This paper analyzes online developer discussions to identify four major security concerns—data leakage, code licensing, adversarial attacks, and insecure suggestions—associated with using generative A…

The paper demonstrates that the AI-like style introduced by post-training alignment can be measured, localized, and causally removed using a novel ablation technique called PASTA.

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by GenAI, moving beyond traditional react…

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by Generative AI, moving beyond tradition…

The paper proposes IntraGuard, a black-box, venue-agnostic defense framework that embeds hidden instructions into manuscripts via PDF structure to disrupt AI-generated peer reviews, achieving up to 84…

The paper outlines the potential for using generative AI to conduct large-scale, simulation-based experiments in literary studies, demonstrating initial results in generating constrained literary text…

This paper demonstrates that targeted adaptation of the small language model Phi Silica, using dataset curation and fine-tuning, significantly improves its performance in short-form text rewriting, na…

The paper analyzes the escalating security and safety threats posed by generative AI systems as they transition from merely generating content to executing real-world actions via tools and agents, fin…

Analyzing Reddit discussions, the paper finds that while security practitioners see LLMs as useful for boosting productivity, their adoption is constrained by concerns over reliability, verification,…

This paper systematically measured web tracking across 20 popular AI chatbots, finding that a majority share both conversational content and user identity information with third parties.

This study analyzes global usage patterns of generative AI among early adopters, finding that usage varies significantly by country income, with schooling being the primary use in low-income countries…

The study compared the cybersecurity risk assessment capabilities of five popular large language models (LLMs) against human experts, finding that LLMs consistently underestimated risks and require ma…

The paper demonstrates that generative AI can automate and scale highly personalized, context-aware spear-phishing attacks using only public social media data, resulting in messages that are significa…

The paper introduces OpAI-Bench, a novel benchmark designed to study how AI authorship signals evolve and accumulate during the progressive co-editing process between humans and AI.

This paper reviews current trends in AI-based cybersecurity, specifically analyzing various AI techniques applied to intrusion detection to provide comparative insights.

This study analyzes ClinicalTrials.gov records to track the rising trend of AI in clinical trials and demonstrates that a hybrid human-AI screening approach is viable but requires clearer reporting of…

This study investigates user perceptions of privacy risks associated with GenAI smartphones, finding that users express heightened concerns across the entire data lifecycle and suggest comprehensive,…

This paper investigates the vulnerability of LLM-based automatic grading systems to prompt injection (PI) attacks, demonstrating that current systems are highly susceptible to manipulation that can le…

The paper proposes a secure-by-design Generative AI framework that integrates PromptShield for LLM security and CIAF for structured cloud forensic investigation, significantly improving both robustnes…

The paper investigates how users perceive and utilize security and privacy transparency in consumer-facing generative AI, finding that users rely on proxies like popularity and require actionable, tru…