One topic being actively researched in connection with the breakout of LLMs is capability uplift – when employees with limited experience or resources in some area become able to perform at a much higher level thanks to LLM technology. This is especially important in information security, where cyberattacks are becoming increasingly cost-effective and larger-scale, causing headaches for security teams.
Among other tools, attackers use LLMs to generate content for fake websites. Such sites can mimic reputable organizations – from social networks to banks – to extract credentials from victims (classic phishing), or they can pretend to be stores of famous brands offering super discounts on products (which mysteriously never get delivered).
Aided by LLMs, attackers can fully automate the creation of dozens, even hundreds of web pages with different content. Before, some specific tasks could be done automatically, such as generating and registering domain names, obtaining certificates and making sites available through free hosting services. Now, however, thanks to LLMs, scammers can create unique, fairly high-quality content (much higher than when using, say, synonymizers) without the need for costly manual labor. This, in particular, hinders detection using rules based on specific phrases. Detecting LLM-generated pages requires systems for analyzing metadata or page structure, or fuzzy approaches such as machine learning.
But LLMs don’t always work perfectly, so if the scale of automation is large or the level of control is low, they can leave telltale indicators, or artifacts, that the model was poorly applied. Such phrases, which recently have been cropping up everywhere from marketplace reviews to academic papers, as well as tags left by LLM tools, make it possible at ..
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