Core Viewpoint - The rapid advancement of generative artificial intelligence (AIGC) has made it increasingly difficult for internet users to distinguish between true and false content, leading to a proliferation of AI-generated misinformation [1][3]. Regulation and Responsibility - The National Internet Information Office and three other departments have introduced the "Artificial Intelligence Generated Synthetic Content Identification Measures," effective from September 1, requiring explicit and implicit labeling for all AI-generated content [3][10]. - The new regulation places the primary responsibility for AI-generated content on the content creators, marking a significant shift from previous content management systems established by platforms like WeChat and Douyin [3][14]. AI Misuse and Challenges - AI has become a major tool for misinformation, with examples of scams and fraudulent activities utilizing AI-generated content [5][6]. - The emergence of user-friendly AI technologies has made it easier for malicious actors to create deceptive content, as seen with the rise of deepfake technology [6][7]. Safety Measures and Limitations - Major tech companies are developing "AI Guardrails" to prevent harmful content generation, but these measures face inherent limitations due to the need for AI models to maintain a degree of autonomy [9][10]. - The balance between safety and functionality is challenging, as overly strict safety measures could render AI models ineffective [10]. Watermarking and Content Authenticity - Companies like Microsoft, Adobe, and OpenAI have formed the C2PA alliance to implement watermarking techniques to distinguish AI-generated content from human-created works, but these watermarks can be easily removed [12]. - Current operational strategies by internet platforms to require creators to disclose AI-generated content have not been effective, as many creators fear that such disclosures will limit their content's reach [12][14].

Core Viewpoint - Digital human live streaming is a hot concept in the current live e-commerce industry, with brands increasingly opting for cost-effective digital humans over real hosts, but there are significant vulnerabilities such as prompt injection attacks that can disrupt the process [1][3][14]. Group 1: Digital Human Live Streaming - Digital human hosts are being used by brands for live streaming sales due to their cost-effectiveness, operating 24/7 without the need for physical resources [14]. - The recent incident of a digital human host executing unrelated commands due to a prompt injection attack highlights the risks associated with this technology [3][17]. - The technology behind digital humans is often not well understood by the merchants using them, leading to potential security vulnerabilities [14][15]. Group 2: Prompt Injection Attacks - Prompt injection is a method where users can manipulate AI responses by issuing specific commands, as demonstrated when a digital human mistakenly responded to a non-relevant prompt [3][7]. - The inability of AI systems to distinguish between trusted developer commands and untrusted user inputs raises concerns about security and reliability [10]. - Previous incidents, such as attacks on ChatGPT and Microsoft Copilot, illustrate that prompt injection is a widespread issue affecting various AI applications [7][12]. Group 3: AI Security Measures - AI guardrails are necessary to ensure that AI systems operate within human expectations and do not generate harmful content or leak sensitive information [10][12]. - Current AI security measures are not fully equipped to handle the unique risks posed by AI models, particularly in the context of prompt injection attacks [10][12]. - Developers face the challenge of balancing AI performance and security, as overly stringent guardrails can hinder the AI's ability to generate high-quality responses [12][14].