Practical Enclave Malware with Intel SGX

Modern CPU architectures offer strong isolation guarantees towards user applications in the form of enclaves. For instance, Intel's threat model for SGX assumes fully trusted enclaves, yet there is an ongoing debate on whether this threat model is realistic. In particular, it is unclear to what extent enclave malware could harm a system. In this work, we practically demonstrate the first enclave malware which fully and stealthily impersonates its host application. Together with poorly-deployed application isolation on personal computers, such malware can not only steal or encrypt documents for extortion, but also act on the user's behalf, e.g., sending phishing emails or mounting denial-of-service attacks. Our SGX-ROP attack uses new TSX-based memory-disclosure primitive and a write-anything-anywhere primitive to construct a code-reuse attack from within an enclave which is then inadvertently executed by the host application. With SGX-ROP, we bypass ASLR, stack canaries, and address sanitizer. We demonstrate that instead of protecting users from harm, SGX currently poses a security threat, facilitating so-called super-malware with ready-to-hit exploits. With our results, we seek to demystify the enclave malware threat and lay solid ground for future research on and defense against enclave malware

HardScope: Thwarting DOP with Hardware-assisted Run-time Scope Enforcement

Widespread use of memory unsafe programming languages (e.g., C and C++) leaves many systems vulnerable to memory corruption attacks. A variety of defenses have been proposed to mitigate attacks that exploit memory errors to hijack the control flow of the code at run-time, e.g., (fine-grained) randomization or Control Flow Integrity. However, recent work on data-oriented programming (DOP) demonstrated highly expressive (Turing-complete) attacks, even in the presence of these state-of-the-art defenses. Although multiple real-world DOP attacks have been demonstrated, no efficient defenses are yet available. We propose run-time scope enforcement (RSE), a novel approach designed to efficiently mitigate all currently known DOP attacks by enforcing compile-time memory safety constraints (e.g., variable visibility rules) at run-time. We present HardScope, a proof-of-concept implementation of hardware-assisted RSE for the new RISC-V open instruction set architecture. We discuss our systematic empirical evaluation of HardScope which demonstrates that it can mitigate all currently known DOP attacks, and has a real-world performance overhead of 3.2% in embedded benchmarks

CFI CaRE: Hardware-supported Call and Return Enforcement for Commercial Microcontrollers

With the increasing scale of deployment of Internet of Things (IoT), concerns about IoT security have become more urgent. In particular, memory corruption attacks play a predominant role as they allow remote compromise of IoT devices. Control-flow integrity (CFI) is a promising and generic defense technique against these attacks. However, given the nature of IoT deployments, existing protection mechanisms for traditional computing environments (including CFI) need to be adapted to the IoT setting. In this paper, we describe the challenges of enabling CFI on microcontroller (MCU) based IoT devices. We then present CaRE, the first interrupt-aware CFI scheme for low-end MCUs. CaRE uses a novel way of protecting the CFI metadata by leveraging TrustZone-M security extensions introduced in the ARMv8-M architecture. Its binary instrumentation approach preserves the memory layout of the target MCU software, allowing pre-built bare-metal binary code to be protected by CaRE. We describe our implementation on a Cortex-M Prototyping System and demonstrate that CaRE is secure while imposing acceptable performance and memory impact.Comment: Author's version of paper to appear in the 20th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2017

Technical Report: A Toolkit for Runtime Detection of Userspace Implants

This paper presents the Userspace Integrity Measurement Toolkit (USIM Toolkit), a set of integrity measurement collection tools capable of detecting advanced malware threats, such as memory-only implants, that evade many traditional detection tools. Userspace integrity measurement validates that a platform is free from subversion by validating that the current state of the platform is consistent with a set of invariants. The invariants enforced by the USIM Toolkit are carefully chosen based on the expected behavior of userspace, and key behaviors of advanced malware. Userspace integrity measurement may be combined with existing filesystem and kernel integrity measurement approaches to provide stronger guarantees that a platform is executing the expected software and that the software is in an expected state

A Survey of Techniques for Improving Security of GPUs

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest `link' in the security `chain'. In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key attributes to highlight their similarities and differences. More than informing users and researchers about GPU security techniques, this survey aims to increase their awareness about GPU security vulnerabilities and potential countermeasures

A Dynamic-Adversarial Mining Approach to the Security of Machine Learning

Operating in a dynamic real world environment requires a forward thinking and adversarial aware design for classifiers, beyond fitting the model to the training data. In such scenarios, it is necessary to make classifiers - a) harder to evade, b) easier to detect changes in the data distribution over time, and c) be able to retrain and recover from model degradation. While most works in the security of machine learning has concentrated on the evasion resistance (a) problem, there is little work in the areas of reacting to attacks (b and c). Additionally, while streaming data research concentrates on the ability to react to changes to the data distribution, they often take an adversarial agnostic view of the security problem. This makes them vulnerable to adversarial activity, which is aimed towards evading the concept drift detection mechanism itself. In this paper, we analyze the security of machine learning, from a dynamic and adversarial aware perspective. The existing techniques of Restrictive one class classifier models, Complex learning models and Randomization based ensembles, are shown to be myopic as they approach security as a static task. These methodologies are ill suited for a dynamic environment, as they leak excessive information to an adversary, who can subsequently launch attacks which are indistinguishable from the benign data. Based on empirical vulnerability analysis against a sophisticated adversary, a novel feature importance hiding approach for classifier design, is proposed. The proposed design ensures that future attacks on classifiers can be detected and recovered from. The proposed work presents motivation, by serving as a blueprint, for future work in the area of Dynamic-Adversarial mining, which combines lessons learned from Streaming data mining, Adversarial learning and Cybersecurity.Comment: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 201

Discovering New Vulnerabilities in Computer Systems

Vulnerability research plays a key role in preventing and defending against malicious computer system exploitations. Driven by a multi-billion dollar underground economy, cyber criminals today tirelessly launch malicious exploitations, threatening every aspect of daily computing. to effectively protect computer systems from devastation, it is imperative to discover and mitigate vulnerabilities before they fall into the offensive parties\u27 hands. This dissertation is dedicated to the research and discovery of new design and deployment vulnerabilities in three very different types of computer systems.;The first vulnerability is found in the automatic malicious binary (malware) detection system. Binary analysis, a central piece of technology for malware detection, are divided into two classes, static analysis and dynamic analysis. State-of-the-art detection systems employ both classes of analyses to complement each other\u27s strengths and weaknesses for improved detection results. However, we found that the commonly seen design patterns may suffer from evasion attacks. We demonstrate attacks on the vulnerabilities by designing and implementing a novel binary obfuscation technique.;The second vulnerability is located in the design of server system power management. Technological advancements have improved server system power efficiency and facilitated energy proportional computing. However, the change of power profile makes the power consumption subjected to unaudited influences of remote parties, leaving the server systems vulnerable to energy-targeted malicious exploit. We demonstrate an energy abusing attack on a standalone open Web server, measure the extent of the damage, and present a preliminary defense strategy.;The third vulnerability is discovered in the application of server virtualization technologies. Server virtualization greatly benefits today\u27s data centers and brings pervasive cloud computing a step closer to the general public. However, the practice of physical co-hosting virtual machines with different security privileges risks introducing covert channels that seriously threaten the information security in the cloud. We study the construction of high-bandwidth covert channels via the memory sub-system, and show a practical exploit of cross-virtual-machine covert channels on virtualized x86 platforms

On a Generic Security Game Model

To protect the systems exposed to the Internet against attacks, a security system with the capability to engage with the attacker is needed. There have been attempts to model the engagement/interactions between users, both benign and malicious, and network administrators as games. Building on such works, we present a game model which is generic enough to capture various modes of such interactions. The model facilitates stochastic games with imperfect information. The information is imperfect due to erroneous sensors leading to incorrect perception of the current state by the players. To model this error in perception distributed over other multiple states, we use Euclidean distances between the outputs of the sensors. We build a 5-state game to represent the interaction of the administrator with the user. The states correspond to 1) the user being out of the system in the Internet, and after logging in to the system; 2) having low privileges; 3) having high privileges; 4) when he successfully attacks and 5) gets trapped in a honeypot by the administrator. Each state has its own action set. We present the game with a distinct perceived action set corresponding to each distinct information set of these states. The model facilitates stochastic games with imperfect information. The imperfect information is due to erroneous sensors leading to incorrect perception of the current state by the players. To model this error in perception distributed over the states, we use Euclidean distances between outputs of the sensors. A numerical simulation of an example game is presented to show the evaluation of rewards to the players and the preferred strategies. We also present the conditions for formulating the strategies when dealing with more than one attacker and making collaborations.Comment: 31 page

Detile: Fine-Grained Information Leak Detection in Script Engines

Memory disclosure attacks play an important role in the exploitation of memory corruption vulnerabilities. By analyzing recent research, we observe that bypasses of defensive solutions that enforce control-flow integrity or attempt to detect return-oriented programming require memory disclosure attacks as a fundamental first step. However, research lags behind in detecting such information leaks. In this paper, we tackle this problem and present a system for fine-grained, automated detection of memory disclosure attacks against scripting engines. The basic insight is as follows: scripting languages, such as JavaScript in web browsers, are strictly sandboxed. They must not provide any insights about the memory layout in their contexts. In fact, any such information potentially represents an ongoing memory disclosure attack. Hence, to detect information leaks, our system creates a clone of the scripting engine process with a re-randomized memory layout. The clone is instrumented to be synchronized with the original process. Any inconsistency in the script contexts of both processes appears when a memory disclosure was conducted to leak information about the memory layout. Based on this detection approach, we have designed and implemented Detile (\underline{det}ection of \underline{i}nformation \underline{le}aks), a prototype for the JavaScript engine in Microsoft's Internet Explorer 10/11 on Windows 8.0/8.1. An empirical evaluation shows that our tool can successfully detect memory disclosure attacks even against this proprietary software