Group 1 - The core issue of AI models facing security vulnerabilities has been highlighted, with concerns about their potential misuse by malicious actors [1][4][5] - The recent amendment to the Cybersecurity Law emphasizes the need for improved AI ethics, risk monitoring, and security regulation to ensure the healthy development of AI applications [2][12] - Experts stress the urgency of building security barriers around AI models through technological innovation and industry collaboration [2][7] Group 2 - Various attack methods targeting AI models have emerged, including data poisoning, which can lead to the model producing incorrect outputs [3][5] - The phenomenon of "trust betrayal" among intelligent agents poses a new threat, where malicious agents can inject hidden commands into established dialogues [6][11] - The open-source nature of AI models introduces vulnerabilities that can affect all industry-specific models built on compromised foundations [6][11][13] Group 3 - The implementation of proactive defense strategies using AI technology is being explored to enhance cybersecurity measures [7][8] - The concept of "honey points" within AI models can be utilized to detect potential attacks before they occur, representing a shift towards preemptive security [8][9] - The need for a collaborative governance framework involving various stakeholders, including industry associations and academic institutions, is emphasized to address algorithmic ethics and security [10][12] Group 4 - The upcoming enforcement of the revised Cybersecurity Law is seen as a precursor to mandatory security measures for AI, aiming to balance innovation with safety [12][13] - The establishment of a third-party security certification and evaluation system is crucial for ensuring the transparency and effectiveness of AI model security [13]