No abstract

We show how third-party web trackers can deanonymize users of cryptocurrencies. We present two distinct but complementary attacks. On most shopping websites, third party trackers receive information about user purchases for purposes of advertising and analytics. We show that, if the user pays using a cryptocurrency, trackers typically possess enough information about the purchase to uniquely identify the transaction on the blockchain, link it to the user's cookie, and further to the user's real identity. Our second attack shows that if the tracker is able to link two purchases of the same user to the blockchain in this manner, it can identify the user's entire cluster of addresses and transactions on the blockchain, even if the user employs blockchain anonymity techniques such as CoinJoin. The attacks are passive and hence can be retroactively applied to past purchases. We discuss several mitigations, but none are perfect.

Library OSes are a promising approach for applications to efficiently obtain the benefits of virtual machines, including security isolation, host platform compatibility, and migration. Library OSes refactor a traditional OS kernel into an application library, avoiding overheads incurred by duplicate functionality. When compared to running a single application on an OS kernel in a VM, recent library OSes reduce the memory footprint by an order-of-magnitude.Previous library OS (libOS) research has focused on single-process applications, yet many Unix applications, such as network servers and shell scripts, span multiple processes. Key design challenges for a multi-process libOS include management of shared state and minimal expansion of the security isolation boundary. This paper presents Graphene, a library OS that seamlessly and efficiently executes both single and multi-process applications, generally with low memory and performance overheads. Graphene broadens the libOS paradigm to support secure, multi-process APIs, such as copy-on-write fork, signals, and System V IPC. Multiple libOS instances coordinate over pipe-like byte streams to implement a consistent, distributed POSIX abstraction. These coordination streams provide a simple vantage point to enforce security isolation.

No abstract

Despite the emphasis on building secure software, the number of vulnerabilities found in our systems is increasing every year, and well-understood vulnerabilities continue to be exploited. A common response to vulnerabilities is patch-based mitigation, which does not completely address the flaw and is often circumvented by an adversary. The problem actually lies in a lack of understanding of the nature of vulnerabilities. Vulnerability taxonomies have been proposed, but their usability is limited because of their ambiguity and complexity. This paper presents a taxonomy that views vulnerabilities as fractures in the interpretation of information as it flows in the system. It also presents a machine learning study validating the taxonomy's unambiguity. A manually labeled set of 641 vulnerabilities trained a classifier that automatically categorized more than 70000 vulnerabilities from three distinct databases with an average success rate of 80%. Important lessons learned are discussed such as (i) approximately 12% of the studied reports provide insufficient information about vulnerabilities, and (ii) the roles of the reporter and developer are not leveraged, especially regarding information about tools used to find vulnerabilities and approaches to address them.