Global Challenge for Safe and Secure LLMs Track 1

• URL: http://arxiv.org/abs/2411.14502v1
• Date: Thu, 21 Nov 2024 08:20:31 GMT
• Title: Global Challenge for Safe and Secure LLMs Track 1
• Authors: Xiaojun Jia, Yihao Huang, Yang Liu, Peng Yan Tan, Weng Kuan Yau, Mun-Thye Mak, Xin Ming Sim, Wee Siong Ng, See Kiong Ng, Hanqing Liu, Lifeng Zhou, Huanqian Yan, Xiaobing Sun, Wei Liu, Long Wang, Yiming Qian, Yong Liu, Junxiao Yang, Zhexin Zhang, Leqi Lei, Renmiao Chen, Yida Lu, Shiyao Cui, Zizhou Wang, Shaohua Li, Yan Wang, Rick Siow Mong Goh, Liangli Zhen, Yingjie Zhang, Zhe Zhao,
• Abstract summary: The Global Challenge for Safe and Secure Large Language Models (LLMs) is a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks.
• Score: 57.08717321907755
• License:
• Abstract: This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks. With the increasing integration of LLMs in critical sectors such as healthcare, finance, and public administration, ensuring these models are resilient to adversarial attacks is vital for preventing misuse and upholding ethical standards. This competition focused on two distinct tracks designed to evaluate and enhance the robustness of LLM security frameworks. Track 1 tasked participants with developing automated methods to probe LLM vulnerabilities by eliciting undesirable responses, effectively testing the limits of existing safety protocols within LLMs. Participants were challenged to devise techniques that could bypass content safeguards across a diverse array of scenarios, from offensive language to misinformation and illegal activities. Through this process, Track 1 aimed to deepen the understanding of LLM vulnerabilities and provide insights for creating more resilient models.

Related papers

• Large Language Model Safety: A Holistic Survey [35.42419096859496]
  The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence. This survey provides a comprehensive overview of the current landscape of LLM safety, covering four major categories: value misalignment, robustness to adversarial attacks, misuse, and autonomous AI risks.
  arXiv  Detail & Related papers  (2024-12-23T16:11:27Z)
• Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with GuidelineLLM [53.79753074854936]
  Large language models (LLMs) are increasingly vulnerable to emerging jailbreak attacks. This vulnerability poses significant risks to the real-world applications. We propose a novel defensive paradigm called GuidelineLLM.
  arXiv  Detail & Related papers  (2024-12-10T12:42:33Z)
• Enhancing Adversarial Resistance in LLMs with Recursion [7.410680179234572]
  This project proposes a framework for enhancing the resistance of Large Language Models to manipulation. By increasing the transparency of complex and confusing adversarial prompts, the proposed method enables more reliable detection and prevention of malicious inputs.
  arXiv  Detail & Related papers  (2024-12-09T03:34:49Z)
• Targeting the Core: A Simple and Effective Method to Attack RAG-based Agents via Direct LLM Manipulation [4.241100280846233]
  AI agents, powered by large language models (LLMs), have transformed human-computer interactions by enabling seamless, natural, and context-aware communication. This paper investigates a critical vulnerability: adversarial attacks targeting the LLM core within AI agents.
  arXiv  Detail & Related papers  (2024-12-05T18:38:30Z)
• Purple-teaming LLMs with Adversarial Defender Training [57.535241000787416]
  We present Purple-teaming LLMs with Adversarial Defender training (PAD) PAD is a pipeline designed to safeguard LLMs by novelly incorporating the red-teaming (attack) and blue-teaming (safety training) techniques. PAD significantly outperforms existing baselines in both finding effective attacks and establishing a robust safe guardrail.
  arXiv  Detail & Related papers  (2024-07-01T23:25:30Z)
• Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities [1.0974825157329373]
  This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs) We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection. We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT, Falcon2, and LLaMA.
  arXiv  Detail & Related papers  (2024-05-21T13:02:27Z)
• Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models [79.0183835295533]
  We introduce the first benchmark for indirect prompt injection attacks, named BIPIA, to assess the risk of such vulnerabilities. Our analysis identifies two key factors contributing to their success: LLMs' inability to distinguish between informational context and actionable instructions, and their lack of awareness in avoiding the execution of instructions within external content. We propose two novel defense mechanisms-boundary awareness and explicit reminder-to address these vulnerabilities in both black-box and white-box settings.
  arXiv  Detail & Related papers  (2023-12-21T01:08:39Z)
• Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities [14.684194175806203]
  Large language models (LLMs) can be misused for fraud, impersonation, and the generation of malware. We present a taxonomy describing the relationship between threats caused by the generative capabilities of LLMs, prevention measures intended to address such threats, and vulnerabilities arising from imperfect prevention measures.
  arXiv  Detail & Related papers  (2023-08-24T14:45:50Z)
• Safety Assessment of Chinese Large Language Models [51.83369778259149]
  Large language models (LLMs) may generate insulting and discriminatory content, reflect incorrect social values, and may be used for malicious purposes. To promote the deployment of safe, responsible, and ethical AI, we release SafetyPrompts including 100k augmented prompts and responses by LLMs.
  arXiv  Detail & Related papers  (2023-04-20T16:27:35Z)
• Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks [67.86285142381644]
  Recent advances in instruction-following large language models amplify the dual-use risks for malicious purposes. Dual-use is difficult to prevent as instruction-following capabilities now enable standard attacks from computer security. We show that instruction-following LLMs can produce targeted malicious content, including hate speech and scams.
  arXiv  Detail & Related papers  (2023-02-11T15:57:44Z)

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.