我的征尘是星辰大海。。。
The dirt and dust from my pilgrimage forms oceans of stars...
-------当记忆的篇章变得零碎，当追忆的图片变得模糊，我们只能求助于数字存储的永恒的回忆
作者:黄教授
手机视频列表
大模型安全与两个时钟的启示
视频
音频
原始脚本
当大模型成为慢时钟，重思大模型安全风险的真实边界。 在大模型技术加速渗透企业的今天，关于安全风险的讨论从未停歇。 奇安信等安全厂商提出，大模型为企业引入了小数据安全危机、业务链式中断风险与决策失准风险三大隐患，认为大模型的引入让企业数据安全防线面临新的挑战。 这类观点并非空穴来风，确实有案例显示未加防护的大模型服务器可能被非法访问，模型输出的幻觉信息也可能干扰业务。 作为深耕网络安全与数据防护领域的企业，其对风险的敏锐感知有其行业合理性。 毕竟当企业开始将大模型融入核心业务流程，数据流转路径、权限访问逻辑都发生了新变化。 安全厂商基于自身技术视野与服务场景，强调这些新变化带来的风险，本质上是对企业数字化转型中 安全需求的回应。 但如果仅将目光聚焦于大模型带来的新风险，却忽视这些风险与传统系统隐患的关联，以及大模型自身具备的风险治理能力，就可能陷入因噎废食的认知误区。 要理解这一点，不妨从两个时钟的隐喻说起。 一个停摆的时钟，每天会有两次恰好显示正确时间，而一个没 小时慢5分钟的时钟，看似始终存在偏差，却能通过校准持续接近准确。 这个隐喻恰恰映射了传统系统与大模型系统的安全逻辑差异。 传统系统的安全，很多时候是风险休眠的假象。 比如企业内部的权限漏洞可能因长期未被攻击者盯上而看似无害，数据篡改风险 也可能因人力审计的滞后性而难以察觉，就像停摆的时钟，两次正确背后是全天的失效，只是这种失效未被激活。 而大模型所谓的安全隐患，更多是将传统系统中隐藏的风险显性化。 其安信担忧的小数据泄露风险，本质上是企业数据分级与权限管控机制的老问题。 在传统系统中，核心数据同样面临内部人员越权访问、外部黑客窃取的威胁。 只是大模型的检索与生成能力，让这种风险的爆发路径更具体。 比如员工通过 prompt 诱导模型输出敏感信息。 但这并非大模型创 创造了新漏洞，而是让原本就存在的权限管控缺陷浮出水面。 更关键的是，大模型并非只能放大风险，它本身就是解决风险的重要工具，这正是慢时钟的价值所在。 中国信通院大模型安全研究报告指出，大模型在逻辑推理、任务编排上的能力，能为网络安全瓶颈提供新解法。 奇安信自身推出的 AI SOC 智能网络安全运营平台，就将大模型嵌入安全研判、事件响应等环节。 在信源位置快速识别异常操作，这意味着大模型带来的风险显性化，同时伴随着风险可追溯的能力提升。 传统系统中，员工越权访问数据的行为可能隐藏在百万行日志之中。 人力审计需耗费数周时间，而在大模型辅助的安全系统中，不仅所有 prompt 检索路径、生成结果都可留痕，还能通过模型实时监测异常提问，比如频繁索要敏感数据的模糊指令，或试图诱导模型生成矛盾信息的恶意 prompt 。 这种用智能对抗智能的机制，让风险从难以察 觉变为可及时根愈，就像慢时钟，虽然存在偏差，但只要知道偏差规律就能校准。 而停摆的时钟，即便偶尔正确，也无法主动发现并修正问题。 再看业务链是中断与决策失准风险。 黑客若能控制员工账号，操纵大模型输出错误信息，本质上仍是企业身份认证与账号安全体系的问题。 在传统系统中，黑客同样可以利用被盗账号修改财务报表。 篡改库存数据。 但大模型的出现反而为这类风险增加了二次校验的可能。 通过大模型对业务数据的逻辑一致性进行实时核查。 比如 如发现订单金额与历史数据偏差过大、库存数量与采购记录矛盾等异常，就能及时阻断错误信息流入核心业务流程。 安全的本质从来不是追求绝对无风险，而是建立风险可控的治理体系。 奇安信等厂商对大模型风险的警示，提醒企业在数字化转型中不可忽视安全建设。 但这并不意味着要否定大模型的价值。 真正理性的选择是将大模型的安全风险纳入企业整体安全框架，利用大模型的可追溯性与智能分析能力，补及传统系统的安全短板。 黑客最害怕的从来不是企业没有漏洞，而是企业能及时发现并修复漏洞。 大模型带来的风险显性化，恰恰让企业有机会直面那些长期被忽视的安全隐患。 与其因担忧慢时钟的偏差而退回停摆时钟的假象安全，不如借助大模型的能力构建更具动态防御能力的安全体系。 毕竟，能被看见的风险才有可能被解决，而隐藏在看似安全之下的隐患才是企业数字化转型中最大的威胁。
修正脚本
当大模型成为慢时钟，重思大模型安全风险的真实边界。 在大模型技术加速渗透企业的今天，关于安全风险的讨论从未停歇。 奇安信等安全厂商提出，大模型为企业引入了小数据安全危机、业务链式中断风险与决策失准风险三大隐患，认为大模型的引入让企业数据安全防线面临新的挑战。 这类观点并非空穴来风，确实有案例显示未加防护的大模型服务器可能被非法访问，模型输出的幻觉信息也可能干扰业务。 作为深耕网络安全与数据防护领域的企业，其对风险的敏锐感知有其行业合理性。 毕竟当企业开始将大模型融入核心业务流程，数据流转路径、权限访问逻辑都发生了新变化。 安全厂商基于自身技术视野与服务场景，强调这些新变化带来的风险，本质上是对企业数字化转型中安全需求的回应。 但如果仅将目光聚焦于大模型带来的新风险，却忽视这些风险与传统系统隐患的关联，以及大模型自身具备的风险治理能力，就可能陷入因噎废食的认知误区。 要理解这一点，不妨从两个时钟的隐喻说起。 一个停摆的时钟，每天会有两次恰好显示正确时间，而一个每小时慢5分钟的时钟，看似始终存在偏差，却能通过校准持续接近准确。 这个隐喻恰恰映射了传统系统与大模型系统的安全逻辑差异。 传统系统的安全，很多时候是风险休眠的假象。 比如企业内部的权限漏洞可能因长期未被攻击者盯上而看似无害，数据篡改风险也可能因人力审计的滞后性而难以察觉，就像停摆的时钟，两次正确背后是全天的失效，只是这种失效未被激活。 而大模型所谓的安全隐患，更多是将传统系统中隐藏的风险显性化。 奇安信担忧的小数据泄露风险，本质上是企业数据分级与权限管控机制的老问题。 在传统系统中，核心数据同样面临内部人员越权访问、外部黑客窃取的威胁。 只是大模型的检索与生成能力，让这种风险的爆发路径更具体。 比如员工通过 prompt 诱导模型输出敏感信息。 但这并非大模型创造了新漏洞，而是让原本就存在的权限管控缺陷浮出水面。 更关键的是，大模型并非只能放大风险，它本身就是解决风险的重要工具，这正是慢时钟的价值所在。 中国信通院大模型安全研究报告指出，大模型在逻辑推理、任务编排上的能力，能为网络安全瓶颈提供新解法。 奇安信自身推出的 AI SOC 智能网络安全运营平台，就将大模型嵌入安全研判、事件响应等环节。 在信源未知时快速识别异常操作，这意味着大模型带来的风险显性化，同时伴随着风险可追溯的能力提升。 传统系统中，员工越权访问数据的行为可能隐藏在百万行日志之中。 人力审计需耗费数周时间，而在大模型辅助的安全系统中，不仅所有 prompt 检索路径、生成结果都可留痕，还能通过模型实时监测异常提问，比如频繁索要敏感数据的模糊指令，或试图诱导模型生成矛盾信息的恶意 prompt 。 这种用智能对抗智能的机制，让风险从难以察觉变为可及时根治，就像慢时钟，虽然存在偏差，但只要知道偏差规律就能校准。 而停摆的时钟，即便偶尔正确，也无法主动发现并修正问题。 再看业务链式中断与决策失准风险。 黑客若能控制员工账号，操纵大模型输出错误信息，本质上仍是企业身份认证与账号安全体系的问题。 在传统系统中，黑客同样可以利用被盗账号修改财务报表、篡改库存数据。 但大模型的出现反而为这类风险增加了二次校验的可能。 通过大模型对业务数据的逻辑一致性进行实时核查。 比如发现订单金额与历史数据偏差过大、库存数量与采购记录矛盾等异常，就能及时阻断错误信息流入核心业务流程。 安全的本质从来不是追求绝对无风险，而是建立风险可控的治理体系。 奇安信等厂商对大模型风险的警示，提醒企业在数字化转型中不可忽视安全建设。 但这并不意味着要否定大模型的价值。 真正理性的选择是将大模型的安全风险纳入企业整体安全框架，利用大模型的可追溯性与智能分析能力，补齐传统系统的安全短板。 黑客最害怕的从来不是企业没有漏洞，而是企业能及时发现并修复漏洞。 大模型带来的风险显性化，恰恰让企业有机会直面那些长期被忽视的安全隐患。 与其因担忧慢时钟的偏差而退回停摆时钟的安全假象，不如借助大模型的能力构建更具动态防御能力的安全体系。 毕竟，能被看见的风险才有可能被解决，而隐藏在看似安全之下的隐患才是企业数字化转型中最大的威胁。
英文翻译
When large models become slow clocks, rethink the true boundaries of large model security risks. As large model technology accelerates its penetration into enterprises, discussions about security risks never cease. Security vendors such as Qi-AnXin have pointed out that large models introduce three major hidden dangers for enterprises: small data security crises, business chain interruption risks, and decision-making accuracy risks, arguing that the introduction of large models poses new challenges to corporate data security defenses. Such views are not unfounded. Indeed, there are cases where unprotected large model servers can be illegally accessed, and hallucinated information output by the model may disrupt business operations. As a company deeply rooted in the fields of cybersecurity and data protection, its keen perception of risks is reasonable within its industry. After all, when enterprises begin to integrate large models into core business processes, data flow paths and permission access logic undergo new changes. Based on their own technical perspectives and service scenarios, security vendors emphasize the risks brought about by these new changes, essentially responding to the security needs of enterprise digital transformation. However, if we only focus on the new risks brought by large models while ignoring their connection with traditional system vulnerabilities, as well as the risk management capabilities inherent in large models themselves, we may fall into a cognitive misconception of "throwing the baby out with the bathwater." To understand this, it is helpful to start with the metaphor of two clocks. A stopped clock shows the correct time exactly twice a day, while a clock that runs five minutes slow per hour always appears to have a deviation, yet it can continually get closer to the accurate time through calibration. This metaphor precisely reflects the difference in security logic between traditional systems and large model systems. In traditional systems, security is often an illusion of dormant risks. For example, internal permission vulnerabilities may appear harmless because they have not been targeted by attackers for a long time, and the risk of data tampering may be difficult to detect due to the lag in manual audits—like a stopped clock, its two moments of correctness are overshadowed by a full day of failure, even if that failure remains unactivated. The so-called security risks of large models, on the other hand, are largely about making the hidden risks in traditional systems explicit. The risk of small data leakage that Qi-AnXin worries about is essentially an old problem of enterprise data classification and permission management mechanisms. In traditional systems, core data also faces threats from internal personnel exceeding authorized access and external hackers stealing it. It is just that the retrieval and generation capabilities of large models make the path of risk outbreak more concrete. For example, employees using prompts to induce the model to output sensitive information. But this is not about large models creating new vulnerabilities; rather, it brings to the surface permission management deficiencies that already existed. More critically, large models are not only capable of amplifying risks—they themselves are important tools for solving risks. This is precisely the value of the slow clock. According to the China Academy of Information and Communications Technology's report on large model security, the capabilities of large models in logical reasoning and task orchestration can provide new solutions to bottlenecks in network security. Qi-AnXin itself has launched an AI SOC intelligent cybersecurity operations platform, embedding large models into security analysis, incident response, and other processes. When the source of information is unknown, it can quickly identify abnormal operations, which means that the explicitness of risks brought by large models comes with an enhanced ability to trace risks. In traditional systems, an employee's unauthorized access to data might be hidden among millions of lines of logs. Manual audits can take weeks, while in a large model-assisted security system, not only can all prompt retrieval paths and generated results be recorded, but the model can also monitor abnormal queries in real time—such as vague instructions frequently requesting sensitive data or malicious prompts attempting to induce the model to generate conflicting information. This mechanism of using intelligence to counter intelligence transforms risks from hard-to-detect to promptly curable, much like the slow clock: even though there is a deviation, as long as the pattern of deviation is known, it can be calibrated. In contrast, the stopped clock, even when occasionally correct, cannot proactively discover and correct issues. Now consider the risks of business chain interruption and decision-making inaccuracy. If hackers can control employee accounts and manipulate the large model to output erroneous information, this is essentially a problem of enterprise identity authentication and account security systems. In traditional systems, hackers could similarly use stolen accounts to modify financial statements or tamper with inventory data. However, the emergence of large models actually adds the possibility of secondary verification for such risks. Through real-time logical consistency checks of business data by large models. For example, detecting anomalies such as order amounts deviating significantly from historical data, or contradictions between inventory quantities and procurement records, can promptly block erroneous information from entering core business processes. The essence of security has never been about pursuing absolute risk-free conditions, but about establishing a governance system where risks are controllable. Warnings from vendors like Qi-AnXin about large model risks remind enterprises not to neglect security construction during digital transformation. But this does not mean denying the value of large models. The truly rational choice is to integrate the security risks of large models into the overall enterprise security framework, using the traceability and intelligent analysis capabilities of large models to compensate for the security shortcomings of traditional systems. What hackers fear most is never that enterprises have vulnerabilities, but that enterprises can discover and fix vulnerabilities in a timely manner. The explicitness of risks brought by large models precisely gives enterprises the opportunity to confront those long-neglected security hazards. Rather than retreating from the deviation of the slow clock back to the illusion of security from the stopped clock, it is better to leverage the capabilities of large models to build a security system with more dynamic defense capabilities. After all, only risks that can be seen have a chance to be resolved, and the hidden hazards lurking under a seemingly secure surface are the greatest threat to enterprise digital transformation.
back to top