Glider: A GPU Library Driver for Improved System Security

Legacy device drivers implement both device resource management and isolation. This results in a large code base with a wide high-level interface making the driver vulnerable to security attacks. This is particularly problematic for increasingly popular accelerators like GPUs that have large, complex drivers. We solve this problem with library drivers, a new driver architecture. A library driver implements resource management as an untrusted library in the application process address space, and implements isolation as a kernel module that is smaller and has a narrower lower-level interface (i.e., closer to hardware) than a legacy driver. We articulate a set of device and platform hardware properties that are required to retrofit a legacy driver into a library driver. To demonstrate the feasibility and superiority of library drivers, we present Glider, a library driver implementation for two GPUs of popular brands, Radeon and Intel. Glider reduces the TCB size and attack surface by about 35% and 84% respectively for a Radeon HD 6450 GPU and by about 38% and 90% respectively for an Intel Ivy Bridge GPU. Moreover, it incurs no performance cost. Indeed, Glider outperforms a legacy driver for applications requiring intensive interactions with the device driver, such as applications using the OpenGL immediate mode API

Optoelectronic Reservoir Computing

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.Comment: Contains main paper and two Supplementary Material

KASR: A Reliable and Practical Approach to Attack Surface Reduction of Commodity OS Kernels

Commodity OS kernels have broad attack surfaces due to the large code base and the numerous features such as device drivers. For a real-world use case (e.g., an Apache Server), many kernel services are unused and only a small amount of kernel code is used. Within the used code, a certain part is invoked only at runtime while the rest are executed at startup and/or shutdown phases in the kernel's lifetime run. In this paper, we propose a reliable and practical system, named KASR, which transparently reduces attack surfaces of commodity OS kernels at runtime without requiring their source code. The KASR system, residing in a trusted hypervisor, achieves the attack surface reduction through a two-step approach: (1) reliably depriving unused code of executable permissions, and (2) transparently segmenting used code and selectively activating them. We implement a prototype of KASR on Xen-4.8.2 hypervisor and evaluate its security effectiveness on Linux kernel-4.4.0-87-generic. Our evaluation shows that KASR reduces the kernel attack surface by 64% and trims off 40% of CVE vulnerabilities. Besides, KASR successfully detects and blocks all 6 real-world kernel rootkits. We measure its performance overhead with three benchmark tools (i.e., SPECINT, httperf and bonnie++). The experimental results indicate that KASR imposes less than 1% performance overhead (compared to an unmodified Xen hypervisor) on all the benchmarks.Comment: The work has been accepted at the 21st International Symposium on Research in Attacks, Intrusions, and Defenses 201

Neuromorphic engineering needs closed-loop benchmarks

Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resiliently than our most advanced machines, at a fraction of the power consumption. It follows that the performance of neuromorphic systems should be evaluated in terms of real-time operation, power consumption, and resiliency to real-world perturbations and noise using task-relevant evaluation metrics. Yet, following in the footsteps of conventional machine learning, most neuromorphic benchmarks rely on recorded datasets that foster sensing accuracy as the primary measure for performance. Sensing accuracy is but an arbitrary proxy for the actual system's goal—taking a good decision in a timely manner. Moreover, static datasets hinder our ability to study and compare closed-loop sensing and control strategies that are central to survival for biological organisms. This article makes the case for a renewed focus on closed-loop benchmarks involving real-world tasks. Such benchmarks will be crucial in developing and progressing neuromorphic Intelligence. The shift towards dynamic real-world benchmarking tasks should usher in richer, more resilient, and robust artificially intelligent systems in the future

Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans

We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior. Law-making and legal interpretation form a computational engine that converts opaque human values into legible directives. "Law Informs Code" is the research agenda embedding legal knowledge and reasoning in AI. Similar to how parties to a legal contract cannot foresee every potential contingency of their future relationship, and legislators cannot predict all the circumstances under which their proposed bills will be applied, we cannot ex ante specify rules that provably direct good AI behavior. Legal theory and practice have developed arrays of tools to address these specification problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior through the threat of sanction), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code. We describe how data generated by legal processes (methods of law-making, statutory interpretation, contract drafting, applications of legal standards, legal reasoning, etc.) can facilitate the robust specification of inherently vague human goals. This increases human-AI alignment and the local usefulness of AI. Toward society-AI alignment, we present a framework for understanding law as the applied philosophy of multi-agent alignment. Although law is partly a reflection of historically contingent political power - and thus not a perfect aggregation of citizen preferences - if properly parsed, its distillation offers the most legitimate computational comprehension of societal values available. If law eventually informs powerful AI, engaging in the deliberative political process to improve law takes on even more meaning.Comment: Forthcoming in Northwestern Journal of Technology and Intellectual Property, Volume 2

The Anthropomorphic Hand Assessment Protocol (AHAP)

The progress in the development of anthropomorphic hands for robotic and prosthetic applications has not been followed by a parallel development of objective methods to evaluate their performance. The need for benchmarking in grasping research has been recognized by the robotics community as an important topic. In this study we present the Anthropomorphic Hand Assessment Protocol (AHAP) to address this need by providing a measure for quantifying the grasping ability of artificial hands and comparing hand designs. To this end, the AHAP uses 25 objects from the publicly available Yale-CMU-Berkeley Object and Model Set thereby enabling replicability. It is composed of 26 postures/tasks involving grasping with the eight most relevant human grasp types and two non-grasping postures. The AHAP allows to quantify the anthropomorphism and functionality of artificial hands through a numerical Grasping Ability Score (GAS). The AHAP was tested with different hands, the first version of the hand of the humanoid robot ARMAR-6 with three different configurations resulting from attachment of pads to fingertips and palm as well as the two versions of the KIT Prosthetic Hand. The benchmark was used to demonstrate the improvements of these hands in aspects like the grasping surface, the grasp force and the finger kinematics. The reliability, consistency and responsiveness of the benchmark have been statistically analyzed, indicating that the AHAP is a powerful tool for evaluating and comparing different artificial hand designs