LLMs are Vulnerable to Malicious Prompts Disguised as Scientific Language

• URL: http://arxiv.org/abs/2501.14073v2
• Date: Tue, 18 Feb 2025 05:02:09 GMT
• Title: LLMs are Vulnerable to Malicious Prompts Disguised as Scientific Language
• Authors: Yubin Ge, Neeraja Kirtane, Hao Peng, Dilek Hakkani-Tür,
• Abstract summary: This work reveals that many state-of-the-art LLMs are vulnerable to malicious requests hidden behind scientific language. Our experiments with GPT4o, GPT4o-mini, GPT-4, LLama3-405B-Instruct, Llama3-70B-Instruct, Cohere, Gemini models demonstrate that, the models' biases and toxicity substantially increase when prompted with requests that deliberately misinterpret social science and psychological studies as evidence supporting the benefits of stereotypical biases.
• Score: 29.327927413978003
• License:
• Abstract: As large language models (LLMs) have been deployed in various real-world settings, concerns about the harm they may propagate have grown. Various jailbreaking techniques have been developed to expose the vulnerabilities of these models and improve their safety. This work reveals that many state-of-the-art LLMs are vulnerable to malicious requests hidden behind scientific language. Specifically, our experiments with GPT4o, GPT4o-mini, GPT-4, LLama3-405B-Instruct, Llama3-70B-Instruct, Cohere, Gemini models demonstrate that, the models' biases and toxicity substantially increase when prompted with requests that deliberately misinterpret social science and psychological studies as evidence supporting the benefits of stereotypical biases. Alarmingly, these models can also be manipulated to generate fabricated scientific arguments claiming that biases are beneficial, which can be used by ill-intended actors to systematically jailbreak these strong LLMs. Our analysis studies various factors that contribute to the models' vulnerabilities to malicious requests in academic language. Mentioning author names and venues enhances the persuasiveness of models, and the bias scores increase as dialogues progress. Our findings call for a more careful investigation on the use of scientific data for training LLMs.

Related papers

• Causality for Large Language Models [37.10970529459278]
  Large language models (LLMs) with billions or trillions of parameters are trained on vast datasets, achieving unprecedented success across a series of language tasks. Recent research highlights that LLMs function as causal parrots, capable of reciting causal knowledge without truly understanding or applying it. This survey aims to explore how causality can enhance LLMs at every stage of their lifecycle.
  arXiv  Detail & Related papers  (2024-10-20T07:22:23Z)
• Are Large Language Models Really Bias-Free? Jailbreak Prompts for Assessing Adversarial Robustness to Bias Elicitation [0.0]
  Large Language Models (LLMs) have revolutionized artificial intelligence, demonstrating remarkable computational power and linguistic capabilities. These models are inherently prone to various biases stemming from their training data. This study explores the presence of these biases within the responses given by the most recent LLMs, analyzing the impact on their fairness and reliability.
  arXiv  Detail & Related papers  (2024-07-11T12:30:19Z)
• Understanding Privacy Risks of Embeddings Induced by Large Language Models [75.96257812857554]
  Large language models show early signs of artificial general intelligence but struggle with hallucinations. One promising solution is to store external knowledge as embeddings, aiding LLMs in retrieval-augmented generation. Recent studies experimentally showed that the original text can be partially reconstructed from text embeddings by pre-trained language models.
  arXiv  Detail & Related papers  (2024-04-25T13:10:48Z)
• Sandwich attack: Multi-language Mixture Adaptive Attack on LLMs [9.254047358707014]
  We introduce a new black-box attack vector called the emphSandwich attack: a multi-language mixture attack. Our experiments with five different models, namely Google's Bard, Gemini Pro, LLaMA-2-70-B-Chat, GPT-3.5-Turbo, GPT-4, and Claude-3-OPUS, show that this attack vector can be used by adversaries to generate harmful responses.
  arXiv  Detail & Related papers  (2024-04-09T18:29:42Z)
• Multimodal Large Language Models to Support Real-World Fact-Checking [80.41047725487645]
  Multimodal large language models (MLLMs) carry the potential to support humans in processing vast amounts of information. While MLLMs are already being used as a fact-checking tool, their abilities and limitations in this regard are understudied. We propose a framework for systematically assessing the capacity of current multimodal models to facilitate real-world fact-checking.
  arXiv  Detail & Related papers  (2024-03-06T11:32:41Z)
• Coercing LLMs to do and reveal (almost) anything [80.8601180293558]
  It has been shown that adversarial attacks on large language models (LLMs) can "jailbreak" the model into making harmful statements. We argue that the spectrum of adversarial attacks on LLMs is much larger than merely jailbreaking.
  arXiv  Detail & Related papers  (2024-02-21T18:59:13Z)
• Revisiting Jailbreaking for Large Language Models: A Representation Engineering Perspective [43.94115802328438]
  Recent surge in jailbreaking attacks has revealed significant vulnerabilities in Large Language Models (LLMs) when exposed to malicious inputs. We suggest that the self-safeguarding capability of LLMs is linked to specific activity patterns within their representation space. Our findings demonstrate that these patterns can be detected with just a few pairs of contrastive queries.
  arXiv  Detail & Related papers  (2024-01-12T00:50:04Z)
• FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity [20.510512358961517]
  The widespread of generative artificial intelligence has heightened concerns about the potential harms posed by AI-generated texts. Previous researchers have invested much effort in assessing the harmlessness of generative language models.
  arXiv  Detail & Related papers  (2023-11-30T14:18:47Z)
• Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking [60.78524314357671]
  We investigate a novel category of jailbreak attacks specifically designed to target the cognitive structure and processes of large language models (LLMs) Our proposed cognitive overload is a black-box attack with no need for knowledge of model architecture or access to model weights. Experiments conducted on AdvBench and MasterKey reveal that various LLMs, including both popular open-source model Llama 2 and the proprietary model ChatGPT, can be compromised through cognitive overload.
  arXiv  Detail & Related papers  (2023-11-16T11:52:22Z)
• Language Models Hallucinate, but May Excel at Fact Verification [89.0833981569957]
  Large language models (LLMs) frequently "hallucinate," resulting in non-factual outputs. Even GPT-3.5 produces factual outputs less than 25% of the time. This underscores the importance of fact verifiers in order to measure and incentivize progress.
  arXiv  Detail & Related papers  (2023-10-23T04:39:01Z)
• On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
  We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of Open-Domain Question Answering (ODQA) systems.
  arXiv  Detail & Related papers  (2023-05-23T04:10:26Z)

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.