Raffaele Sommese

Sulyab Thottungal Valapu, John Heidemann, Mattijs Jonker et al.

DNS integrations leverage the discovery, trust, and uniqueness of the global Domain Name System with a linkage to another naming ecosystem, so the DNS name can help identify resources such as a cryptocurrency wallet or software component. While DNS ownership is verified at linkage creation, many ecosystems do not track subsequent DNS changes. The result is zombie linkages, where the DNS ownership has expired or changed, but the mapping to the linked resource persists. We define a threat model for DNS integrations, identifying five classes of attacks that leverage or exploit zombie linkages. We measure zombie occurrence across three DNS integrations -- Web PKI; ENS, a blockchain naming system; and Maven Central, a Java software repository. We show that zombies exist in every ecosystem, but at very different fractions -- zombies make up roughly 3% of TLS certificates for new domains, 24% of ENS on-chain imports, and 15% of Maven Central namespaces. We evaluate how integration design choices affect outcomes, with validate-once integrations (ENS on-chain, Maven Central) accumulating long-lasting zombies, linkages with expiration (Web PKI) limiting damage, while integrations that validate on every use (ENS gasless) are zombie-free by design. We look for specific attacks, finding attacks actively available for exploitation in both Web PKI and Maven Central. Finally, we recommend steps to reduce zombie occurrence.

Olivier van der Toorn, Raffaele Sommese, Anna Sperotto et al.

Given the importance of privacy, many Internet protocols are nowadays designed with privacy in mind (e.g., using TLS for confidentiality). Foreseeing all privacy issues at the time of protocol design is, however, challenging and may become near impossible when interaction out of protocol bounds occurs. One demonstrably not well understood interaction occurs when DHCP exchanges are accompanied by automated changes to the global DNS (e.g., to dynamically add hostnames for allocated IP addresses). As we will substantiate, this is a privacy risk: one may be able to infer device presence and network dynamics from virtually anywhere on the Internet -- and even identify and track individuals -- even if other mechanisms to limit tracking by outsiders (e.g., blocking pings) are in place. We present a first of its kind study into this risk. We identify networks that expose client identifiers in reverse DNS records and study the relation between the presence of clients and said records. Our results show a strong link: in 9 out of 10 cases, records linger for at most an hour, for a selection of academic, enterprise and ISP networks alike. We also demonstrate how client patterns and network dynamics can be learned, by tracking devices owned by persons named Brian over time, revealing shifts in work patterns caused by COVID-19 related work-from-home measures, and by determining a good time to stage a heist.