A Framework for Heavyweight Dynamic Binary Instrumentation
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Arxiv:2006.14147V2 [Cs.CR] 26 Mar 2021
FastSpec: Scalable Generation and Detection of Spectre Gadgets Using Neural Embeddings M. Caner Tol Berk Gulmezoglu Koray Yurtseven Berk Sunar Worcester Polytechnic Institute Iowa State University Worcester Polytechnic Institute Worcester Polytechnic Institute [email protected] [email protected] [email protected] [email protected] Abstract—Several techniques have been proposed to detect [4] are implemented against the SGX environment and vulnerable Spectre gadgets in widely deployed commercial even remotely over the network [5]. These attacks show software. Unfortunately, detection techniques proposed so the applicability of Spectre attacks in the wild. far rely on hand-written rules which fall short in covering Unfortunately, chip vendors try to patch the leakages subtle variations of known Spectre gadgets as well as demand one-by-one with microcode updates rather than fixing a huge amount of time to analyze each conditional branch the flaws by changing their hardware designs. Therefore, in software. Moreover, detection tool evaluations are based developers rely on automated malware analysis tools to only on a handful of these gadgets, as it requires arduous eliminate mistakenly placed Spectre gadgets in their pro- effort to craft new gadgets manually. grams. The proposed detection tools mostly implement In this work, we employ both fuzzing and deep learning taint analysis [6] and symbolic execution [7], [8] to iden- techniques to automate the generation and detection of tify potential gadgets in benign applications. However, Spectre gadgets. We first create a diverse set of Spectre-V1 the methods proposed so far are associated with two gadgets by introducing perturbations to the known gadgets. shortcomings: (1) the low number of Spectre gadgets Using mutational fuzzing, we produce a data set with more prevents the comprehensive evaluation of the tools, (2) than 1 million Spectre-V1 gadgets which is the largest time consumption exponentially increases when the binary Spectre gadget data set built to date. -
Dynamic Optimization of IA-32 Applications Under Dynamorio by Timothy Garnett
Dynamic Optimization of IA-32 Applications Under DynamoRIO by Timothy Garnett Submitted to the Department of Electrical Engineering and Computer Science in Partial Fulfillment of the Requirements for the Degree of Master of Engineering in Computer Science Abstract The ability of compilers to optimize programs statically is diminishing. The advent and increased use of shared libraries, dynamic class loading, and runtime binding means that the compiler has, even with difficult to accurately obtain profiling data, less and less knowledge of the runtime state of the program. Dynamic optimization systems attempt to fill this gap by providing the ability to optimize the application at runtime when more of the system's state is known and very accurate profiling data can be obtained. This thesis presents two uses of the DynamoRIO runtime introspection and modification system to optimize applications at runtime. The first is a series of optimizations for interpreters running under DynamoRIO's logical program counter extension. The optimizations include extensions to DynamoRIO to allow the interpreter writer add a few annotations to the source code of his interpreter that enable large amounts of optimization. For interpreters instrumented as such, improvements in performance of 20 to 60 percent were obtained. The second use is a proof of concept for accelerating the performance of IA-32 applications running on the Itanium processor by dynamically translating hot trace paths into Itanium IA-64 assembly through DynamoRIO while allowing the less frequently executed potions of the application to run in Itanium's native IA-32 hardware emulation. This transformation yields speedups of almost 50 percent on small loop intensive benchmarks. -
Automatic Benchmark Profiling Through Advanced Trace Analysis Alexis Martin, Vania Marangozova-Martin
Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin, Vania Marangozova-Martin To cite this version: Alexis Martin, Vania Marangozova-Martin. Automatic Benchmark Profiling through Advanced Trace Analysis. [Research Report] RR-8889, Inria - Research Centre Grenoble – Rhône-Alpes; Université Grenoble Alpes; CNRS. 2016. hal-01292618 HAL Id: hal-01292618 https://hal.inria.fr/hal-01292618 Submitted on 24 Mar 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin , Vania Marangozova-Martin RESEARCH REPORT N° 8889 March 23, 2016 Project-Team Polaris ISSN 0249-6399 ISRN INRIA/RR--8889--FR+ENG Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin ∗ † ‡, Vania Marangozova-Martin ∗ † ‡ Project-Team Polaris Research Report n° 8889 — March 23, 2016 — 15 pages Abstract: Benchmarking has proven to be crucial for the investigation of the behavior and performances of a system. However, the choice of relevant benchmarks still remains a challenge. To help the process of comparing and choosing among benchmarks, we propose a solution for automatic benchmark profiling. It computes unified benchmark profiles reflecting benchmarks’ duration, function repartition, stability, CPU efficiency, parallelization and memory usage. -
Linux Kernel and Driver Development Training Slides
Linux Kernel and Driver Development Training Linux Kernel and Driver Development Training © Copyright 2004-2021, Bootlin. Creative Commons BY-SA 3.0 license. Latest update: October 9, 2021. Document updates and sources: https://bootlin.com/doc/training/linux-kernel Corrections, suggestions, contributions and translations are welcome! embedded Linux and kernel engineering Send them to [email protected] - Kernel, drivers and embedded Linux - Development, consulting, training and support - https://bootlin.com 1/470 Rights to copy © Copyright 2004-2021, Bootlin License: Creative Commons Attribution - Share Alike 3.0 https://creativecommons.org/licenses/by-sa/3.0/legalcode You are free: I to copy, distribute, display, and perform the work I to make derivative works I to make commercial use of the work Under the following conditions: I Attribution. You must give the original author credit. I Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under a license identical to this one. I For any reuse or distribution, you must make clear to others the license terms of this work. I Any of these conditions can be waived if you get permission from the copyright holder. Your fair use and other rights are in no way affected by the above. Document sources: https://github.com/bootlin/training-materials/ - Kernel, drivers and embedded Linux - Development, consulting, training and support - https://bootlin.com 2/470 Hyperlinks in the document There are many hyperlinks in the document I Regular hyperlinks: https://kernel.org/ I Kernel documentation links: dev-tools/kasan I Links to kernel source files and directories: drivers/input/ include/linux/fb.h I Links to the declarations, definitions and instances of kernel symbols (functions, types, data, structures): platform_get_irq() GFP_KERNEL struct file_operations - Kernel, drivers and embedded Linux - Development, consulting, training and support - https://bootlin.com 3/470 Company at a glance I Engineering company created in 2004, named ”Free Electrons” until Feb. -
Dynamic Binary Translation & Instrumentation
Dynamic Binary Translation & Instrumentation Pin Building Customized Program Analysis Tools with Dynamic Instrumentation CK Luk, Robert Cohn, Robert Muth, Harish Patil, Artur Klauser, Geoff Lowney, Steven Wallace, Kim Hazelwood Intel Vijay Janapa Reddi University of Colorado http://rogue.colorado.edu/Pin PLDI’05 2 Instrumentation • Insert extra code into programs to collect information about execution • Program analysis: • Code coverage, call-graph generation, memory-leak detection • Architectural study: • Processor simulation, fault injection • Existing binary-level instrumentation systems: • Static: • ATOM, EEL, Etch, Morph • Dynamic: • Dyninst, Vulcan, DTrace, Valgrind, Strata, DynamoRIO C Pin is a new dynamic binary instrumentation system PLDI’05 3 A Pintool for Tracing Memory Writes #include <iostream> #include "pin.H" executed immediately before a FILE* trace; write is executed • Same source code works on the 4 architectures VOID RecordMemWrite(VOID* ip, VOID* addr, UINT32 size) { fprintf(trace,=> “%p: Pin Wtakes %p %dcare\n”, of ip, different addr, size); addressing modes } • No need to manually save/restore application state VOID Instruction(INS ins, VOID *v) { if (INS_IsMemoryWrite(ins))=> Pin does it for you automatically and efficiently INS_InsertCall(ins, IPOINT_BEFORE, AFUNPTR(RecordMemWrite), IARG_INST_PTR, IARG_MEMORYWRITE_EA, IARG_MEMORYWRITE_SIZE, IARG_END); } int main(int argc, char * argv[]) { executed when an instruction is PIN_Init(argc, argv); dynamically compiled trace = fopen(“atrace.out”, “w”); INS_AddInstrumentFunction(Instruction, -
Automatic Benchmark Profiling Through Advanced Trace Analysis
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Hal - Université Grenoble Alpes Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin, Vania Marangozova-Martin To cite this version: Alexis Martin, Vania Marangozova-Martin. Automatic Benchmark Profiling through Advanced Trace Analysis. [Research Report] RR-8889, Inria - Research Centre Grenoble { Rh^one-Alpes; Universit´eGrenoble Alpes; CNRS. 2016. <hal-01292618> HAL Id: hal-01292618 https://hal.inria.fr/hal-01292618 Submitted on 24 Mar 2016 HAL is a multi-disciplinary open access L'archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destin´eeau d´ep^otet `ala diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publi´esou non, lished or not. The documents may come from ´emanant des ´etablissements d'enseignement et de teaching and research institutions in France or recherche fran¸caisou ´etrangers,des laboratoires abroad, or from public or private research centers. publics ou priv´es. Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin , Vania Marangozova-Martin RESEARCH REPORT N° 8889 March 23, 2016 Project-Team Polaris ISSN 0249-6399 ISRN INRIA/RR--8889--FR+ENG Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin ∗ † ‡, Vania Marangozova-Martin ∗ † ‡ Project-Team Polaris Research Report n° 8889 — March 23, 2016 — 15 pages Abstract: Benchmarking has proven to be crucial for the investigation of the behavior and performances of a system. However, the choice of relevant benchmarks still remains a challenge. To help the process of comparing and choosing among benchmarks, we propose a solution for automatic benchmark profiling. -
Generic User-Level PCI Drivers
Generic User-Level PCI Drivers Hannes Weisbach, Bj¨orn D¨obel, Adam Lackorzynski Technische Universit¨at Dresden Department of Computer Science, 01062 Dresden {weisbach,doebel,adam}@tudos.org Abstract Linux has become a popular foundation for systems with real-time requirements such as industrial control applications. In order to run such workloads on Linux, the kernel needs to provide certain properties, such as low interrupt latencies. For this purpose, the kernel has been thoroughly examined, tuned, and verified. This examination includes all aspects of the kernel, including the device drivers necessary to run the system. However, hardware may change and therefore require device driver updates or replacements. Such an update might require reevaluation of the whole kernel because of the tight integration of device drivers into the system and the manyfold ways of potential interactions. This approach is time-consuming and might require revalidation by a third party. To mitigate these costs, we propose to run device drivers in user-space applications. This allows to rely on the unmodified and already analyzed latency characteristics of the kernel when updating drivers, so that only the drivers themselves remain in the need of evaluation. In this paper, we present the Device Driver Environment (DDE), which uses the UIO framework supplemented by some modifications, which allow running any recent PCI driver from the Linux kernel without modifications in user space. We report on our implementation, discuss problems related to DMA from user space and evaluate the achieved performance. 1 Introduction in safety-critical systems would need additional au- dits and certifications to take place. -
Building Embedded Linux Systems ,Roadmap.18084 Page Ii Wednesday, August 6, 2008 9:05 AM
Building Embedded Linux Systems ,roadmap.18084 Page ii Wednesday, August 6, 2008 9:05 AM Other Linux resources from O’Reilly Related titles Designing Embedded Programming Embedded Hardware Systems Linux Device Drivers Running Linux Linux in a Nutshell Understanding the Linux Linux Network Adminis- Kernel trator’s Guide Linux Books linux.oreilly.com is a complete catalog of O’Reilly’s books on Resource Center Linux and Unix and related technologies, including sample chapters and code examples. ONLamp.com is the premier site for the open source web plat- form: Linux, Apache, MySQL, and either Perl, Python, or PHP. Conferences O’Reilly brings diverse innovators together to nurture the ideas that spark revolutionary industries. We specialize in document- ing the latest tools and systems, translating the innovator’s knowledge into useful skills for those in the trenches. Visit con- ferences.oreilly.com for our upcoming events. Safari Bookshelf (safari.oreilly.com) is the premier online refer- ence library for programmers and IT professionals. Conduct searches across more than 1,000 books. Subscribers can zero in on answers to time-critical questions in a matter of seconds. Read the books on your Bookshelf from cover to cover or sim- ply flip to the page you need. Try it today for free. main.title Page iii Monday, May 19, 2008 11:21 AM SECOND EDITION Building Embedded Linux SystemsTomcat ™ The Definitive Guide Karim Yaghmour, JonJason Masters, Brittain Gilad and Ben-Yossef, Ian F. Darwin and Philippe Gerum Beijing • Cambridge • Farnham • Köln • Sebastopol • Taipei • Tokyo Building Embedded Linux Systems, Second Edition by Karim Yaghmour, Jon Masters, Gilad Ben-Yossef, and Philippe Gerum Copyright © 2008 Karim Yaghmour and Jon Masters. -
Pin: Building Customized Program Analysis Tools with Dynamic Instrumentation
Pin: Building Customized Program Analysis Tools with Dynamic Instrumentation Chi-Keung Luk Robert Cohn Robert Muth Harish Patil Artur Klauser Geoff Lowney Steven Wallace Vijay Janapa Reddi Kim Hazelwood Intel Corporation ¡ University of Colorado ¢¤£¦¥¨§ © £ ¦£ "! #%$&'( £)&(¦*+©-,.+/01$©-!2 ©-,2¦3 45£) 67©2, £¦!2 "0 Abstract 1. Introduction Robust and powerful software instrumentation tools are essential As software complexity increases, instrumentation—a technique for program analysis tasks such as profiling, performance evalu- for inserting extra code into an application to observe its behavior— ation, and bug detection. To meet this need, we have developed is becoming more important. Instrumentation can be performed at a new instrumentation system called Pin. Our goals are to pro- various stages: in the source code, at compile time, post link time, vide easy-to-use, portable, transparent, and efficient instrumenta- or at run time. Pin is a software system that performs run-time tion. Instrumentation tools (called Pintools) are written in C/C++ binary instrumentation of Linux applications. using Pin’s rich API. Pin follows the model of ATOM, allowing the The goal of Pin is to provide an instrumentation platform for tool writer to analyze an application at the instruction level with- building a wide variety of program analysis tools for multiple archi- out the need for detailed knowledge of the underlying instruction tectures. As a result, the design emphasizes ease-of-use, portabil- set. The API is designed to be architecture independent whenever ity, transparency, efficiency, and robustness. This paper describes possible, making Pintools source compatible across different archi- the design of Pin and shows how it provides these features. -
Pin Tutorial What Is Instrumentation?
Pin Tutorial What is Instrumentation? A technique that inserts extra code into a program to collect runtime information Instrumentation approaches: • Source instrumentation: – Instrument source programs • Binary instrumentation: – Instrument executables directly 1 Pin Tutorial 2007 Why use Dynamic Instrumentation? No need to recompile or relink Discover code at runtime Handle dynamically-generated code Attach to running processes 2 Pin Tutorial 2007 Advantages of Pin Instrumentation Easy-to-use Instrumentation: • Uses dynamic instrumentation – Do not need source code, recompilation, post-linking Programmable Instrumentation: • Provides rich APIs to write in C/C++ your own instrumentation tools (called Pintools) Multiplatform: • Supports x86, x86-64, Itanium, Xscale • Supports Linux, Windows, MacOS Robust: • Instruments real-life applications: Database, web browsers, … • Instruments multithreaded applications • Supports signals Efficient: • Applies compiler optimizations on instrumentation code 3 Pin Tutorial 2007 Using Pin Launch and instrument an application $ pin –t pintool –- application Instrumentation engine Instrumentation tool (provided in the kit) (write your own, or use one provided in the kit) Attach to and instrument an application $ pin –t pintool –pid 1234 4 Pin Tutorial 2007 Pin Instrumentation APIs Basic APIs are architecture independent: • Provide common functionalities like determining: – Control-flow changes – Memory accesses Architecture-specific APIs • e.g., Info about segmentation registers on IA32 Call-based APIs: -
In-Memory Protocol Fuzz Testing Using the Pin Toolkit
In-Memory Protocol Fuzz Testing using the Pin Toolkit BACHELOR’S THESIS submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Engineering by Patrik Fimml Registration Number 1027027 to the Faculty of Informatics at the Vienna University of Technology Advisor: Dipl.-Ing. Markus Kammerstetter Vienna, 5th March, 2015 Patrik Fimml Markus Kammerstetter Technische Universität Wien A-1040 Wien Karlsplatz 13 Tel. +43-1-58801-0 www.tuwien.ac.at Erklärung zur Verfassung der Arbeit Patrik Fimml Kaiserstraße 92 1070 Wien Hiermit erkläre ich, dass ich diese Arbeit selbständig verfasst habe, dass ich die verwen- deten Quellen und Hilfsmittel vollständig angegeben habe und dass ich die Stellen der Arbeit – einschließlich Tabellen, Karten und Abbildungen –, die anderen Werken oder dem Internet im Wortlaut oder dem Sinn nach entnommen sind, auf jeden Fall unter Angabe der Quelle als Entlehnung kenntlich gemacht habe. Wien, 5. März 2015 Patrik Fimml iii Abstract In this Bachelor’s thesis, we explore in-memory fuzz testing of TCP server binaries. We use the Pin instrumentation framework to inject faults directly into application buffers and take snapshots of the program state. On a simple testing binary, this results in a 2.7× speedup compared to a naive fuzz testing implementation. To evaluate our implementation, we apply it to a VNC server binary as an example for a real-world application. We describe obstacles that we had to tackle during development and point out limitations of our approach. v Contents Abstract v Contents vii 1 Introduction 1 2 Fuzz Testing 3 2.1 Naive Protocol Fuzz Testing . -
Introduction to PIN 6.823: Computer System Architecture
6.823: Computer System Architecture Introduction to PIN Victor Ying [email protected] Adapted from: Prior 6.823 offerings, and Intel’s Tutorial at CGO 2010 2/7/2020 6.823 Spring 2020 1 Designing Computer Architectures Build computers that run programs efficiently Two important components: 1. Study of programs and patterns – Guides interface, design choices 2. Engineering under constraints – Evaluate tradeoffs of architectural choices 2/7/2020 6.823 Spring 2020 2 Simulation: An Essential Tool in Architecture Research • A tool to reproduce the behavior of a computing device • Why use simulators? – Obtain fine-grained details about internal behavior – Enable software development – Obtain performance predictions for candidate architectures – Cheaper than building system 2/7/2020 6.823 Spring 2020 3 Labs • Focus on understanding program behavior, evaluating architectural tradeoffs Model Results Suite Benchmark 2/7/2020 6.823 Spring 2020 4 PIN • www.pintool.org – Developed by Intel – Free for download and use • A tool for dynamic binary instrumentation Runtime No need to Insert code in the re-compile program to collect or re-link information 2/7/2020 6.823 Spring 2020 5 Pin: A Versatile Tool • Architecture research – Simulators: zsim, CMPsim, Graphite, Sniper, Swarm • Software Development – Intel Parallel Studio, Intel SDE – Memory debugging, correctness/perf. analysis • Security Useful tool to have in – Taint analysis your arsenal! 2/7/2020 6.823 Spring 2020 6 PIN • www.pintool.org – Developed by Intel – Free for download and use • A tool for dynamic binary instrumentation Runtime No need to Insert code in the re-compile program to collect or re-link information 2/7/2020 6.823 Spring 2020 7 Instrumenting Instructions sub $0xff, %edx cmp %esi, %edx jle <L1> mov $0x1, %edi add $0x10, %eax 2/7/2020 6.823 Spring 2020 8 Example 1: Instruction Count I want to count the number of instructions executed.