23T01:24:56Z)
• Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models [41.1058288041033]
  We propose ProAttack, a novel and efficient method for performing clean-label backdoor attacks based on the prompt. Our method does not require external triggers and ensures correct labeling of poisoned samples, improving the stealthy nature of the backdoor attack.
  arXiv  Detail & Related papers  (2023-05-02T06:19:36Z)
• Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger [48.59965356276387]
  We propose to use syntactic structure as the trigger in textual backdoor attacks. We conduct extensive experiments to demonstrate that the trigger-based attack method can achieve comparable attack performance. These results also reveal the significant insidiousness and harmfulness of textual backdoor attacks.
  arXiv  Detail & Related papers  (2021-05-26T08:54:19Z)

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.