3. Data privac copyright infringement

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While hint/query injections may have seemed inconsequential in the past, these attacks can now have very real consequences as they begin to execute generated code, integrate into external APIs, and even read your browser tabs.

Training large language models requires huge amounts of data, with some models having over half a trillion parameters. At this scale, understanding provenance, authorship, and copyright status is a daunting, if not impossible, task. An unverified training set could result in a model leaking sensitive data, misquoting, or plagiarizing copyrighted content.

The data privacy laws surrounding LLM are also very murky. As we know from social media, if something is free, there’s a chance that users become the product. It’s worth remembering that if bahrain mobile database ask a chatbot to find a bug in our code or write a confidential document, we are sending that data to a third party that may ultimately use it for model training, advertising, or competitive advantage. Data leakage via AI prompts can be especially dangerous for businesses.

As LLM-based services are integrated into productivity tools such as messaging and collaboration tools, it is important to carefully review the privacy policies of providers, understand how AI suggestions may be used, and regulate the use of LLM in the workplace accordingly. In terms of copyright protection, we need to regulate the acquisition and use of data through consent or special licensing, without hindering the open and largely free Internet that it is today.