An LLM can Fool Itself: A Prompt-Based Adversarial Attack

• URL: http://arxiv.org/abs/2310.13345v1
• Date: Fri, 20 Oct 2023 08:16:46 GMT
• Title: An LLM can Fool Itself: A Prompt-Based Adversarial Attack
• Authors: Xilie Xu, Keyi Kong, Ning Liu, Lizhen Cui, Di Wang, Jingfeng Zhang, Mohan Kankanhalli,
• Abstract summary: This paper proposes an efficient tool to audit the LLM's adversarial robustness via a prompt-based adversarial attack (PromptAttack) PromptAttack converts adversarial textual attacks into an attack prompt that can cause the victim LLM to output the adversarial sample to fool itself. Comprehensive empirical results using Llama2 and GPT-3.5 validate that PromptAttack consistently yields a much higher attack success rate compared to AdvGLUE and AdvGLUE++.
• Score: 26.460067102821476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM's adversarial robustness. This paper proposes an efficient tool to audit the LLM's adversarial robustness via a prompt-based adversarial attack (PromptAttack). PromptAttack converts adversarial textual attacks into an attack prompt that can cause the victim LLM to output the adversarial sample to fool itself. The attack prompt is composed of three important components: (1) original input (OI) including the original sample and its ground-truth label, (2) attack objective (AO) illustrating a task description of generating a new sample that can fool itself without changing the semantic meaning, and (3) attack guidance (AG) containing the perturbation instructions to guide the LLM on how to complete the task by perturbing the original sample at character, word, and sentence levels, respectively. Besides, we use a fidelity filter to ensure that PromptAttack maintains the original semantic meanings of the adversarial examples. Further, we enhance the attack power of PromptAttack by ensembling adversarial examples at different perturbation levels. Comprehensive empirical results using Llama2 and GPT-3.5 validate that PromptAttack consistently yields a much higher attack success rate compared to AdvGLUE and AdvGLUE++. Interesting findings include that a simple emoji can easily mislead GPT-3.5 to make wrong predictions.

Related papers

• Pseudo-Conversation Injection for LLM Goal Hijacking [3.574664325523221]
  In goal hijacking, an attacker typically appends a carefully crafted malicious suffix to the user's prompt. We introduce a novel goal hijacking attack method called Pseudo-Conversation Injection. We propose three Pseudo-Conversation construction strategies: Targeted Pseudo-Conversation, Universal Pseudo-Conversation, and Robust Pseudo-Conversation.
  arXiv  Detail & Related papers  (2024-10-31T06:58:34Z)
• 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)
• 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)
• DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers [74.7446827091938]
  We introduce an automatic prompt textbfDecomposition and textbfReconstruction framework for jailbreak textbfAttack (DrAttack) DrAttack includes three key components: (a) Decomposition' of the original prompt into sub-prompts, (b) Reconstruction' of these sub-prompts implicitly by in-context learning with semantically similar but harmless reassembling demo, and (c) a Synonym Search' of sub-prompts, aiming to find sub-prompts' synonyms that maintain the original intent while
  arXiv  Detail & Related papers  (2024-02-25T17:43:29Z)
• 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)
• Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information [67.78183175605761]
  Large Language Models are susceptible to adversarial prompt attacks. This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs. We introduce a novel approach to detecting adversarial prompts at a token level.
  arXiv  Detail & Related papers  (2023-11-20T03:17:21Z)
• SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks [99.23352758320945]
  We propose SmoothLLM, the first algorithm designed to mitigate jailbreaking attacks on large language models (LLMs) Based on our finding that adversarially-generated prompts are brittle to character-level changes, our defense first randomly perturbs multiple copies of a given input prompt, and then aggregates the corresponding predictions to detect adversarial inputs.
  arXiv  Detail & Related papers  (2023-10-05T17:01:53Z)
• Certifying LLM Safety against Adversarial Prompting [75.19953634352258]
  Large language models (LLMs) are vulnerable to adversarial attacks that add malicious tokens to an input prompt. We introduce erase-and-check, the first framework for defending against adversarial prompts with certifiable safety guarantees.
  arXiv  Detail & Related papers  (2023-09-06T04:37:20Z)
• Detecting Language Model Attacks with Perplexity [0.0]
  A novel hack involving Large Language Models (LLMs) has emerged, exploiting adversarial suffixes to deceive models into generating perilous responses. A Light-GBM trained on perplexity and token length resolved the false positives and correctly detected most adversarial attacks in the test set.
  arXiv  Detail & Related papers  (2023-08-27T15:20:06Z)

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.