我可能，刚刚成为了哈基米的儿子。 至少，AI是这么认为的。 事情是这样的。 前两天，我在小红书上闲逛，无意间用他们的AI搜索功能，搜了一下影视飓风的李四维。 然后，就发现了一个神奇的AI回答。 李四维，是Tim的父亲。 嗯。。。 如果看过影视飓风的朋友都知道，左边这个就是李四维。。。 在李四维踹了一脚无影墙的那一刻，他勉强能算的上，是Tim的爹。。。 我打开了AI搜索参考的那三篇笔记。 发现问题好像出在最后一篇。 这篇的图片有"李四维是影视飓风创始人潘天鸿(Tim)的父亲"的AI总结，AI很可能就是从这里获取的错误信息。 真的，这玩意其实就是那种所谓的无意识投毒，就是，有人，在互联网上，写了一些内容，然后AI就信了，然后AI就开始到处跟别人说，李四维是Tim的 父亲。 再然后，就是以谣传谣，先污染了百度，然后又被用户分发以后，又污染了小红书。 当时觉得这个玩意很有意思，所以，我就想，自己也试一下玩玩。 其实吧，这种所谓的投毒或者GEO，有些时候，在一些冷门的话题下，想污染起来是很轻松的。 比如给我自己，也安排一个父亲，安排一个哈基米。 于是，我随手注册了一个小号。 随手发了一条笔记，内容写的就是"卡兹克是哈基 ...

Core Viewpoint - The article discusses the phenomenon of information pollution through AI, highlighting how misinformation can spread rapidly and be accepted as truth by AI systems, leading to potential harm to individuals and brands [27][45]. Group 1: Information Pollution Mechanism - AI can inadvertently spread false information based on erroneous data it encounters online, as demonstrated by the example of misidentifying a character's parentage [6][8]. - The author conducted experiments to illustrate how easily misinformation can be injected into AI systems, showing that even a newly created account can influence AI responses with the right prompts [12][15]. - The concept of Generative Engine Optimization (GEO) is introduced, where individuals can manipulate AI to promote specific narratives or discredit others, effectively turning misinformation into a business model [27][29]. Group 2: Impact on Individuals and Brands - The article highlights the risks posed to individuals, such as job candidates, who may be unfairly judged based on fabricated negative information generated by AI [30][31]. - It emphasizes the ease with which negative information can overshadow positive attributes, leading to reputational damage for brands and individuals alike [39][40]. - The author notes that the current landscape allows for the rapid dissemination of negative narratives, which can be more impactful than positive ones due to human nature's tendency to focus on negative information [41][42]. Group 3: Recommendations for Mitigation - The article suggests that individuals should not take AI responses at face value and should seek additional sources of information to verify claims [53]. - It encourages the preservation of original information sources to maintain a sense of perspective and awareness of biases in AI-generated content [54]. - The author advocates for contributing truthful content to counter misinformation, even if it seems insignificant, to help create a more balanced information environment [55][56].

Core Viewpoint - Security risk management is not merely a cost center but a value engine for companies to build brand reputation and gain market trust in the AI era [2][4]. Group 1: AI Risks and Security - AI risks have already become a reality, as evidenced by the recent vulnerability in the open-source model tool Ollama, which had an unprotected port [6][12]. - The notion of "exchanging privacy for convenience" is dangerous and can lead to irreversible risks, as AI can reconstruct personal profiles from fragmented data [6][10]. - AI risks are a "new species," and traditional methods are inadequate to address them due to their inherent complexities, such as algorithmic black boxes and model hallucinations [6][12]. - Companies must develop new AI security protection systems that adapt to these unique characteristics [6][12]. Group 2: Strategic Advantages of Security Compliance - Security compliance should be viewed as a strategic advantage rather than a mere compliance action, with companies encouraged to transform compliance requirements into internal risk control indicators [6][12]. - The approach to AI application registration should focus on enhancing risk management capabilities rather than just fulfilling regulatory requirements [6][15]. Group 3: Recommendations for Enterprises - Companies should adopt a mixed strategy of "core closed-source and peripheral open-source" models, using closed-source for sensitive operations and open-source for innovation [7][23]. - To ensure the long-term success of AI initiatives, companies should cultivate a mindset of curiosity, pragmatism, and respect for compliance [7][24]. - A systematic AI security compliance governance framework should be established, integrating risk management into the entire business lifecycle [7][24]. Group 4: Emerging Threats and Defense Mechanisms - "Prompt injection" attacks are akin to social engineering and require multi-dimensional defense mechanisms, including input filtering and sandbox isolation [7][19]. - Companies should implement behavior monitoring and context tracing to enhance security against sophisticated AI attacks [7][19][20]. - The debate between open-source and closed-source models is not binary; companies should choose based on their specific needs and risk tolerance [7][21][23].

Core Viewpoint - A recent investigation reveals that at least 14 top universities have embedded secret instructions in research papers that only AI can read, aimed at manipulating AI reviews to improve scores [2][3]. Group 1: Academic Integrity Issues - The investigation found at least 17 papers from 8 countries containing hidden instructions, primarily in computer science, using techniques like white text on a white background to embed commands [3][10]. - This practice raises concerns about the integrity of academic peer review, as AI could give inflated evaluations based on these hidden instructions, undermining the objectivity of academic assessments [7][10]. - Some researchers view this as a form of "justifiable defense" against lazy reviewers who rely on AI for evaluations, while others acknowledge the unethical nature of such actions [8][7]. Group 2: Prompt Injection Attacks - The incident highlights a new type of cyber attack known as "prompt injection," where attackers use cleverly designed instructions to bypass AI safety and ethical constraints, potentially leading to the dissemination of biased or harmful content [10][13]. - This technique can extend beyond academic papers, such as embedding positive instructions in resumes to manipulate AI screening systems [10]. Group 3: Regulatory Challenges - There is a growing concern over the lack of unified rules regarding AI usage in academic evaluations, with some publishers allowing AI use while others prohibit it due to bias risks [18]. - The urgency to establish clear regulations for AI use across various sectors is emphasized, as governments and academic institutions face the challenge of leveraging AI benefits while ensuring effective oversight [18].