Despite the proven efficacy of large language models (LLMs) like GPT in numerous applications, concerns have emerged regarding their exploitation in creating phishing emails or network intrusions, which have shown to be detrimental. The multimodal functionalities of large vision-language models (LVLMs) enable them to grasp visual commonsense knowledge. This study investigates the feasibility of using two widely available commercial LVLMs, LLAVA, and multimodal GPT4, for effectively bypassing CAPTCHAs or producing bot-driven fraud through malicious prompts. It was found that these LVLMs can interpret and respond to the visual information presented in image, puzzle, and text-based CAPTCHA and reCAPTCHA, thereby potentially circumventing the challenge-response authentication security measure. This capability suggests that such systems could facilitate unauthorized access to secured accounts via remote digital methods. Remarkably, these attacks can be executed with the standard, unaltered versions of the LVLMs, eliminating the need for previous adversarial methods like jailbreaking.

Inspection of Anti-COVID vaccination tweets can be useful for many such analyses, and extraction of relevant information about opinion expressed on Twitter. This study proposes an analytical framework for analyzing tweets (COVID Vaccine, especially the Anti- COVID Vaccine) to identify and categorize fine-grained details about the COVID19 disaster such as affected individuals, public feelings towards the vaccine and reopening of business, polarity of public opinions on the vaccine and services provided, discussed topic changing over temporal dimension, and different clustering algorithms. In this project, we have analyzed COVID -Vaccine related tweets and Anti-Vaccine tweets, performed sentiment analysis and Topic modeling, and compared various models’ behavior based on different configuration and training datasets. The result of this work will help policy makers and data scientists to identify the best approach for twitter sentiment analysis and topic modeling as well as providing feedback on people attitude and opinion on COVID-19 vaccine.