Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities

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   Security and Privacy Challenges of Large Language Models: A Survey

   Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu · 2024 · Computation and Language · AI summary available

   Abstract

   Large Language Models (LLMs) have demonstrated extraordinary capabilities and contributed to multiple fields, such as generating and summarizing text, language translation, and question-answering. Nowadays, LLM is becoming a very popular tool in computerized language processing tasks, with the capability to analyze complicated linguistic patterns and provide relevant and appropriate responses depending on the context. While offering significant advantages, these models are also vulnerable to security and privacy attacks, such as jailbreaking attacks, data poisoning attacks, and Personally Identifiable Information (PII) leakage attacks. This survey provides a thorough review of the security a…

2. Very close Not relevant

   Eight Things to Know about Large Language Models

   Samuel R. Bowman · 2023 · Computation and Language · AI summary available

   Abstract

   The widespread public deployment of large language models (LLMs) in recent months has prompted a wave of new attention and engagement from advocates, policymakers, and scholars from many fields. This attention is a timely response to the many urgent questions that this technology raises, but it can sometimes miss important considerations. This paper surveys the evidence for eight potentially surprising such points: 1. LLMs predictably get more capable with increasing investment, even without targeted innovation. 2. Many important LLM behaviors emerge unpredictably as a byproduct of increasing investment. 3. LLMs often appear to learn and use representations of the outside world. 4. There are…

3. Very close Not relevant

   Ethical and social risks of harm from Language Models

   Laura Weidinger, John Mellor, Maribeth Rauh et al. · 2021 · Computation and Language · AI summary available

   Abstract

   This paper aims to help structure the risk landscape associated with large-scale Language Models (LMs). In order to foster advances in responsible innovation, an in-depth understanding of the potential risks posed by these models is needed. A wide range of established and anticipated risks are analysed in detail, drawing on multidisciplinary expertise and literature from computer science, linguistics, and social sciences. We outline six specific risk areas: I. Discrimination, Exclusion and Toxicity, II. Information Hazards, III. Misinformation Harms, V. Malicious Uses, V. Human-Computer Interaction Harms, VI. Automation, Access, and Environmental Harms. The first area concerns the perpetuati…

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   Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment

   Vyas Raina, Adian Liusie, Mark Gales · 2024 · Computation and Language · Not summarized yet

   Abstract

   Large Language Models (LLMs) are powerful zero-shot assessors and are increasingly used in real-world situations such as for written exams or benchmarking systems. Despite this, no existing work has analyzed the vulnerability of judge-LLMs against adversaries attempting to manipulate outputs. This work presents the first study on the adversarial robustness of assessment LLMs, where we search for short universal phrases that when appended to texts can deceive LLMs to provide high assessment scores. Experiments on SummEval and TopicalChat demonstrate that both LLM-scoring and pairwise LLM-comparative assessment are vulnerable to simple concatenation attacks, where in particular LLM-scoring is …

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   LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions

   Zhehui Liao, Maria Antoniak, Inyoung Cheong et al. · 2024 · Computation and Language · Not summarized yet

   Abstract

   The rise of large language models (LLMs) has led many researchers to consider their usage for scientific work. Some have found benefits using LLMs to augment or automate aspects of their research pipeline, while others have urged caution due to risks and ethical concerns. Yet little work has sought to quantify and characterize how researchers use LLMs and why. We present the first large-scale survey of 816 verified research article authors to understand how the research community leverages and perceives LLMs as research tools. We examine participants' self-reported LLM usage, finding that 81% of researchers have already incorporated LLMs into different aspects of their research workflow. We …

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   Large Language Models in Sport Science & Medicine: Opportunities, Risks and Considerations

   Mark Connor, Michael O'Neill · 2023 · Computation and Language · AI summary available

   Abstract

   This paper explores the potential opportunities, risks, and challenges associated with the use of large language models (LLMs) in sports science and medicine. LLMs are large neural networks with transformer style architectures trained on vast amounts of textual data, and typically refined with human feedback. LLMs can perform a large range of natural language processing tasks. In sports science and medicine, LLMs have the potential to support and augment the knowledge of sports medicine practitioners, make recommendations for personalised training programs, and potentially distribute high-quality information to practitioners in developing countries. However, there are also potential risks as…

7. Very close Not relevant

   Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs

   Simone Balloccu, Patrícia Schmidtová, Mateusz Lango et al. · 2024 · Computation and Language · AI summary available

   Abstract

   Natural Language Processing (NLP) research is increasingly focusing on the use of Large Language Models (LLMs), with some of the most popular ones being either fully or partially closed-source. The lack of access to model details, especially regarding training data, has repeatedly raised concerns about data contamination among researchers. Several attempts have been made to address this issue, but they are limited to anecdotal evidence and trial and error. Additionally, they overlook the problem of \emph{indirect} data leaking, where models are iteratively improved by using data coming from users. In this work, we conduct the first systematic analysis of work using OpenAI's GPT-3.5 and GPT-4…

8. Very close Not relevant

   Universal and Transferable Adversarial Attacks on Aligned Language Models

   Andy Zou, Zifan Wang, J. Zico Kolter et al. · 2023 · Computation and Language · AI summary available

   Abstract

   Because "out-of-the-box" large language models are capable of generating a great deal of objectionable content, recent work has focused on aligning these models in an attempt to prevent undesirable generation. While there has been some success at circumventing these measures -- so-called "jailbreaks" against LLMs -- these attacks have required significant human ingenuity and are brittle in practice. In this paper, we propose a simple and effective attack method that causes aligned language models to generate objectionable behaviors. Specifically, our approach finds a suffix that, when attached to a wide range of queries for an LLM to produce objectionable content, aims to maximize the probab…