ret2spec: Speculative Execution Using Return Stack Buffers

Speculative execution is an optimization technique that has been part of CPUs for over a decade. It predicts the outcome and target of branch instructions to avoid stalling the execution pipeline. However, until recently, the security implications of speculative code execution have not been studied. In this paper, we investigate a special type of branch predictor that is responsible for predicting return addresses. To the best of our knowledge, we are the first to study return address predictors and their consequences for the security of modern software. In our work, we show how return stack buffers (RSBs), the core unit of return address predictors, can be used to trigger misspeculations. Based on this knowledge, we propose two new attack variants using RSBs that give attackers similar capabilities as the documented Spectre attacks. We show how local attackers can gain arbitrary speculative code execution across processes, e.g., to leak passwords another user enters on a shared system. Our evaluation showed that the recent Spectre countermeasures deployed in operating systems can also cover such RSB-based cross-process attacks. Yet we then demonstrate that attackers can trigger misspeculation in JIT environments in order to leak arbitrary memory content of browser processes. Reading outside the sandboxed memory region with JIT-compiled code is still possible with 80\% accuracy on average.Comment: Updating to the cam-ready version and adding reference to the original pape

A Vulnerability in RSA Implementations due to Instruction Cache Analysis and Its Demonstration on OpenSSL

MicroArchitectural Analysis (MA) techniques, more specifically Simple Branch Prediction Analysis (SBPA) and Instruction Cache Analysis, have the potential of disclosing the entire execution ow of a software-implemented cryptosystem ([5, 2]). In this paper we will show that one can completely break RSA in the original unpatched OpenSSL version (v.0.9.8e) even if the most secure configuration is in place, including all countermeasures against side-channel and MicroArchitectural analysis (in particular, base blinding). We also discuss (known) countermeasures that prevent this attack. In a first step we apply an instruction cache attack to reveal which Montgomery operations require extra reductions. To exploit this information we model the timing behavior of the modular exponentiation algorithm by a stochastic process. Its analysis provides the optimal guessing strategy, which reveals the secret key ( mod p1) and finally the factorization of the RSA modulus n = p1p2. For the instruction cache attack we applied a spy process that was embedded in the target process (OpenSSL), which clearly facilitates the experimental part. This simplifiation yet does not nullify our results since in cache attacks empirical results from embedded spy processes and (suitably implemented) stand-alone spy processes are very close to each other [16] and, moreover, our guessing strategy is fault-tolerant. Interestingly, the second step of our attack is related to that of a particular combined power and timing attack on smart cards [23] (see also [27, 22]). Before we published our result [1] we informed the OpenSSL development team who included a patch into the stable branch of v.0.9.7e ([31, 32]) and CERT which informed software vendors ([33{35]). In particular, this countermeasure is included in the current version 0.9.8f. We have only analyzed OpenSSL, thus we currently do not know the strength of other cryptographic libraries

Securing Access to Cloud Computing for Critical Infrastructure

Cloud computing offers cost effective services on-demand which encourage critical infrastructure providers to consider migrating to the cloud. Critical infrastructures are considered as a backbone of modern societies such as power plants and water. Information in cloud computing is likely to be shared among different entities, which could have various degrees of sensitivity. This requires robust isolation and access control mechanisms. Although various access control models and policies have been developed, they cannot fulfil requirements for a cloud based access control system. The reason is that cloud computing has a diverse sets of security requirements and unique security challenges such as multi-tenant and heterogeneity of security policies, rules and domains. This thesis provides a detailed study of cloud computing security challenges and threats, which were used to identify security requirements for various critical infrastructure providers. We found that an access control system is a crucial security requirement for the surveyed critical infrastructure providers. Furthermore, the requirement analysis was used to propose a new criteria to evaluate access control systems for cloud computing. Moreover, this work presents a new cloud based access control model to meet the identified cloud access control requirements. The model does not only ensure the secure sharing of resources among potential untrusted tenants, but also has the capacity to support different access permissions for the same cloud user. Our focused in the proposed model is the lack of data isolation in lower levels (CPU caches), which could lead to bypass access control models to gain some sensitive information by using cache side-channel attacks. Therefore, the thesis investigates various real attack scenarios and the gaps in existing mitigation approaches. It presents a new Prime and Probe cache side-channel attack, which can give detailed information about addresses accessed by a virtual machine with no need for any information about cache sets accessed by the virtual machine. The design, implementation and evaluation of a proposed solution preventing cache side-channel attacks are also presented in the thesis. It is a new lightweight solution, which introduces very low overhead (less than 15,000 CPU cycles). It can be applied in any operating system and prevents cache side-channel attacks in cloud computing. The thesis also presents a new detecting cache side-channel attacks solution. It focuses on the infrastructure used to host cloud computing tenants by counting cache misses caused by a virtual machine. The detection solutions has 0% false negative and 15% false positive