But separating hype from reality when it comes to ChatGPT is no small feat. Its coding abilities have inspired a series of overheated headlines — “ChatGPT is a bigger threat to cybersecurity than most realize,” for example — about its ability to write malware and have left seasoned hackers wondering about the extent to which large language models can really be used for malicious hacking…
Marcus Hutchins, the black-hat-turned-white-hat hacker, made headlines in 2017 for stopping the spread of the WannaCry ransomware, and given his experience writing banking trojans in a past life, counted himself among the curious regarding ChatGPT’s abilities. He wondered: Could the chat-bot be used to write malware?…
After hours of tinkering, Hutchins was able to generate components of a ransomware program — a file encryption routine — but when trying to combine that component with other features necessary to build a fully fledged piece of malware, ChatGPT failed in sophomoric ways, requesting to open a file after attempting to open it. And when he tried to combine various components, ChatGPT would generally fail.
These types of rudimentary ordering problems illustrate the shortcomings of generative AI systems such as ChatGPT. While they are able to create content that is immensely similar to the data it is trained on, large language models often lack the error correction tools and contextual knowledge that make up actual expertise. And amid the astonished reactions to ChatGPT, the limitations of the tool are often lost… Read the full article here.