Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game

• URL: http://arxiv.org/abs/2311.01011v1
• Date: Thu, 2 Nov 2023 06:13:36 GMT
• Title: Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game
• Authors: Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, Stuart Russell
• Abstract summary: This paper presents a dataset of over 126,000 prompt injection attacks and 46,000 prompt-based "defenses" against prompt injection. To the best of our knowledge, this is currently the largest dataset of human-generated adversarial examples for instruction-following LLMs. We also use the dataset to create a benchmark for resistance to two types of prompt injection, which we refer to as prompt extraction and prompt hijacking.
• Score: 86.66627242073724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Large Language Models (LLMs) are increasingly being used in real-world applications, they remain vulnerable to prompt injection attacks: malicious third party prompts that subvert the intent of the system designer. To help researchers study this problem, we present a dataset of over 126,000 prompt injection attacks and 46,000 prompt-based "defenses" against prompt injection, all created by players of an online game called Tensor Trust. To the best of our knowledge, this is currently the largest dataset of human-generated adversarial examples for instruction-following LLMs. The attacks in our dataset have a lot of easily interpretable stucture, and shed light on the weaknesses of LLMs. We also use the dataset to create a benchmark for resistance to two types of prompt injection, which we refer to as prompt extraction and prompt hijacking. Our benchmark results show that many models are vulnerable to the attack strategies in the Tensor Trust dataset. Furthermore, we show that some attack strategies from the dataset generalize to deployed LLM-based applications, even though they have a very different set of constraints to the game. We release all data and source code at https://tensortrust.ai/paper

Related papers

• Fine-tuned Large Language Models (LLMs): Improved Prompt Injection Attacks Detection [6.269725911814401]
  Large language models (LLMs) are becoming a popular tool as they have significantly advanced in their capability to tackle a wide range of language-based tasks. However, LLMs applications are highly vulnerable to prompt injection attacks, which poses a critical problem. This project explores the security vulnerabilities in relation to prompt injection attacks.
  arXiv  Detail & Related papers  (2024-10-28T00:36:21Z)
• Denial-of-Service Poisoning Attacks against Large Language Models [64.77355353440691]
  LLMs are vulnerable to denial-of-service (DoS) attacks, where spelling errors or non-semantic prompts trigger endless outputs without generating an [EOS] token. We propose poisoning-based DoS attacks for LLMs, demonstrating that injecting a single poisoned sample designed for DoS purposes can break the output length limit.
  arXiv  Detail & Related papers  (2024-10-14T17:39:31Z)
• SecAlign: Defending Against Prompt Injection with Preference Optimization [52.48001255555192]
  Adrial prompts can be injected into external data sources to override the system's intended instruction and execute a malicious instruction. We propose a new defense called SecAlign based on the technique of preference optimization. Our method reduces the success rates of various prompt injections to around 0%, even against attacks much more sophisticated than ones seen during training.
  arXiv  Detail & Related papers  (2024-10-07T19:34:35Z)
• 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)
• Formalizing and Benchmarking Prompt Injection Attacks and Defenses [59.57908526441172]
  We propose a framework to formalize prompt injection attacks. Based on our framework, we design a new attack by combining existing ones. Our work provides a common benchmark for quantitatively evaluating future prompt injection attacks and defenses.
  arXiv  Detail & Related papers  (2023-10-19T15:12:09Z)
• From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application? [4.361862281841999]
  We present a comprehensive examination of P$$ injections targeting web applications based on the Langchain framework. Our findings indicate that LLM-integrated applications based on Langchain are highly susceptible to P$$ injection attacks, warranting the adoption of robust defenses. We propose four effective defense techniques that can be integrated as extensions to the Langchain framework.
  arXiv  Detail & Related papers  (2023-08-03T19:03:18Z)
• 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.