A Practical System for Intermodule Code Optimization at Link-Time
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A Methodology for Assessing Javascript Software Protections
A methodology for Assessing JavaScript Software Protections Pedro Fortuna A methodology for Assessing JavaScript Software Protections Pedro Fortuna About me Pedro Fortuna Co-Founder & CTO @ JSCRAMBLER OWASP Member SECURITY, JAVASCRIPT @pedrofortuna 2 A methodology for Assessing JavaScript Software Protections Pedro Fortuna Agenda 1 4 7 What is Code Protection? Testing Resilience 2 5 Code Obfuscation Metrics Conclusions 3 6 JS Software Protections Q & A Checklist 3 What is Code Protection Part 1 A methodology for Assessing JavaScript Software Protections Pedro Fortuna Intellectual Property Protection Legal or Technical Protection? Alice Bob Software Developer Reverse Engineer Sells her software over the Internet Wants algorithms and data structures Does not need to revert back to original source code 5 A methodology for Assessing JavaScript Software Protections Pedro Fortuna Intellectual Property IP Protection Protection Legal Technical Encryption ? Trusted Computing Server-Side Execution Obfuscation 6 A methodology for Assessing JavaScript Software Protections Pedro Fortuna Code Obfuscation Obfuscation “transforms a program into a form that is more difficult for an adversary to understand or change than the original code” [1] More Difficult “requires more human time, more money, or more computing power to analyze than the original program.” [1] in Collberg, C., and Nagra, J., “Surreptitious software: obfuscation, watermarking, and tamperproofing for software protection.”, Addison- Wesley Professional, 2010. 7 A methodology for Assessing -
Attacking Client-Side JIT Compilers.Key
Attacking Client-Side JIT Compilers Samuel Groß (@5aelo) !1 A JavaScript Engine Parser JIT Compiler Interpreter Runtime Garbage Collector !2 A JavaScript Engine • Parser: entrypoint for script execution, usually emits custom Parser bytecode JIT Compiler • Bytecode then consumed by interpreter or JIT compiler • Executing code interacts with the Interpreter runtime which defines the Runtime representation of various data structures, provides builtin functions and objects, etc. Garbage • Garbage collector required to Collector deallocate memory !3 A JavaScript Engine • Parser: entrypoint for script execution, usually emits custom Parser bytecode JIT Compiler • Bytecode then consumed by interpreter or JIT compiler • Executing code interacts with the Interpreter runtime which defines the Runtime representation of various data structures, provides builtin functions and objects, etc. Garbage • Garbage collector required to Collector deallocate memory !4 A JavaScript Engine • Parser: entrypoint for script execution, usually emits custom Parser bytecode JIT Compiler • Bytecode then consumed by interpreter or JIT compiler • Executing code interacts with the Interpreter runtime which defines the Runtime representation of various data structures, provides builtin functions and objects, etc. Garbage • Garbage collector required to Collector deallocate memory !5 A JavaScript Engine • Parser: entrypoint for script execution, usually emits custom Parser bytecode JIT Compiler • Bytecode then consumed by interpreter or JIT compiler • Executing code interacts with the Interpreter runtime which defines the Runtime representation of various data structures, provides builtin functions and objects, etc. Garbage • Garbage collector required to Collector deallocate memory !6 Agenda 1. Background: Runtime Parser • Object representation and Builtins JIT Compiler 2. JIT Compiler Internals • Problem: missing type information • Solution: "speculative" JIT Interpreter 3. -
CS153: Compilers Lecture 19: Optimization
CS153: Compilers Lecture 19: Optimization Stephen Chong https://www.seas.harvard.edu/courses/cs153 Contains content from lecture notes by Steve Zdancewic and Greg Morrisett Announcements •HW5: Oat v.2 out •Due in 2 weeks •HW6 will be released next week •Implementing optimizations! (and more) Stephen Chong, Harvard University 2 Today •Optimizations •Safety •Constant folding •Algebraic simplification • Strength reduction •Constant propagation •Copy propagation •Dead code elimination •Inlining and specialization • Recursive function inlining •Tail call elimination •Common subexpression elimination Stephen Chong, Harvard University 3 Optimizations •The code generated by our OAT compiler so far is pretty inefficient. •Lots of redundant moves. •Lots of unnecessary arithmetic instructions. •Consider this OAT program: int foo(int w) { var x = 3 + 5; var y = x * w; var z = y - 0; return z * 4; } Stephen Chong, Harvard University 4 Unoptimized vs. Optimized Output .globl _foo _foo: •Hand optimized code: pushl %ebp movl %esp, %ebp _foo: subl $64, %esp shlq $5, %rdi __fresh2: movq %rdi, %rax leal -64(%ebp), %eax ret movl %eax, -48(%ebp) movl 8(%ebp), %eax •Function foo may be movl %eax, %ecx movl -48(%ebp), %eax inlined by the compiler, movl %ecx, (%eax) movl $3, %eax so it can be implemented movl %eax, -44(%ebp) movl $5, %eax by just one instruction! movl %eax, %ecx addl %ecx, -44(%ebp) leal -60(%ebp), %eax movl %eax, -40(%ebp) movl -44(%ebp), %eax Stephen Chong,movl Harvard %eax,University %ecx 5 Why do we need optimizations? •To help programmers… •They write modular, clean, high-level programs •Compiler generates efficient, high-performance assembly •Programmers don’t write optimal code •High-level languages make avoiding redundant computation inconvenient or impossible •e.g. -
Design and Implementation of Generics for the .NET Common Language Runtime
Design and Implementation of Generics for the .NET Common Language Runtime Andrew Kennedy Don Syme Microsoft Research, Cambridge, U.K. fakeÒÒ¸d×ÝÑeg@ÑicÖÓ×ÓfغcÓÑ Abstract cally through an interface definition language, or IDL) that is nec- essary for language interoperation. The Microsoft .NET Common Language Runtime provides a This paper describes the design and implementation of support shared type system, intermediate language and dynamic execution for parametric polymorphism in the CLR. In its initial release, the environment for the implementation and inter-operation of multiple CLR has no support for polymorphism, an omission shared by the source languages. In this paper we extend it with direct support for JVM. Of course, it is always possible to “compile away” polymor- parametric polymorphism (also known as generics), describing the phism by translation, as has been demonstrated in a number of ex- design through examples written in an extended version of the C# tensions to Java [14, 4, 6, 13, 2, 16] that require no change to the programming language, and explaining aspects of implementation JVM, and in compilers for polymorphic languages that target the by reference to a prototype extension to the runtime. JVM or CLR (MLj [3], Haskell, Eiffel, Mercury). However, such Our design is very expressive, supporting parameterized types, systems inevitably suffer drawbacks of some kind, whether through polymorphic static, instance and virtual methods, “F-bounded” source language restrictions (disallowing primitive type instanti- type parameters, instantiation at pointer and value types, polymor- ations to enable a simple erasure-based translation, as in GJ and phic recursion, and exact run-time types. -
Quaxe, Infinity and Beyond
Quaxe, infinity and beyond Daniel Glazman — WWX 2015 /usr/bin/whoami Primary architect and developer of the leading Web and Ebook editors Nvu and BlueGriffon Former member of the Netscape CSS and Editor engineering teams Involved in Internet and Web Standards since 1990 Currently co-chair of CSS Working Group at W3C New-comer in the Haxe ecosystem Desktop Frameworks Visual Studio (Windows only) Xcode (OS X only) Qt wxWidgets XUL Adobe Air Mobile Frameworks Adobe PhoneGap/Air Xcode (iOS only) Qt Mobile AppCelerator Visual Studio Two solutions but many issues Fragmentation desktop/mobile Heavy runtimes Can’t easily reuse existing c++ libraries Complex to have native-like UI Qt/QtMobile still require c++ Qt’s QML is a weak and convoluted UI language Haxe 9 years success of Multiplatform OSS language Strong affinity to gaming Wide and vibrant community Some press recognition Dead code elimination Compiles to native on all But no native GUI… platforms through c++ and java Best of all worlds Haxe + Qt/QtMobile Multiplatform Native apps, native performance through c++/Java C++/Java lib reusability Introducing Quaxe Native apps w/o c++ complexity Highly dynamic applications on desktop and mobile Native-like UI through Qt HTML5-based UI, CSS-based styling Benefits from Haxe and Qt communities Going from HTML5 to native GUI completeness DOM dynamism in native UI var b: Element = document.getElementById("thirdButton"); var t: Element = document.createElement("input"); t.setAttribute("type", "text"); t.setAttribute("value", "a text field"); b.parentNode.insertBefore(t, -
Dead Code Elimination for Web Applications Written in Dynamic Languages
Dead code elimination for web applications written in dynamic languages Master’s Thesis Hidde Boomsma Dead code elimination for web applications written in dynamic languages THESIS submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in COMPUTER ENGINEERING by Hidde Boomsma born in Naarden 1984, the Netherlands Software Engineering Research Group Department of Software Technology Department of Software Engineering Faculty EEMCS, Delft University of Technology Hostnet B.V. Delft, the Netherlands Amsterdam, the Netherlands www.ewi.tudelft.nl www.hostnet.nl © 2012 Hidde Boomsma. All rights reserved Cover picture: Hoster the Hostnet mascot Dead code elimination for web applications written in dynamic languages Author: Hidde Boomsma Student id: 1174371 Email: [email protected] Abstract Dead code is source code that is not necessary for the correct execution of an application. Dead code is a result of software ageing. It is a threat for maintainability and should therefore be removed. Many organizations in the web domain have the problem that their software grows and de- mands increasingly more effort to maintain, test, check out and deploy. Old features often remain in the software, because their dependencies are not obvious from the software documentation. Dead code can be found by collecting the set of code that is used and subtract this set from the set of all code. Collecting the set can be done statically or dynamically. Web applications are often written in dynamic languages. For dynamic languages dynamic analysis suits best. From the maintainability perspective a dynamic analysis is preferred over static analysis because it is able to detect reachable but unused code. -
Tricore Architecture Manual for a Detailed Discussion of Instruction Set Encoding and Semantics
User’s Manual, v2.3, Feb. 2007 TriCore 32-bit Unified Processor Core Embedded Applications Binary Interface (EABI) Microcontrollers Edition 2007-02 Published by Infineon Technologies AG 81726 München, Germany © Infineon Technologies AG 2007. All Rights Reserved. Legal Disclaimer The information given in this document shall in no event be regarded as a guarantee of conditions or characteristics (“Beschaffenheitsgarantie”). With respect to any examples or hints given herein, any typical values stated herein and/or any information regarding the application of the device, Infineon Technologies hereby disclaims any and all warranties and liabilities of any kind, including without limitation warranties of non- infringement of intellectual property rights of any third party. Information For further information on technology, delivery terms and conditions and prices please contact your nearest Infineon Technologies Office (www.infineon.com). Warnings Due to technical requirements components may contain dangerous substances. For information on the types in question please contact your nearest Infineon Technologies Office. Infineon Technologies Components may only be used in life-support devices or systems with the express written approval of Infineon Technologies, if a failure of such components can reasonably be expected to cause the failure of that life-support device or system, or to affect the safety or effectiveness of that device or system. Life support devices or systems are intended to be implanted in the human body, or to support and/or maintain and sustain and/or protect human life. If they fail, it is reasonable to assume that the health of the user or other persons may be endangered. User’s Manual, v2.3, Feb. -
Lecture Notes on Peephole Optimizations and Common Subexpression Elimination
Lecture Notes on Peephole Optimizations and Common Subexpression Elimination 15-411: Compiler Design Frank Pfenning and Jan Hoffmann Lecture 18 October 31, 2017 1 Introduction In this lecture, we discuss common subexpression elimination and a class of optimiza- tions that is called peephole optimizations. The idea of common subexpression elimination is to avoid to perform the same operation twice by replacing (binary) operations with variables. To ensure that these substitutions are sound we intro- duce dominance, which ensures that substituted variables are always defined. Peephole optimizations are optimizations that are performed locally on a small number of instructions. The name is inspired from the picture that we look at the code through a peephole and make optimization that only involve the small amount code we can see and that are indented of the rest of the program. There is a large number of possible peephole optimizations. The LLVM com- piler implements for example more than 1000 peephole optimizations [LMNR15]. In this lecture, we discuss three important and representative peephole optimiza- tions: constant folding, strength reduction, and null sequences. 2 Constant Folding Optimizations have two components: (1) a condition under which they can be ap- plied and the (2) code transformation itself. The optimization of constant folding is a straightforward example of this. The code transformation itself replaces a binary operation with a single constant, and applies whenever c1 c2 is defined. l : x c1 c2 −! l : x c (where c = -
Tribits Lifecycle Model Version
SANDIA REPORT SAND2012-0561 Unlimited Release Printed February 2012 TriBITS Lifecycle Model Version 1.0 A Lean/Agile Software Lifecycle Model for Research-based Computational Science and Engineering and Applied Mathematical Software Roscoe A. Bartlett Michael A. Heroux James M. Willenbring Prepared by Sandia National Laboratories Albuquerque, New Mexico 87185 and Livermore, California 94550 Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energys National Nuclear Security Administration under Contract DE-AC04-94AL85000. Approved for public release; further dissemination unlimited. Issued by Sandia National Laboratories, operated for the United States Department of Energy by Sandia Corporation. NOTICE: This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government, nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors, or their employees, make any warranty, express or implied, or assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or rep- resent that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government, any agency thereof, or any of their contractors or subcontractors. The views and opinions expressed herein do not necessarily state or reflect those of the United States Government, any agency thereof, or any of their contractors. -
Lecture 2 — Software Correctness
Lecture 2 — Software Correctness David J. Pearce School of Engineering and Computer Science Victoria University of Wellington Software Correctness RECOMMENDATIONS To Builders and Users of Software Make the most of effective software development technologies and formal methods. A variety of modern technologies — in particular, safe programming languages, static analysis, and formal methods — are likely to reduce the cost and difficulty of producing dependable software. Elementary best practices, such as source code control and systematic defect tracking, should be universally adopted, . –Software for Dependable Systems Patriot Missle 1991, Dhahran - Iraqi Scud missile hits barracks killing 28 soldiers - Patriot system was activated, but missed target by over 600m - Floating point representation of 0.1 was cause (rounding error over time, required 100 hours of continuous operation) See: “Engineering Disasters” https://www.youtube.com/watch?v=EMVBLg2MrLs (Image by Darkone - Own work, CC BY-SA 2.5) Unintended Acceleration? (Overview) Braking Problems (Toyota Prius) Drivers reported “sudden acceleration” when braking By 2010, over seven million cars recalled NASA experts called into investigate Specialists in static analysis Their report had significant redactions though Bookout vs Toyota, 2013 Victim’s awarded $1.5million each Barr provided expert testimony See “Unintended Acceleration and Other Embedded Software bugs”, Michael Barr, 2011 See “An Update on Toyota and Unintended acceleration” Unintended Acceleration? (NASA Analysis) Throttle -
Code Optimizations Recap Peephole Optimization
7/23/2016 Program Analysis Recap https://www.cse.iitb.ac.in/~karkare/cs618/ • Optimizations Code Optimizations – To improve efficiency of generated executable (time, space, resources …) Amey Karkare – Maintain semantic equivalence Dept of Computer Science and Engg • Two levels IIT Kanpur – Machine Independent Visiting IIT Bombay [email protected] – Machine Dependent [email protected] 2 Peephole Optimization Peephole optimization examples… • target code often contains redundant Redundant loads and stores instructions and suboptimal constructs • Consider the code sequence • examine a short sequence of target instruction (peephole) and replace by a Move R , a shorter or faster sequence 0 Move a, R0 • peephole is a small moving window on the target systems • Instruction 2 can always be removed if it does not have a label. 3 4 1 7/23/2016 Peephole optimization examples… Unreachable code example … Unreachable code constant propagation • Consider following code sequence if 0 <> 1 goto L2 int debug = 0 print debugging information if (debug) { L2: print debugging info } Evaluate boolean expression. Since if condition is always true the code becomes this may be translated as goto L2 if debug == 1 goto L1 goto L2 print debugging information L1: print debugging info L2: L2: The print statement is now unreachable. Therefore, the code Eliminate jumps becomes if debug != 1 goto L2 print debugging information L2: L2: 5 6 Peephole optimization examples… Peephole optimization examples… • flow of control: replace jump over jumps • Strength reduction -
Topic 1D Slides
Topic I (d): Static Single Assignment Form (SSA) 621-10F/Topic-1d-SSA 1 Reading List Slides: Topic Ix Other readings as assigned in class 621-10F/Topic-1d-SSA 2 ABET Outcome Ability to apply knowledge of SSA technique in compiler optimization An ability to formulate and solve the basic SSA construction problem based on the techniques introduced in class. Ability to analyze the basic algorithms using SSA form to express and formulate dataflow analysis problem A Knowledge on contemporary issues on this topic. 621-10F/Topic-1d-SSA 3 Roadmap Motivation Introduction: SSA form Construction Method Application of SSA to Dataflow Analysis Problems PRE (Partial Redundancy Elimination) and SSAPRE Summary 621-10F/Topic-1d-SSA 4 Prelude SSA: A program is said to be in SSA form iff Each variable is statically defined exactly only once, and each use of a variable is dominated by that variable’s definition. So, is straight line code in SSA form ? 621-10F/Topic-1d-SSA 5 Example 푿ퟏ In general, how to transform an arbitrary program into SSA form? Does the definition of X 푿ퟐ 2 dominate its use in the example? 푿ퟑ 흓 (푿ퟏ, 푿ퟐ) 푿 ퟒ 621-10F/Topic-1d-SSA 6 SSA: Motivation Provide a uniform basis of an IR to solve a wide range of classical dataflow problems Encode both dataflow and control flow information A SSA form can be constructed and maintained efficiently Many SSA dataflow analysis algorithms are more efficient (have lower complexity) than their CFG counterparts. 621-10F/Topic-1d-SSA 7 Algorithm Complexity Assume a 1 GHz machine, and an algorithm that takes f(n) steps (1 step = 1 nanosecond).