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hCaptcha discusses deploying a simple yet straightforward approach for identifying Large Language Models ( LLMs ) and other forms of automation . The discussion delves into how LLMs can impact services like hCaptcha and addresses the concern that growth and advancement in AI will lead to more challenging human verifications .

How do LLMs affect services like hCaptcha ?

It is important to understand that AI and Machine Learning ( ML ) is not a new discussion . hCaptcha has developed systems to detect automation of all kinds for years using various approaches .

However , the services we provide are both urgent and more challenging as AI and ML techniques improve . Legacy vendors that have failed to maintain the level of R & D required will struggle to keep up . This is why we have always focused on research and continuously evolved in our strategies and methods to stay ahead .

How does hCaptcha detect LLMs and other automation ? no difficulty detecting them today and do not expect this to change any time soon . As AI and ML gets better , adversaries can adapt faster but by the same token so do we . This is an ongoing arms race , but not a new one .

Will humanity verification questions get harder as AI gets smarter ?

Not necessarily . To understand why , we need a key insight . AI and ML systems make different kinds of mistakes than people do . Individual failings in AI and ML systems can be fixed , but exactly emulating human cognition is not on the near-term horizon even when AI systems start to approach or exceed human problem-solving capacity in other ways .

This is a fundamental limitation of artificial neural networks . They are useful tools but do not reproduce human cognition particularly well . Understanding these differences gives us many ways to detect LLMs and other models via challenges . hCaptcha is already able to use techniques like these to reliably detect LLMs and to identify which LLM is being used to produce the answer , especially because each LLM consistently makes identifiable errors in a row .

As you may have guessed , this is hardly our only detection method ; we chose it to write up as one of the simpler approaches to explain . We expect that this example will soon be patched due to the publication of our results but the underlying difference that allows detection is fundamental to these systems .

Need to detect advanced automation yourself ?

Check out hCaptcha Enterprise to find and stop online fraud and abuse , whether automated or human .

P . S .: If you ’ re interested in these kinds of problems and the web-scale distributed systems behind them , hCaptcha is hiring . p

Although we often publish our research at academic conferences on ML , we generally do not share specific details or strategies of our security measures publicly to protect our users .

We are making an exception to this practice below to help dispel some of the confusion around the true capabilities of LLMs . We have

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