2026年9月安全回顾：利用合法功能与AI执行能力的攻击
网络安全形势持续快速发展，随着技术的进步，新的威胁不断涌现。近期对2026年9月公开信息的回顾揭示了数个关键安全问题，组织必须加以应对。这些问题围绕利用合法连接和权限在入侵后进行横向移动的攻击、嵌入在软件供应链中的恶意代码在正常使用下激活，以及管理AI执行能力的挑战。理解这些威胁对于制定稳健的防御策略至关重要。
利用合法功能的攻击
2026年9月回顾中观察到最阴险的战术之一是利用合法功能与权限在初始入侵后进行网络横向移动。这种方法利用了合法访问已建立的信任，使其难以检测和缓解。
工作原理
攻击者通常利用合法服务、API或用户账户在网络中获得立足点。进入内部后，他们使用这些相同的合法连接来导航环境，访问敏感数据或系统而不会触发传统安全警报。这种方法特别有效，因为它模拟了正常用户行为，使入侵检测系统（IDS）更难识别恶意活动。
例如，攻击者可能获得具有管理员权限的用户账户的访问权限。使用此账户，他们可以访问各种系统和服务，包括通常受限的那些。通过这样做，他们避免了检测，同时执行其恶意目标。这种技术被称为“凭证滥用”，对严重依赖用户账户进行访问控制的组织具有毁灭性。
缓解策略
为应对这些攻击，组织必须实施多层防御措施：
- 最小权限访问：确保用户和服务仅拥有执行其任务所需的最低必要权限。这减少了攻击者在获得账户访问权限时可能造成的潜在损害。
- 用户行为分析（UBA）：实施UBA系统，监控用户行为并检测可能表明恶意活动的异常情况。这些系统可以识别异常模式，例如访问受限文件或进行意外网络连接。
- 特权访问管理（PAM）：使用PAM解决方案来管理和监控特权账户。这些工具可以提供特权账户使用的可见性，并帮助检测未经授权的访问。
软件供应链中的恶意代码
回顾中另一个重大关注点是软件供应链中存在在正常使用下或分发恢复后激活的恶意代码。这一威胁突出了在整个软件开发生命周期中确保安全的重要性。
供应链攻击面
软件供应链包括开发、构建、测试和交付软件的所有组件，包括第三方库、依赖项和开发工具。攻击者可以利用这些组件中的漏洞来注入恶意代码，然后将其分发给最终用户。
例如，攻击者可能攻陷一个流行的开源库并在其中嵌入后门。包括此库的开发者在他们的项目中无意中引入了恶意代码。当软件部署时，攻击者可以激活后门以获得对受影响系统的未经授权的访问。
缓解策略
保护软件供应链需要一个全面的方法：
- 依赖项扫描：使用OWASP Dependency-Check或Snyk等工具定期扫描第三方依赖项中的已知漏洞。这有助于在开发过程中早期识别和缓解潜在风险。
- 安全编码实践：采用安全编码实践以最大程度地降低漏洞被引入代码库的风险。这包括代码审查、静态分析和动态分析。
- 供应链透明度：通过记录所有依赖项及其来源，在软件供应链中保持透明度。这使得跟踪和管理潜在风险变得更加容易。
AI执行能力的治理
人工智能（AI）已成为现代系统日益不可或缺的一部分，但其执行能力也带来了新的安全挑战。2026年9月回顾关注AI治理问题，特别是如何管理AI执行范围。
无控制AI的风险
AI系统可以是强大的工具，但如果其执行没有得到适当治理，它们也带来重大风险。例如，设计为执行特定任务的AI系统，如果其执行范围没有得到严格控制，可能会以意想不到的方式访问或操作数据。
一个值得注意的例子是AI在自主系统中的应用，例如自动驾驶汽车或无人机。如果AI的决策过程不透明或其执行范围定义不明确，可能会导致灾难性后果。
缓解策略
为应对这些风险，组织必须为AI实施稳健的治理框架：
- 执行边界：为AI系统定义明确的执行边界，确保它们仅执行授权任务。这可以通过基于角色的访问控制（RBAC）和其他访问管理技术来实现。
- 监控和日志记录：实施全面的监控和日志记录，以跟踪AI系统的行为。这有助于实时检测和响应异常活动。
- 伦理AI原则：采用伦理AI原则，优先考虑透明度、问责制和公平性。这包括记录AI系统的决策过程，并确保它们与组织目标和价值观保持一致。
总结
2026年9月回顾中强调的安全挑战突出了采取积极主动和全面方法进行网络安全的需求。利用合法功能的攻击、软件供应链中的恶意代码以及AI执行能力的治理都需要仔细考虑和缓解策略。
通过实施稳健的防御措施，如最小权限访问、用户行为分析、依赖项扫描、安全编码实践和AI治理框架，组织可以更好地保护自己免受这些不断变化的威胁。关键在于保持警惕，持续评估风险，并根据新挑战的出现调整安全策略。
September 2026 Security Review: Attacks Following Legitimate Features and AI Execution Capabilities
The landscape of cybersecurity continues to evolve at a rapid pace, with new threats emerging alongside advancements in technology. A recent review of publicly available information from September 2026 has brought to light several critical security concerns that organizations must address. These issues revolve around attacks that exploit legitimate connections and privileges for lateral movement post-infiltration, malicious code embedded in software supply chains that activates under normal use, and the challenges of governing AI execution capabilities. Understanding these threats is essential for developing robust defensive strategies.
Attacks Following Legitimate Features
One of the most insidious tactics observed in the September 2026 review is the use of attacks that follow legitimate features and privileges to move laterally within a network after initial infiltration. This approach leverages the trust already established by legitimate access, making it difficult to detect and mitigate.
How It Works
Attackers often exploit legitimate services, APIs, or user accounts to gain a foothold in a network. Once inside, they use these same legitimate connections to navigate the environment, accessing sensitive data or systems without triggering traditional security alerts. This method is particularly effective because it mimics normal user behavior, making it harder for intrusion detection systems (IDS) to identify malicious activity.
For example, an attacker might gain access to a user account with administrative privileges. Using this account, they can access various systems and services, including those that are typically restricted. By doing so, they avoid detection while carrying out their malicious objectives. This technique is known as "credential abuse" and can be devastating to organizations that rely heavily on user accounts for access control.
Mitigation Strategies
To combat these attacks, organizations must implement multi-layered defensive measures:
- Least Privilege Access: Ensure that users and services have the minimum necessary privileges to perform their tasks. This reduces the potential damage an attacker can cause if they gain access to an account.
- User Behavior Analytics (UBA): Implement UBA systems that monitor user behavior and detect anomalies that could indicate malicious activity. These systems can identify unusual patterns, such as accessing restricted files or making unexpected network connections.
- Privileged Access Management (PAM): Use PAM solutions to manage and monitor privileged accounts. These tools can provide visibility into how privileged accounts are used and help detect unauthorized access.
Malicious Code in Software Supply Chains
Another significant concern highlighted in the review is the presence of malicious code in software supply chains that activates during normal use or after distribution resumes. This threat underscores the importance of securing the entire software development lifecycle.
The Supply Chain Attack Surface
The software supply chain includes all the components involved in developing, building, testing, and delivering software, including third-party libraries, dependencies, and development tools. Attackers can exploit vulnerabilities in these components to inject malicious code, which can then be distributed to end-users.
For instance, an attacker might compromise a popular open-source library and embed a backdoor in it. Developers who include this library in their projects inadvertently introduce the malicious code to their applications. When the software is deployed, the attacker can activate the backdoor to gain unauthorized access to affected systems.
Mitigation Strategies
Securing the software supply chain requires a comprehensive approach:
- Dependency Scanning: Regularly scan third-party dependencies for known vulnerabilities using tools like OWASP Dependency-Check or Snyk. This helps identify and mitigate potential risks early in the development process.
- Secure Coding Practices: Adopt secure coding practices to minimize the risk of vulnerabilities being introduced into the codebase. This includes code reviews, static analysis, and dynamic analysis.
- Supply Chain Transparency: Maintain transparency in the software supply chain by documenting all dependencies and their sources. This makes it easier to track and manage potential risks.
Governance Over AI Execution Capabilities
Artificial intelligence (AI) has become increasingly integral to modern systems, but its execution capabilities also present new security challenges. The September 2026 review highlighted concerns about the governance of AI, particularly how the scope of AI execution is managed.
The Risks of Uncontrolled AI
AI systems can be powerful tools, but they also pose significant risks if their execution is not properly governed. For example, an AI system might be designed to perform specific tasks, but if its execution scope is not tightly controlled, it could potentially access or manipulate data in unintended ways.
One notable example is the use of AI in autonomous systems, such as self-driving cars or drones. If the AI's decision-making processes are not transparent or if its execution scope is not well-defined, it could lead to catastrophic consequences.
Mitigation Strategies
To address these risks, organizations must implement robust governance frameworks for AI:
- Execution Boundaries: Define clear execution boundaries for AI systems to ensure they only perform authorized tasks. This can be achieved through role-based access control (RBAC) and other access management techniques.
- Monitoring and Logging: Implement comprehensive monitoring and logging to track the behavior of AI systems. This helps detect and respond to anomalous activity in real-time.
- Ethical AI Principles: Adopt ethical AI principles that prioritize transparency, accountability, and fairness. This includes documenting the decision-making processes of AI systems and ensuring they are aligned with organizational goals and values.
Takeaway
The security challenges highlighted in the September 2026 review underscore the need for a proactive and holistic approach to cybersecurity. Attacks that exploit legitimate features, malicious code in software supply chains, and the governance of AI execution capabilities all require careful consideration and mitigation strategies.
By implementing robust defensive measures such as least privilege access, user behavior analytics, dependency scanning, secure coding practices, and AI governance frameworks, organizations can better protect themselves against these evolving threats. The key is to stay vigilant, continuously assess risks, and adapt security strategies to address new challenges as they arise.
