As the advent of autonomous vehicle (AV) technology revolutionizes transportation, it simultaneously introduces new vulnerabilities to cyber-attacks, posing significant challenges to vehicle safety and security. The complexity of these systems, coupled with their increasing reliance on advanced computer vision and machine learning algorithms, makes them susceptible to sophisticated AV attacks. This paper explores the potential of Large Multimodal Models (LMMs) in identifying Natural Denoising Diffusion (NDD) attacks on traffic signs. Our comparative analysis show the superior performance of LMMs in detecting NDD samples with an average accuracy of 82.52% across the selected models compared to 37.75% for state-of-the-art deep learning models. We further discuss the integration of LMMs within the resource-constrained computational environments to mimic typical autonomous vehicles and assess their practicality through latency benchmarks. Results show substantial superiority of GPT models in achieving lower latency, down to 4.5 seconds per image for both computation time and network latency (RTT), suggesting a viable path towards real-world deployability. Lastly, we extend our analysis to LMMs’ applicability against a wider spectrum of AV attacks, particularly focusing on the Automated Lane Centering systems, emphasizing the potential of LMMs to enhance vehicular cybersecurity.

Amazon Alexa is one of the largest Voice Personal Assistant (VPA) platforms and it allows third-party developers to publish their voice apps, named skills, to the Alexa skill store. To satisfy the needs of European users, Amazon Alexa has established multiple skill marketplaces in Europe and allows developers to publish skills in their native languages. Skills in European marketplaces are required to comply with GDPR (General Data Protection Regulation), which imposes strict obligations on data collection and processing. Skills that involve data collection should provide a privacy policy to disclose the data practice to users and meet GDPR requirements.

In recent years, the field of Natural Language Processing (NLP) has witnessed a groundbreaking transformation with the emergence of large language models (LLMs). ChatGPT stands out as an example among these LLM models captivating considerable public interest due to its impressive language generation capabilities. Researchers have been exploring the potential of using ChatGPT for data annotation tasks, aiming to discover more timesaving and cost-effective approaches. In this paper, we present a comprehensive evaluation of ChatGPT's data annotation capabilities across ten diverse datasets covering various subject areas and varied number of classes. To ensure the quality of our evaluation, we leveraged datasets that were previously annotated by human experts, providing a reliable benchmark for comparison. Through rigorous experimentation, we assessed the impact of different prompt strategies and model configurations on the annotation performance. Our findings emphasize the capability of ChatGPT in handling most data annotation tasks achieving average accuracy of 78.2% across various tasks. The banking queries dataset stands out with an impressive 95.9% accuracy, while emotions classification presents challenges, yielding an accuracy of 57.5%. Our evaluation also highlights the impact of prompt strategies on annotation performance and reveals significant performance differences between GPT models, with “gpt-4” achieving higher accuracy 79.2% on average compared to “gpt-3.5” of 74.6%. Our research provides valuable insights into the capabilities and limitations of ChatGPT in automating data annotation tasks.