Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Today, cybersecurity companies are in a never-ending race against cyber criminals, each seeking innovative new tactics to outpace the other. The newfound accessibility of generative artificial intelligence (gen AI) has revolutionized how people work, but it's also made threat actors more efficient. Attackers can now quickly create phishing messages or automate vulnerability discoveries.

At Black Hat USA 2023, a session led by a team of security researchers, including Fredrik Heiding, Bruce Schneier, Arun Vishwanath, and Jeremy Bernstein, unveiled an intriguing experiment. They tested large language models (LLMs) to see how they performed in both writing convincing phishing emails and detecting them. This is the PDF technical paper.

In today's world, cybercriminals are learning to harness the power of AI. Cybersecurity professionals must be prepared for the current threats of zero days, insider threats, and supply chain, but now add in Artificial Intelligence (AI), specifically Generative AI. AI can revolutionize industries, but cybersecurity leaders and practitioners should be mindful of its capabilities and ensure it is used effectively.

As technology continues to evolve, there is a growing concern about the potential for large language models (LLMs), like ChatGPT, to be used for criminal purposes. In this blog we will discuss two such LLM engines that were made available recently on underground forums, WormGPT and FraudGPT. If criminals were to possess their own ChatGPT-like tool, the implications for cybersecurity, social engineering, and overall digital safety could be significant.

ChatGPT continues to lead the news cycle and increase in popularity, with new applications and uses seemingly uncovered each day for this innovative platform. However, as interesting as this solution is, and as many efficiencies as it is already providing to modern businesses, it’s not without its risks.

Today’s discussions on AI are often affected by emotions like fear and awe, as well as the fact that AI is reduced to large language models (LLM) — but this isn’t the entire story. So, let’s step back from these fears and dig into the data explaining why AI is richer than LLMs.