Hardening High-Assurance Security Systems with Trusted Computing

We are living in the time of the digital revolution in which the world we know changes beyond recognition every decade. The positive aspect is that these changes also drive the progress in quality and availability of digital assets crucial for our societies. To name a few examples, these are broadly available communication channels allowing quick exchange of knowledge over long distances, systems controlling automatic share and distribution of renewable energy in international power grid networks, easily accessible applications for early disease detection enabling self-examination without burdening the health service, or governmental systems assisting citizens to settle official matters without leaving their homes. Unfortunately, however, digitalization also opens opportunities for malicious actors to threaten our societies if they gain control over these assets after successfully exploiting vulnerabilities in the complex computing systems building them. Protecting these systems, which are called high-assurance security systems, is therefore of utmost importance. For decades, humanity has struggled to find methods to protect high-assurance security systems. The advancements in the computing systems security domain led to the popularization of hardware-assisted security techniques, nowadays available in commodity computers, that opened perspectives for building more sophisticated defense mechanisms at lower costs. However, none of these techniques is a silver bullet. Each one targets particular use cases, suffers from limitations, and is vulnerable to specific attacks. I argue that some of these techniques are synergistic and help overcome limitations and mitigate specific attacks when used together. My reasoning is supported by regulations that legally bind high-assurance security systems' owners to provide strong security guarantees. These requirements can be fulfilled with the help of diverse technologies that have been standardized in the last years. In this thesis, I introduce new techniques for hardening high-assurance security systems that execute in remote execution environments, such as public and hybrid clouds. I implemented these techniques as part of a framework that provides technical assurance that high-assurance security systems execute in a specific data center, on top of a trustworthy operating system, in a virtual machine controlled by a trustworthy hypervisor or in strong isolation from other software. I demonstrated the practicality of my approach by leveraging the framework to harden real-world applications, such as machine learning applications in the eHealth domain. The evaluation shows that the framework is practical. It induces low performance overhead (<6%), supports software updates, requires no changes to the legacy application's source code, and can be tailored to individual trust boundaries with the help of security policies. The framework consists of a decentralized monitoring system that offers better scalability than traditional centralized monitoring systems. Each monitored machine runs a piece of code that verifies that the machine's integrity and geolocation conform to the given security policy. This piece of code, which serves as a trusted anchor on that machine, executes inside the trusted execution environment, i.e., Intel SGX, to protect itself from the untrusted host, and uses trusted computing techniques, such as trusted platform module, secure boot, and integrity measurement architecture, to attest to the load-time and runtime integrity of the surrounding operating system running on a bare metal machine or inside a virtual machine. The trusted anchor implements my novel, formally proven protocol, enabling detection of the TPM cuckoo attack. The framework also implements a key distribution protocol that, depending on the individual security requirements, shares cryptographic keys only with high-assurance security systems executing in the predefined security settings, i.e., inside the trusted execution environments or inside the integrity-enforced operating system. Such an approach is particularly appealing in the context of machine learning systems where some algorithms, like the machine learning model training, require temporal access to large computing power. These algorithms can execute inside a dedicated, trusted data center at higher performance because they are not limited by security features required in the shared execution environment. The evaluation of the framework showed that training of a machine learning model using real-world datasets achieved 0.96x native performance execution on the GPU and a speedup of up to 1560x compared to the state-of-the-art SGX-based system. Finally, I tackled the problem of software updates, which makes the operating system's integrity monitoring unreliable due to false positives, i.e., software updates move the updated system to an unknown (untrusted) state that is reported as an integrity violation. I solved this problem by introducing a proxy to a software repository that sanitizes software packages so that they can be safely installed. The sanitization consists of predicting and certifying the future (after the specific updates are installed) operating system's state. The evaluation of this approach showed that it supports 99.76% of the packages available in Alpine Linux main and community repositories. The framework proposed in this thesis is a step forward in verifying and enforcing that high-assurance security systems execute in an environment compliant with regulations. I anticipate that the framework might be further integrated with industry-standard security information and event management tools as well as other security monitoring mechanisms to provide a comprehensive solution hardening high-assurance security systems

Glamdring: automatic application partitioning for Intel SGX

Trusted execution support in modern CPUs, as offered by Intel SGX enclaves , can protect applications in untrusted environments. While prior work has shown that legacy applications can run in their entirety inside enclaves, this results in a large trusted computing base (TCB). Instead, we explore an approach in which we partition an applica- tion and use an enclave to protect only security-sensitive data and functions, thus obtaining a smaller TCB. We describe Glamdring , the first source-level parti- tioning framework that secures applications written in C using Intel SGX. A developer first annotates security- sensitive application data. Glamdring then automatically partitions the application into untrusted and enclave parts: (i) to preserve data confidentiality, Glamdring uses dataflow analysis to identify functions that may be ex- posed to sensitive data; (ii) for data integrity, it uses back- ward slicing to identify functions that may affect sensitive data. Glamdring then places security-sensitive functions inside the enclave, and adds runtime checks and crypto- graphic operations at the enclave boundary to protect it from attack. Our evaluation of Glamdring with the Mem- cached store, the LibreSSL library, and the Digital Bitbox bitcoin wallet shows that it achieves small TCB sizes and has acceptable performance overheads

Can NSEC5 be practical for DNSSEC deployments?

NSEC5 is proposed modification to DNSSEC that simultaneously guarantees two security properties: (1) privacy against offline zone enumeration, and (2) integrity of zone contents, even if an adversary compromises the authoritative nameserver responsible for responding to DNS queries for the zone. This paper redesigns NSEC5 to make it both practical and performant. Our NSEC5 redesign features a new fast verifiable random function (VRF) based on elliptic curve cryptography (ECC), along with a cryptographic proof of its security. This VRF is also of independent interest, as it is being standardized by the IETF and being used by several other projects. We show how to integrate NSEC5 using our ECC-based VRF into the DNSSEC protocol, leveraging precomputation to improve performance and DNS protocol-level optimizations to shorten responses. Next, we present the first full-fledged implementation of NSEC5—extending widely-used DNS software to present a nameserver and recursive resolver that support NSEC5—and evaluate their performance under aggressive DNS query loads. Our performance results indicate that our redesigned NSEC5 can be viable even for high-throughput scenarioshttps://eprint.iacr.org/2017/099.pdfFirst author draf