Temporal epistemic logic is a well-established framework for expressing agents knowledge and how it evolves over time. Within language-based security these are central issues, for instance in the context of declassification. We propose to bring these two areas together. The paper presents a computational model and an epistemic temporal logic used to reason about knowledge acquired by observing program outputs. This approach is shown to elegantly capture standard notions of noninterference and declassification in the literature as well as information flow properties where sensitive and public data intermingle in delicate ways.
A separation kernel simulates a distributed environment using a single physical machine by executing partitions in isolation and appropriately controlling communication among them. We present a formal verification of information flow security for a simple separation kernel for ARMv7. Previous work on information flow kernel security leaves communication to be handled by model-external means, and cannot be used to draw conclusions when there is explicit interaction between partitions. We propose a different approach where communication between partitions is made explicit and the information flow is analyzed in the presence of such a channel. Limiting the kernel functionality as much as meaningfully possible, we accomplish a detailed analysis and verification of the system, proving its correctness at the level of the ARMv7 assembly. As a sanity check we show how the security condition is reduced to noninterference in the special case where no communication takes place. The verification is done in HOL4 taking the Cambridge model of ARM as basis, transferring verification tasks on the actual assembly code to an adaptation of the BAP binary analysis tool developed at CMU.
In this paper we study induction in the context of the firstorder µ-calculus with explicit approximations. We present and compare two Gentzen-style proof systems each using a different type of induction. The first is based on finite proof trees and uses a local well-founded induction rule, while the second is based on (finitely represented) ω-regular proof trees and uses a global induction discharge condition to ensure externally that all inductive reasoning is well-founded. We give effective procedures for the translation of proofs between the two systems, thus establishing their equivalence.
Caches pose a significant challenge to formal proofs of security for code executing on application processors, as the cache access pattern of security-critical services may leak secret information. This paper reveals a novel attack vector, exposing a low-noise cache storage channel that can be exploited by adapting well-known timing channel analysis techniques. The vector can also be used to attack various types of securitycritical software such as hypervisors and application security monitors. The attack vector uses virtual aliases with mismatched memory attributes and self-modifying code to misconfigure the memory system, allowing an attacker to place incoherent copies of the same physical address into the caches and observe which addresses are stored in different levels of cache. We design and implement three different attacks using the new vector on trusted services and report on the discovery of an 128-bit key from an AES encryption service running in TrustZone on Raspberry Pi 2. Moreover, we subvert the integrity properties of an ARMv7 hypervisor that was formally verified against a cache-less model. We evaluate well-known countermeasures against the new attack vector and propose a verification methodology that allows to formally prove the effectiveness of defence mechanisms on the binary code of the trusted software.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.