Mobile phone security is a relatively new field that is gathering momentum in the wake of rapid advancements in phone system technology. Mobile phones are now becoming sophisticated smart phones that provide services beyond basic telephony, such as supporting third-party applications. Such third-party applications may be security-critical, such as mobile banking, or may be untrusted applications, such as downloaded games. Our goal is to protect the integrity of such critical applications from potentially untrusted functionality, but we find that existing mandatory access control approaches are too complex and do not provide formal integrity guarantees. In this work, we leverage the simplicity inherent to phone system environments to develop a compact SELinux policy that can be used to justify the integrity of a phone system using the Policy Reduced Integrity Measurement Architecture (PRIMA) approach. We show that the resultant policy enables systems to be proven secure to remote parties, enables the desired functionality for installing and running trusted programs, and the resultant SELinux policy is over 90% smaller in size. We envision that this approach can provide an outline for how to build high integrity phone systems.

Hardware support for trusted execution in modern CPUs enables tenants to shield their data processing workloads in otherwise untrusted cloud environments. Runtime systems for the trusted execution must rely on an interface to the untrusted host OS to use external resources such as storage, network, and other functions. Attackers may exploit this interface to leak data or corrupt the computation.We describe SGX-LKL, a system for running Linux binaries inside of Intel SGX enclaves that only exposes a minimal, protected and oblivious host interface: the interface is (i) minimal because SGX-LKL uses a complete library OS inside the enclave, including file system and network stacks, which requires a host interface with only 7 calls; (ii) protected because SGX-LKL transparently encrypts and integrity-protects all data passed via low-level I/O operations; and (iii) oblivious because SGX-LKL performs host operations independently of the application workload. For oblivious disk I/O, SGX-LKL uses an encrypted ext4 file system with shuffled disk blocks. We show that SGX-LKL protects TensorFlow training with a 21% overhead.

When servers manage resources on behalf of multiple, mutuallydistrusting clients, they must mediate access to those resources to ensure that each client request complies with an authorization policy. This goal is typically achieved by placing authorization hooks at appropriate locations in server code. The goal of authorization hook placement is to completely mediate all security-sensitive operations on shared resources.To date, authorization hook placement in code bases, such as the X server and postgresql, has largely been a manual procedure, driven by informal analysis of server code and discussions on developer forums. Often, there is a lack of consensus about basic concepts, such as what constitutes a security-sensitive operation.In this paper, we propose an automated hook placement approach that is motivated by a novel observation -that the deliberate choices made by clients for objects from server collections and for processing those objects must all be authorized. We have built a tool that uses this observation to statically analyze the server source. Using real-world examples (the X server and postgresql), we show that the hooks placed by our method are just as effective as hooks that were manually placed over the course of years while greatly reducing the burden on programmers.

Abstract. We present a framework that automatically produces suggestions to resolve type errors in security-typed programs, enabling legacy code to be retrofit with comprehensive security policy mediation. Resolving such type errors requires selecting a placement of mediation statements that implement runtime security decisions, such as declassifiers and authorization checks. Manually placing mediation statements in legacy code can be difficult, as there may be several, interacting type errors. In this paper, we solve this problem by constructing a graph that has the property that a vertex cut is equivalent to the points at which mediation statements can be inserted to allow the program to satisfy the type system. We build a framework that produces suggestions that are minimum cuts of this graph, and the framework can be customized to find suggestions that satisfy programmer requirements. Our framework implementation for Java programs computes suggestions for 20,000 line programs in less than 100 seconds, reduces the number of locations a programmer must consider by 90%, and selects suggestions similar to those proposed by expert programmers 80% of the time.

Users of online services such as messaging, code hosting and collaborative document editing expect the services to uphold the integrity of their data. Despite providers' best efforts, data corruption still occurs, but at present service integrity violations are excluded from SLAs. For providers to include such violations as part of SLAs, the competing requirements of clients and providers must be satisfied. Clients need the ability to independently identify and prove service integrity violations to claim compensation. At the same time, providers must be able to refute spurious claims.We describe LibSEAL, a SEcure Audit Library for Internet services that creates a non-repudiable audit log of service operations and checks invariants to discover violations of service integrity. LibSEAL is a drop-in replacement for TLS libraries used by services, and thus observes and logs all service requests and responses. It runs inside a trusted execution environment, such as Intel SGX, to protect the integrity of the audit log. Logs are stored using an embedded relational database, permitting service invariant violations to be discovered using simple SQL queries. We evaluate LibSEAL with three popular online services (Git, ownCloud and Dropbox) and demonstrate that it is effective in discovering integrity violations, while reducing throughput by at most 14%.