Unleashing the Dark Side: Unveiling Threats & Vulnerabilities in AI Models

The rapid surge in LLMs (Large language models) across several industries and sectors has raised critical concerns about their safety, security, and potential for misuse.

In the current threat landscape, threat actors can exploit the LLMs for several illicit purposes, such as:-

Recently, a group of cybersecurity experts from the following universities have conducted a study in which they analyzed how threat actors could abuse threats and vulnerabilities in AI models for illicit purposes:-

  • Maximilian Mozes (Department of Computer Science, University College London and Department of Security and Crime Science, University College London)
  • Xuanli He (Department of Computer Science, University College London)
  • Bennett Kleinberg (Department of Security and Crime Science, University College London and Department of Methodology and Statistics, Tilburg University)
  • Lewis D. Griffin (Department of Computer Science, University College London)

Flaws in AI Models

Apart from this, with several extraordinary advancements, the LLM models are also vulnerable to several threats and flaws, as threat actors could easily abuse these AI models for several illicit tasks. 

Besides this, recent detection of the following cyber AI weapons also depicted the rapid uptick in the exploitation of AI models:-

Overview of the taxonomy of malicious and criminal use cases enabled via LLMs  (Source – Arxiv)

However, AI text generation aids in detecting malicious content, including misinformation and plagiarism in essays and journalism, using diverse proposed methods like:-

  • Watermarking
  • Discriminating approaches
  • Zero-shot approaches

Red teaming tests LLMs for harmful language, and the content filtering methods aim to prevent it, an area with a limited focus in the research.

Here below, we have mentioned all the flaws in AI models:-

  • Prompt leaking
  • Indirect prompt injection attacks
  • Prompt injection for multi-modal models
  • Goal hijacking
  • Jailbreaking
  • Universal adversarial triggers

LLMs like ChatGPT have gained huge popularity quickly, but they face challenges, including safety and security concerns, from adversarial examples to generative threats.

With this analysis, security analysts highlighted the LLM risks in academia and the real world, stressing the need for peer review to address proper concerns.

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Tushar is a Cyber security content editor with a passion for creating captivating and informative content. With years of experience under his belt in Cyber Security, he is covering Cyber Security News, technology and other news.