Responsibility

In addition to the previously mentioned vulnerabilities, LLMs are susceptible to a range of other challenges. These include phenomena such as hallucination, misinformation, and various active attack techniques such as adversarial attacks, prompt injecting, and jailbreak attacks. Furthermore, other generative AI models have demonstrated the ability to fabricate human faces and voices convincingly, raising concerns regarding their potential misuse for fraudulent purposes.

Generative AI models are being increasingly utilized to fabricate synthetic videos and voices, fueling the proliferation of deceptive news, fraudulent schemes, scams, and other criminal pursuits. Unlike traditional centralized settings, where model training is overseen and regulated by a single entity, DeAI environments lack centralized oversight. This decentralized nature renders models susceptible to exploitation by malicious actors who may seek to manipulate or misuse them for illicit purposes. This underscores the critical importance of implementing robust security measures and oversight mechanisms to mitigate the risks associated with the misuse of generative AI in DeAI environments.