Preventing Jailbreak Prompts as Malicious Tools for Cybercriminals: A Cyber Defense Perspective

• URL: http://arxiv.org/abs/2411.16642v1
• Date: Mon, 25 Nov 2024 18:23:58 GMT
• Title: Preventing Jailbreak Prompts as Malicious Tools for Cybercriminals: A Cyber Defense Perspective
• Authors: Jean Marie Tshimula, Xavier Ndona, D'Jeff K. Nkashama, Pierre-Martin Tardif, Froduald Kabanza, Marc Frappier, Shengrui Wang,
• Abstract summary: Jailbreak prompts pose a significant threat in AI and cybersecurity, as they are crafted to bypass ethical safeguards in large language models. This paper analyzes jailbreak prompts from a cyber defense perspective, exploring techniques like prompt injection and context manipulation. We propose strategies involving advanced prompt analysis, dynamic safety protocols, and continuous model fine-tuning to strengthen AI resilience.
• Score: 1.083674643223243
• License:
• Abstract: Jailbreak prompts pose a significant threat in AI and cybersecurity, as they are crafted to bypass ethical safeguards in large language models, potentially enabling misuse by cybercriminals. This paper analyzes jailbreak prompts from a cyber defense perspective, exploring techniques like prompt injection and context manipulation that allow harmful content generation, content filter evasion, and sensitive information extraction. We assess the impact of successful jailbreaks, from misinformation and automated social engineering to hazardous content creation, including bioweapons and explosives. To address these threats, we propose strategies involving advanced prompt analysis, dynamic safety protocols, and continuous model fine-tuning to strengthen AI resilience. Additionally, we highlight the need for collaboration among AI researchers, cybersecurity experts, and policymakers to set standards for protecting AI systems. Through case studies, we illustrate these cyber defense approaches, promoting responsible AI practices to maintain system integrity and public trust. \textbf{\color{red}Warning: This paper contains content which the reader may find offensive.}

Related papers

• Is Generative AI the Next Tactical Cyber Weapon For Threat Actors? Unforeseen Implications of AI Generated Cyber Attacks [0.0]
  This paper delves into the escalating threat posed by the misuse of AI, specifically through the use of Large Language Models (LLMs) Through a series of controlled experiments, the paper demonstrates how these models can be manipulated to bypass ethical and privacy safeguards to effectively generate cyber attacks. We also introduce Occupy AI, a customized, finetuned LLM specifically engineered to automate and execute cyberattacks.
  arXiv  Detail & Related papers  (2024-08-23T02:56:13Z)
• AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lens [83.08119913279488]
  We present a systematic analysis of the dependency relationships in jailbreak attack and defense techniques. We propose three comprehensive, automated, and logical frameworks. We show that the proposed ensemble jailbreak attack and defense framework significantly outperforms existing research.
  arXiv  Detail & Related papers  (2024-06-06T07:24:41Z)
• Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security [0.0]
  This paper explores the integration of Artificial Intelligence (AI) into offensive cybersecurity. It develops an autonomous AI agent, ReaperAI, designed to simulate and execute cyberattacks. ReaperAI demonstrates the potential to identify, exploit, and analyze security vulnerabilities autonomously.
  arXiv  Detail & Related papers  (2024-05-09T18:15:12Z)
• Review of Generative AI Methods in Cybersecurity [0.6990493129893112]
  This paper provides a comprehensive overview of the current state-of-the-art deployments of Generative AI (GenAI) It covers assaults, jailbreaking, and applications of prompt injection and reverse psychology. It also provides the various applications of GenAI in cybercrimes, such as automated hacking, phishing emails, social engineering, reverse cryptography, creating attack payloads, and creating malware.
  arXiv  Detail & Related papers  (2024-03-13T17:05:05Z)
• From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy [0.0]
  This research paper highlights the limitations, challenges, potential risks, and opportunities of GenAI in the domain of cybersecurity and privacy. The paper investigates how cyber offenders can use the GenAI tools in developing cyber attacks. We will also discuss the social, legal, and ethical implications of ChatGPT.
  arXiv  Detail & Related papers  (2023-07-03T00:36:57Z)
• Impacts and Risk of Generative AI Technology on Cyber Defense [0.0]
  We propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks. This paper aims to provide a comprehensive analysis of the risk areas introduced by the offensive use of GenAI techniques. We also analyze the strategies employed by threat actors, highlighting the implications for cyber defense.
  arXiv  Detail & Related papers  (2023-06-22T16:51:41Z)
• Graph Mining for Cybersecurity: A Survey [61.505995908021525]
  The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
  arXiv  Detail & Related papers  (2023-04-02T08:43:03Z)
• Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022 [55.573187938617636]
  The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
  arXiv  Detail & Related papers  (2022-02-28T18:27:41Z)
• Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS [70.60975663021952]
  We study blackbox adversarial attacks on network classifiers. We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions. We show that a continual learning approach is required to study attacker-defender dynamics.
  arXiv  Detail & Related papers  (2021-11-23T23:42:16Z)
• A System for Efficiently Hunting for Cyber Threats in Computer Systems Using Threat Intelligence [78.23170229258162]
  We build ThreatRaptor, a system that facilitates cyber threat hunting in computer systems using OSCTI. ThreatRaptor provides (1) an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructured OSCTI text, (2) a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities, and (3) a query synthesis mechanism that automatically synthesizes a TBQL query from the extracted threat behaviors.
  arXiv  Detail & Related papers  (2021-01-17T19:44:09Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)

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.