This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection. We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT, Falcon2, and LLaMA. Our analysis extends to LLM vulnerabilities, such as prompt injection, insecure output handling, data poisoning, DDoS attacks, and adversarial instructions. We delve into mitigation strategies to protect these models, providing a comprehensive look at potential attack scenarios and prevention techniques. Furthermore, we evaluate the performance of 42 LLM models in cybersecurity knowledge and hardware security, highlighting their strengths and weaknesses. We thoroughly evaluate cybersecurity datasets for LLM training and testing, covering the lifecycle from data creation to usage and identifying gaps for future research. In addition, we review new strategies for leveraging LLMs, including techniques like Half-Quadratic Quantization (HQQ), Reinforcement Learning with Human Feedback (RLHF), Direct Preference Optimization (DPO), Quantized Low-Rank Adapters (QLoRA), and Retrieval-Augmented Generation (RAG). These insights aim to enhance real-time cybersecurity defenses and improve the sophistication of LLM applications in threat detection and response. Our paper provides a foundational understanding and strategic direction for integrating LLMs into future cybersecurity frameworks, emphasizing innovation and robust model deployment to safeguard against evolving cyber threats.

In this work we investigate how children ages 5-12 perceive, understand, and use generative AI models such as a text-based LLMs ChatGPT and a visual-based model DALL-E. Generative AI is newly being used widely since chatGPT. Children are also building mental models of generative AI. Those haven't been studied before and it is also the case that the children's models are dynamic as they use the tools, even with just very short usage. Upon surveying and experimentally observing over 40 children ages 5-12, we found that children generally have a very positive outlook towards AI and are excited about the ways AI may benefit and aid them in their everyday lives. In a forced choice, children robustly associated AI with positive adjectives versus negative ones. We also categorize what children are querying AI models for and find that children search for more imaginative things that don't exist when using a visual-based AI and not when using a text-based one. Our follow-up study monitored children's responses and feelings towards AI before and after interacting with GenAI models. We even find that children find AI to be less scary after interacting with it. We hope that these findings will shine a light on children's mental models of AI and provide insight for how to design the best possible tools for children who will inevitably be using AI in their lifetimes. The motivation of this work is to bridge the gap between Human-Computer Interaction (HCI) and Psychology in an effort to study the effects of AI on society. We aim to identify the gaps in humans' mental models of what AI is and how it works. Previous work has investigated how both adults and children perceive various kinds of robots, computers, and other technological concepts. However, there is very little work investigating these concepts for generative AI models and not simply embodied robots or physical technology.

This study evaluates the security of web application code generated by Large Language Models, analyzing 2,500 GPT-4 generated PHP websites. These were deployed in Docker containers and tested for vulnerabilities using a hybrid approach of Burp Suite active scanning, static analysis, and manual review. Our investigation focuses on identifying Insecure File Upload, SQL Injection, Stored XSS, and Reflected XSS in GPT-4 generated PHP code. This analysis highlights potential security risks and the implications of deploying such code in real-world scenarios. Overall, our analysis found 2,440 vulnerable parameters. According to Burp's Scan, 11.56% of the sites can be straight out compromised. Adding static scan results, 26% had at least one vulnerability that can be exploited through web interaction. Certain coding scenarios, like file upload functionality, are insecure 78% of the time, underscoring significant risks to software safety and security. To support further research, we have made the source codes and a detailed vulnerability record for each sample publicly available. This study emphasizes the crucial need for thorough testing and evaluation if generative AI technologies are used in software development.

The emergence of generative artificial intelligence (GenAI) is transforming information interaction. For decades, search engines such as Google and Bing have been the primary means of locating relevant information for the general population. They have provided search results in the same standard format (the so-called "10 blue links"). The recent ability to chat via natural language with AI-based agents and have GenAI automatically synthesize answers in real-time (grounded in top-ranked results) is changing how people interact with and consume information at massive scale. These two information interaction modalities (traditional search and AI-powered chat) coexist in current search engines, either loosely coupled (e.g., as separate options/tabs) or tightly coupled (e.g., integrated as a chat answer embedded directly within a traditional search result page). We believe that the existence of these two different modalities, and potentially many others, is creating an opportunity to re-imagine the search experience, capitalize on the strengths of many modalities, and develop systems and strategies to support seamless flow between them. We refer to these as panmodal experiences. Unlike monomodal experiences, where only one modality is available and/or used for the task at hand, panmodal experiences make multiple modalities available to users (multimodal), directly support transitions between modalities (crossmodal), and seamlessly combine modalities to tailor task assistance (transmodal). While our focus is search and chat, with learnings from insights from a survey of over 100 individuals who have recently performed common tasks on these two modalities, we also present a more general vision for the future of information interaction using multiple modalities and the emergent capabilities of GenAI.

Last week OpenAI revealed a new conversational interface for ChatGPT with an expressive synthetic voice strikingly similar to that of the AI assistant played by Scarlett Johansson in the sci-fi movie Her--only to suddenly disable the new voice over the weekend. On Monday, Johansson issued a statement claiming to have forced that reversal, after her lawyers demanded OpenAI clarify how the new voice was created. Johansson's statement, relayed to WIRED by her publicist, claims that OpenAI CEO Sam Altman asked her last September to provide ChatGPT's new voice but that she declined. She describes being astounded to see the company demo a new voice for ChatGPT last week that sounded like her anyway. "When I heard the release demo I was shocked, angered, and in disbelief that Mr. Altman would pursue a voice that sounded so eerily similar to mine that my closest friends and news outlets could not tell the difference," the statement reads.

Actor Scarlett Johansson has accused OpenAI of copying her voice for one of the voice assisstants in ChatGPT despite denying the company permission to do so. Johansson's statement on Monday came hours after OpenAI said that the company would no longer use the voice in ChatGPT but did not provide a reason why. "Last September, I received an offer from Sam Altman, who wanted to hire me to voice the current ChatGPT 4.0 system," Johansson wrote in the statement that was first shared with NPR. "He told me that he felt that by my voicing the system, I could bridge the gap between tech companies and creatives and help consumers to feel comfortable with the seismic shift concerning humans and AI. He said he felt that my voice would be comforting to people."

In a statement provided to The Washington Post by Johansson's publicist, she claimed that she received an offer from OpenAI CEO Sam Altman in September to be the voice of its AI system. Johansson, who famously voiced the role of the all-knowing AI in the 2013 movie "Her," said she declined Altman's offer.

Microsoft wants laptop users to get so comfortable with its artificial intelligence chatbot that it will remember everything you're doing on your computer and help figure out what you want to do next. The software giant on Monday revealed an upgraded version of Copilot, its AI assistant, as it confronts heightened competition from big tech rivals in pitching generative AI technology that can compose documents, make images and serve as a lifelike personal assistant at work or home. The announcements ahead of Microsoft's annual Build developer conference in Seattle centered on imbuing AI features into a product where Microsoft already has the eyes of millions of consumers: the Windows operating system for personal computers. The new features will include Windows Recall, enabling the AI assistant to "access virtually what you have seen or done on your PC in a way that feels like having photographic memory". Microsoft promises to protect users' privacy by giving them the option to filter out what they don't want tracked.

We've been hearing rumblings for months now that Microsoft was working on so-called "AI PCs." At a pre-Build event, the company spelled out its vision for AI PCs. Microsoft is calling its version Copilot PCs, which CEO Satya Nadella described as a "new class of Windows PCs." These include hardware designed to handle more generative AI Copilot processes locally, rather than relying on the cloud. That requires a chipset with a neural processing unit (NPU) and manufacturers such as Qualcomm have been laying the groundwork with chips like the Snapdragon X Elite.

OpenAI removed a heavily promoted voice option from ChatGPT on Monday, following a widespread reaction to the flirtatious, feminine voice that sounded almost identical to Scarlett Johansson. The company used the voice, which it calls "Sky", during its widely publicized event last week debuting the capabilities of the new ChatGPT-4o artificial intelligence model. Researchers talked with the AI assistant to show off Sky's personable and responsive affectations, which users and members of the media immediately compared to Johansson's AI companion character in the 2013 Spike Jonze film Her. Even OpenAI's CEO, Sam Altman, seemed to suggest that the vocal design was intentionally mimicking Johansson's character, posting a one-word tweet after the presentation that simply said "her". Less than a week later, OpenAI felt compelled to explicitly clarify that Sky was not based on Johansson.