论文标题
测量和减轻公共云上IP再利用的风险
Measuring and Mitigating the Risk of IP Reuse on Public Clouds
论文作者
论文摘要
公共云通过资源共享提供可扩展且成本效益的计算。但是,从传统的本地服务管理转变为云引入了新的挑战。无法正确提供,维护或退役弹性服务会导致功能失败和攻击脆弱性。在本文中,我们探讨了对云的广泛攻击,我们称这是云蹲。在云蹲攻击中,对手在云中分配资源(例如,IP地址),然后利用潜在配置来利用先前的租户。为了测量和分类云蹲,我们在Amazon Web Services US-EAST-1区域内部署了自定义的Internet望远镜。使用此设备,我们在2021年3月开始的101天内部署了超过300万台服务器,收到了150万个独特的IP地址(占可用池的56%)。我们确定了4类云服务,7类的第三方服务和DNS作为可利用的潜在配置的来源。我们发现可剥削的配置既常见,又在许多情况下极为危险。我们收到了超过500万个云消息,其中许多包含敏感数据,例如金融交易,GPS位置和PII。在7类的第三方服务类别中,我们确定了数十个跨越数百个服务器(例如数据库,卡车,移动应用程序和Web服务)的可剥削软件系统。最后,我们确定了5446个可剥削的域,跨越了231个etlds,其中包括105架的105个,在前1000名流行域中的23个域。通过租户披露,我们确定了几个根本原因,包括(a)缺乏组织控制,(b)服务卫生不良,以及(c)未能遵循最佳实践。最后,我们讨论了可能的缓解空间,并描述了亚马逊为响应这项研究而部署的缓解措施。
Public clouds provide scalable and cost-efficient computing through resource sharing. However, moving from traditional on-premises service management to clouds introduces new challenges; failure to correctly provision, maintain, or decommission elastic services can lead to functional failure and vulnerability to attack. In this paper, we explore a broad class of attacks on clouds which we refer to as cloud squatting. In a cloud squatting attack, an adversary allocates resources in the cloud (e.g., IP addresses) and thereafter leverages latent configuration to exploit prior tenants. To measure and categorize cloud squatting we deployed a custom Internet telescope within the Amazon Web Services us-east-1 region. Using this apparatus, we deployed over 3 million servers receiving 1.5 million unique IP addresses (56% of the available pool) over 101 days beginning in March of 2021. We identified 4 classes of cloud services, 7 classes of third-party services, and DNS as sources of exploitable latent configurations. We discovered that exploitable configurations were both common and in many cases extremely dangerous; we received over 5 million cloud messages, many containing sensitive data such as financial transactions, GPS location, and PII. Within the 7 classes of third-party services, we identified dozens of exploitable software systems spanning hundreds of servers (e.g., databases, caches, mobile applications, and web services). Lastly, we identified 5446 exploitable domains spanning 231 eTLDs-including 105 in the top 10,000 and 23 in the top 1000 popular domains. Through tenant disclosures we have identified several root causes, including (a) a lack of organizational controls, (b) poor service hygiene, and (c) failure to follow best practices. We conclude with a discussion of the space of possible mitigations and describe the mitigations to be deployed by Amazon in response to this study.