ARM TrustZone technology has been widely used to create Trusted Execution Environments (TEEs) for enhancing the security of applications. However, the increasing number of installed security-sensitive applications in the secure world will inevitably enlarge the trusted computing base (TCB) of TEE systems. To minimize the TCB of the secure world and increase application portability, Isolated Execution Environments (IEEs) are proposed to protect applications in enclaves created in the normal world. However, existing IEE systems cannot provide the same level of security as the TEE systems, particularly, on resolving the multi-vector attacks that include both physical memory disclosure attacks and software attacks. In this article, we develop a new cache-assisted IEE system called CacheIEE that creates enclaves in the L1 data cache of the normal world to protect sensitive data against multi-vector attacks. First, by always storing the sensitive data in the L1 data cache, CacheIEE can effectively prevent physical memory disclosure attacks. Second, we protect the L1 data cache against untrusted rich OS running in other cores. To support more applications, CacheIEE can process large-size sensitive data in the L1 data cache with constrained capacity. We implement a system prototype of CacheIEE and verify its security and practicability.

Mobile devices are becoming an indispensable part of work for corporations and governments to store and process sensitive information. Thus, it is important for remote administrators to maintain control of these devices via Mobile Device Management (MDM) solutions. ARM TrustZone has been widely regarded as the de facto solution for protecting the security-sensitive software, such as MDM agents, from attacks of a compromised rich OS. However, little attention has been given to protecting the MDM control channel, a fundamental component for a remote administrator to invoke the TrustZone-based MDM agents and perform specific management operations. In this work, we design an ARM TrustZone-based network mechanism, called TZNIC, towards enabling resilient and secure access to TrustZone-based software, even in the presence of a malicious rich OS. TZNIC deploys two NIC drivers, one secure-world driver and one normal-world driver, multiplexing one physical NIC. We utilize the ARM TrustZone-based high privilege to protect the secure-world driver and further resolve several challenges on sharing one set of hardware peripherals between two isolated software environments. TZNIC does not require any changes or collaboration of the rich OS. We implement a prototype of TZNIC, and the evaluation results show that TZNIC can provide a reliable network channel to invoke the security software in the secure world, with minimal system overhead on the rich OS.

In the past decade, Trusted Execution Environment (TEE) provided by ARM TrustZone is becoming one of the primary techniques for enhancing the security of mobile devices. The isolation enforced by TrustZone can protect the trusted applications running in the TEE against malicious software in the untrusted rich execution environment (REE). However, TrustZone cannot completely prevent vulnerabilities in trusted applications residing in the TEE, which can then be used to attack other trusted applications or even the trusted OS. Previously, a number of memory corruption vulnerabilities have been reported on different TAs, which are written in memory-unsafe languages like C.

The traditional usage of ARM TrustZone has difficulty on solving the conflicts between the manufacturers that want to minimize the trusted computing base by constraining the installation of third-party applications in the secure world and the third-party application developers who prefer to have the freedom of installing their applications into the secure world. To address this issue, researchers propose to create Isolated Execution Environments (called IEEs) in the normal world to protect the security-sensitive applications. In this paper, we perform a systematic study on the IEE data protection models and the ARM cache attributes, and discover three cache-based attacks called CITM that can be leveraged to manipulate the sensitive data protected in IEEs. Specifically, due to the inefficient and incoherent security measures on the cache that maps to the IEE memory (i.e., memory designated for IEEs), attackers in the normal world may compromise the security of IEE data by manipulating the IEE memory during concurrent execution, bypassing the security measures enforced when a security-sensitive application is suspended or finished, or misusing the incomplete security measures during IEE's context switching processes. We conduct case studies of CITM attacks on three well-known IEE systems including SANCTUARY, Ginseng, and TrustICE to illustrate the feasibility to exploit them on real hardware testbeds. Finally, we analyze the root causes of the CITM attacks and propose a countermeasure to defeat them. The experimental results show that our defense scheme has a small overhead.

On ARM processors with TrustZone security extension, asynchronous introspection mechanisms have been developed in the secure world to detect security policy violations in the normal world. These mechanisms provide security protection via passively checking the normal world snapshot. However, since previous secure world checking solutions require to suspend the entire rich OS, asynchronous introspection has not been widely adopted in the real world. Given a multi-core ARM system that can execute the two worlds simultaneously on different cores, secure world introspection can check the rich OS without suspension. However, we identify a new normal-world evasion attack that can defeat the asynchronous introspection by removing the attacking traces in parallel from one core when the security checking is performing on another core. We perform a systematic study on this attack and present its efficiency against existing asynchronous introspection mechanisms. As the countermeasure, we propose a secure and trustworthy asynchronous introspection mechanism called SATIN, which can efficiently detect the evasion attacks by increasing the attackers' evasion time cost and decreasing the defender's execution time under a safe limit. We implement a prototype on an ARM development board and the experimental results show that SATIN can effectively prevent evasion attacks on multi-core systems with a minor system overhead.

Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator. Moreover, armoured crawlers are evolving against new anti-crawler mechanisms in the arm races between crawler developers and crawler defenders. In this paper, based on one observation that normal users and malicious crawlers have different short-term and long-term download behaviours, we develop a new anti-crawler mechanism called PathMarker to detect and constrain persistent distributed crawlers. By adding a marker to each Uniform Resource Locator (URL), we can trace the page that leads to the access of this URL and the user identity who accesses this URL. With this supporting information, we can not only perform more accurate heuristic detection using the path related features, but also develop a Support Vector Machine based machine learning detection model to distinguish malicious crawlers from normal users via inspecting their different patterns of URL visiting paths and URL visiting timings. In addition to effectively detecting crawlers at the earliest stage, PathMarker can dramatically suppress the scraping efficiency of crawlers before they are detected. We deploy our approach on an online forum website, and the evaluation results show that PathMarker can quickly capture all 6 open-source and in-house crawlers, plus two external crawlers (i.e., Googlebots and Yahoo Slurp).

Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator. In this paper, based on one observation that normal users and malicious crawlers have different short-term and long-term download behaviors, we develop a new anti-crawler mechanism called PathMarker to detect and constrain persistent distributed crawlers. For each URL, by adding a marker to record its parent page that leads to the access to this URL and the user identity who accesses this URL, we can not only perform more accurate heuristic detection and Support Vector Machine (SVM) based machine learning detection to detect malicious crawlers at an earlier stage, but also dramatically suppress the efficiency of crawlers before they are detected. We deploy our approach on a forum website, and the evaluation results show that PathMarker can quickly capture all 6 open-source and in-house crawlers.