LLM-generated fraud: Guide to malware and vulnerabilities

Large Language Models (LLMs) like OpenAI's GPT, Google's Bard, and Anthropic's Claude introduce significant complexities to cybersecurity, enabling personalized, sophisticated online fraud that is challenging to detect. These models can mimic trusted entities, generate phishing emails, create malware, and participate in information warfare, raising concerns about their potential to facilitate automated fraud and misinformation at scale. Techniques such as fine-tuning LLMs for malicious purposes, prompt engineering, and exploiting open-source models without safety measures illustrate their versatility in creating harmful outputs. Notable malicious LLMs like WormGPT and FraudGPT are being used for phishing and malware generation, while others like DarkBERT and PoisonGPT demonstrate the potential for spreading misinformation and exploiting vulnerabilities. This emerging landscape poses threats across various sectors, including finance, healthcare, e-commerce, and government, necessitating advanced detection systems, employee training, and regular security audits to mitigate risks. As the arms race between AI capabilities and security intensifies, proactive collaboration among stakeholders is essential to balance innovation with societal safety.

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