DeepMind Blog 04月02日
Evaluating potential cybersecurity threats of advanced AI
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文章探讨了人工智能(AI)在网络安全领域的双重影响:既能增强防御,也能被用于提升攻击。文章提出了一个全面的框架,用于评估AI驱动的攻击能力,并帮助网络安全专家识别和优先考虑防御措施。通过分析大量真实世界的攻击尝试,作者构建了一个包含50个挑战的基准测试,旨在帮助防御者开发有针对性的缓解措施,并模拟AI驱动的攻击,从而提升网络安全防御水平。

🛡️ AI在网络安全领域中扮演着双重角色:一方面,它可以用于加强防御,例如检测恶意软件和分析网络流量;另一方面,它也可能被用于增强网络攻击,使得攻击更加自动化和复杂。

🔍 文章提出了一种新的评估框架,该框架借鉴了MITRE ATT&CK等现有的网络安全评估框架,并针对AI在攻击中的应用进行了特别的调整,涵盖了从侦察到目标行动的整个攻击链,并考虑了各种可能的攻击场景。

📊 研究人员分析了来自20个国家的超过12,000起真实世界中利用AI进行网络攻击的案例,从中发现了七种典型的攻击类别,例如网络钓鱼、恶意软件和拒绝服务攻击。他们还创建了一个包含50个挑战的基准测试,用于评估AI模型的网络安全优势和劣势,以帮助防御者制定有针对性的缓解措施。

💡 评估结果表明,虽然目前的AI模型在单独使用时可能无法实现突破性的攻击能力,但随着AI技术的进步,攻击方式将会演变,因此需要持续改进防御策略。研究还强调了AI在规避检测和维持长期访问等方面的潜在优势。

Artificial intelligence (AI) has long been a cornerstone of cybersecurity. From malware detection to network traffic analysis, predictive machine learning models and other narrow AI applications have been used in cybersecurity for decades. As we move closer to artificial general intelligence (AGI), AI's potential to automate defenses and fix vulnerabilities becomes even more powerful.

But to harness such benefits, we must also understand and mitigate the risks of increasingly advanced AI being misused to enable or enhance cyberattacks. Our new framework for evaluating the emerging offensive cyber capabilities of AI helps us do exactly this. It’s the most comprehensive evaluation of its kind to date: it covers every phase of the cyberattack chain, addresses a wide range of threat types, and is grounded in real-world data.

Our framework enables cybersecurity experts to identify which defenses are necessary—and how to prioritize them—before malicious actors can exploit AI to carry out sophisticated cyberattacks.

Building a comprehensive benchmark

Our updated Frontier Safety Framework recognizes that advanced AI models could automate and accelerate cyberattacks, potentially lowering costs for attackers. This, in turn, raises the risks of attacks being carried out at greater scale.

To stay ahead of the emerging threat of AI-powered cyberattacks, we’ve adapted tried-and-tested cybersecurity evaluation frameworks, such as MITRE ATT&CK. These frameworks enabled us to evaluate threats across the end-to-end cyber attack chain, from reconnaissance to action on objectives, and across a range of possible attack scenarios. However, these established frameworks were not designed to account for attackers using AI to breach a system. Our approach closes this gap by proactively identifying where AI could make attacks faster, cheaper, or easier—for instance, by enabling fully automated cyberattacks.

We analyzed over 12,000 real-world attempts to use AI in cyberattacks in 20 countries, drawing on data from Google’s Threat Intelligence Group. This helped us identify common patterns in how these attacks unfold. From these, we curated a list of seven archetypal attack categories—including phishing, malware, and denial-of-service attacks—and identified critical bottleneck stages along the cyberattack chain where AI could significantly disrupt the traditional costs of an attack. By focusing evaluations on these bottlenecks, defenders can prioritize their security resources more effectively.

Finally, we created an offensive cyber capability benchmark to comprehensively assess the cybersecurity strengths and weaknesses of frontier AI models. Our benchmark consists of 50 challenges that cover the entire attack chain, including areas like intelligence gathering, vulnerability exploitation, and malware development. Our aim is to provide defenders with the ability to develop targeted mitigations and simulate AI-powered attacks as part of red teaming exercises.

Insights from early evaluations

Our initial evaluations using this benchmark suggest that in isolation, present-day AI models are unlikely to enable breakthrough capabilities for threat actors. However, as frontier AI becomes more advanced, the types of cyberattacks possible will evolve, requiring ongoing improvements in defense strategies.

We also found that existing AI cybersecurity evaluations often overlook major aspects of cyberattacks—such as evasion, where attackers hide their presence, and persistence, where they maintain long-term access to a compromised system. Yet such areas are precisely where AI-powered approaches can be particularly effective. Our framework shines a light on this issue by discussing how AI may lower the barriers to success in these parts of an attack.

Empowering the cybersecurity community

As AI systems continue to scale, their ability to automate and enhance cybersecurity has the potential to transform how defenders anticipate and respond to threats.

Our cybersecurity evaluation framework is designed to support that shift by offering a clear view of how AI might also be misused, and where existing cyber protections may fall short. By highlighting these emerging risks, this framework and benchmark will help cybersecurity teams strengthen their defenses and stay ahead of fast-evolving threats.

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相关标签

人工智能 网络安全 AI攻击 安全框架
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