DOCSLIB.ORG

Function Fuzzer (Funcfuzz)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Function Fuzzer (Funcfuzz) SESSION ID: HTA-W12 Function Fuzzer (FuncFuzz) Jason Raber Function Fuzzer Malwarebytes #RSAC #RSAC Introduction What – Traditional fuzzing versus function fuzzing o Traditional: Blackbox/Whitebox – Brute Force o FuncFuzz: Inside-out fuzzing – Automation Why – Find zero-days quicker and find more of them – Find bugs that could be exploitable – Extend FuncFuzz into protections How – Unique reverse engineering techniques adapted to novel fuzzing ”Inside Out” fuzzing 2 #RSAC Microsoft Detours NOTE: SOURCE FUNCTION IS A DLL (I.E. KERNEL32, USER32, ETC.) System DLL rerouting via JMP trampolines 3 #RSAC Detours Hooked function acts as a trampoline that allows control flow from a binary to hooked function. _PrintEnter() and _PrintExit() record runtime data to log files. 4 #RSAC Stack overflow FUZZ To overwrite WHAT DOES A BOF LOOK LIKE? Lots of ways to exploit (i.e. UAF) 5 #RSAC Modern fuzzing Controlling the data inputs/outputs is key! Black box fuzzing by generating random data that is fed to the targeted binary’s external inputs. Without source code this approach is computationally expensive and the data set generated is not exhaustive. 6 #RSAC Inside-Out fuzzing What if we could create a fuzzing tool that is a more elegant approach to feeding generated data to functions? – Modify input/output parameters – Modify return values – Skip calling the “Real” API call – Pre-processing (additional function calls) – Post-processing 7 #RSAC Extending Detours Fuzz DLLs? – Microsoft does pretty good coding of system DLLs… Fuzz Internal Functions? – Detours does not hook internal functions Solution: Extending Detours to do internal hooks – easy with the aid of IDA Pro and a little Python scripting 8 #RSAC FuncFuzz Here we have control of the function Foo’s input parameter ‘char *bar’ Which takes char string “sample str” DETOURS ONLY HOOKS DLLS NOT BINARY FUNCTIONS These log files will later be analyzed by other off-line analysis programs for identifying vulnerabilities. 9 #RSAC Type Rebuilding To be able to fuzz function parameters we need to know how many, what order, and what type they are (w/o source code!) Leverage IDA Pro’s function prototyping 10 #RSAC Generating Hooks IDA Pro does a relatively good job of correctly identifying the correct parameters and types, even without the binary being compiled with debug information or supplied with a support program database (.pdb) file. Disassembly Decompiler Hint: ~ 40% Change of prototypes from Decompiler vs. Disassembly 11 #RSAC Parameter fuzzing Push the limits (force stack overflow) IDA script generate .cpp files to compile into FuncFuzz 12 #RSAC Parameter Fuzzing (continued) Key functions with string params FuncFuzz now controls the epilog and prolog of target function Overload those strings looking for vulnerabilities – Swap original string for new memory (larger) – Target does not check for sizing constraints…bingo! Also possible integer overflows 13 #RSAC Aftermath Crash => examine our log files Zero in on which targeted function caused the crash – i.e. main()->foo("A string that is just too long") Crash = Exploitable vulnerability? – Access points? o i.e. XML file from website, Windows register, etc o Logs great! = See the chain of events before crash • func1()->func2()->func3() 14 #RSAC Formula • Add hooks to binary using backend Detours • Detours designed for system DLLs not binaries Function Generation • Little help from IDA Pro on function prototypes​ • Does every hook run? Profile • Regenerate hooks with fuzzing API added Fuzz • Fuzz and interact with program to force execution paths • Interpret output from FuncFuzz Analysis • Determine is an inherent bug vs. exploitable vulnerability 15 #RSAC FuncFuzz Malwarebytes Software Secure our software! Several bugs and vulnerabilities found Vulnerabilities found in malware samples – Techniques to dump protected malware Due to sensitivity of Malwarebytes sensitivity code we will not show a demo 16 #RSAC Completed Windows 32bit PE files DLLs​ Spawned executables or services Integer Overflows – False Positives​ 17 #RSAC Limitations 64-bit​ UAF – Use After Free​ Clusters – Speed up​ Driver/Kernel Land 18 #RSAC Possibilities - Protections FuncFuzz is a man-in-the-middle probe​ – Verification​ o Could validate parameters!​ o SEH​ o Examine stack integrity (prolog/epilog)​ • Even d’ref target memory to see if local array being used​ 19 #RSAC Protections (continued) What we need: .pdb file (program database file)​ – Holds correct type info​ – If no .pdb, we need to be spot on with parameter building UAF – Create a Map @ runtime​ – Call Indirect (i.e. call eax)​ o Verify if memory has been free’d o More on this later… 20 #RSAC Additional Research - Automation Tool research – Dynamic taint analysis, Symbolic execution, and Constraint solving – High performance solution – Function Fuzzer currently extends strings; but, we could pair with an input generator o Compositional techniques • P. Godefroid. Compositional dynamic test generation. • J. Burnim and K. Sen. Heuristics for scalable dynamic test generation. • R. Majumdar and K. Sen. Hybrid concolic testing. 21 #RSAC Additional Research - UAF A different approach than buffer overflows Hooks to call indirectly (C++ virtual functions) to dynamically monitor legitimate heap address/function pointers. – Call in-directs – Object frees + later refs = crash (exploitable) o Could FuncFuzz detect? • Verify in-directs since we have them hooked 22 #RSAC “Apply” Slide Next week you should: – Identify software within your organization to fuzz In the first three months following this presentation you should: – We recommend that you download a free copy of the source code, please email: [email protected] Within six months you should: – Fix vulnerabilities found through this fuzzing method – Remember while a bug is not always a exploit, it could turn into one. Fix it! 23 #RSAC Questions? 24.
Load more

Recommended publications
  • Ironpython in Action
    IronPytho IN ACTION Michael J. Foord Christian Muirhead FOREWORD BY JIM HUGUNIN MANNING IronPython in Action Download at Boykma.Com Licensed to Deborah Christiansen <[email protected]> Download at Boykma.Com Licensed to Deborah Christiansen <[email protected]> IronPython in Action MICHAEL J. FOORD CHRISTIAN MUIRHEAD MANNING Greenwich (74° w. long.) Download at Boykma.Com Licensed to Deborah Christiansen <[email protected]> For online information and ordering of this and other Manning books, please visit www.manning.com. The publisher offers discounts on this book when ordered in quantity. For more information, please contact Special Sales Department Manning Publications Co. Sound View Court 3B fax: (609) 877-8256 Greenwich, CT 06830 email: [email protected] ©2009 by Manning Publications Co. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps. Recognizing the importance of preserving what has been written, it is Manning’s policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end. Recognizing also our responsibility to conserve the resources of our planet, Manning books are printed on paper that is at least 15% recycled and processed without the use of elemental chlorine.
    [Show full text]
  • Thriving in a Crowded and Changing World: C++ 2006–2020
    Thriving in a Crowded and Changing World: C++ 2006–2020 BJARNE STROUSTRUP, Morgan Stanley and Columbia University, USA Shepherd: Yannis Smaragdakis, University of Athens, Greece By 2006, C++ had been in widespread industrial use for 20 years. It contained parts that had survived unchanged since introduced into C in the early 1970s as well as features that were novel in the early 2000s. From 2006 to 2020, the C++ developer community grew from about 3 million to about 4.5 million. It was a period where new programming models emerged, hardware architectures evolved, new application domains gained massive importance, and quite a few well-financed and professionally marketed languages fought for dominance. How did C++ ś an older language without serious commercial backing ś manage to thrive in the face of all that? This paper focuses on the major changes to the ISO C++ standard for the 2011, 2014, 2017, and 2020 revisions. The standard library is about 3/4 of the C++20 standard, but this paper’s primary focus is on language features and the programming techniques they support. The paper contains long lists of features documenting the growth of C++. Significant technical points are discussed and illustrated with short code fragments. In addition, it presents some failed proposals and the discussions that led to their failure. It offers a perspective on the bewildering flow of facts and features across the years. The emphasis is on the ideas, people, and processes that shaped the language. Themes include efforts to preserve the essence of C++ through evolutionary changes, to simplify itsuse,to improve support for generic programming, to better support compile-time programming, to extend support for concurrency and parallel programming, and to maintain stable support for decades’ old code.
    [Show full text]
  • Cornell CS6480 Lecture 3 Dafny Robbert Van Renesse Review All States
    Cornell CS6480 Lecture 3 Dafny Robbert van Renesse Review All states Reachable Ini2al states states Target states Review • Behavior: infinite sequence of states • Specificaon: characterizes all possible/desired behaviors • Consists of conjunc2on of • State predicate for the inial states • Acon predicate characterizing steps • Fairness formula for liveness • TLA+ formulas are temporal formulas invariant to stuering • Allows TLA+ specs to be part of an overall system Introduction to Dafny What’s Dafny? • An imperave programming language • A (mostly funconal) specificaon language • A compiler • A verifier Dafny programs rule out • Run2me errors: • Divide by zero • Array index out of bounds • Null reference • Infinite loops or recursion • Implementa2ons that do not sa2sfy the specifica2ons • But it’s up to you to get the laFer correct Example 1a: Abs() method Abs(x: int) returns (x': int) ensures x' >= 0 { x' := if x < 0 then -x else x; } method Main() { var x := Abs(-3); assert x >= 0; print x, "\n"; } Example 1b: Abs() method Abs(x: int) returns (x': int) ensures x' >= 0 { x' := 10; } method Main() { var x := Abs(-3); assert x >= 0; print x, "\n"; } Example 1c: Abs() method Abs(x: int) returns (x': int) ensures x' >= 0 ensures if x < 0 then x' == -x else x' == x { x' := 10; } method Main() { var x := Abs(-3); print x, "\n"; } Example 1d: Abs() method Abs(x: int) returns (x': int) ensures x' >= 0 ensures if x < 0 then x' == -x else x' == x { if x < 0 { x' := -x; } else { x' := x; } } Example 1e: Abs() method Abs(x: int) returns (x': int) ensures
    [Show full text]
  • Advance Dynamic Malware Analysis Using Api Hooking
    www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume – 5 Issue -03 March, 2016 Page No. 16038-16040 Advance Dynamic Malware Analysis Using Api Hooking Ajay Kumar , Shubham Goyal Department of computer science Shivaji College, University of Delhi, Delhi, India [email protected] [email protected] Abstract— As in real world, in virtual world also there are type of Analysis is ineffective against many sophisticated people who want to take advantage of you by exploiting you software. Advanced static analysis consists of reverse- whether it would be your money, your status or your personal engineering the malware’s internals by loading the executable information etc. MALWARE helps these people into a disassembler and looking at the program instructions in accomplishing their goals. The security of modern computer order to discover what the program does. Advanced static systems depends on the ability by the users to keep software, analysis tells you exactly what the program does. OS and antivirus products up-to-date. To protect legitimate users from these threats, I made a tool B. Dynamic Malware Analysis (ADVANCE DYNAMIC MALWARE ANAYSIS USING API This is done by watching and monitoring the behavior of the HOOKING) that will inform you about every task that malware while running on the host. Virtual machines and software (malware) is doing over your machine at run-time Sandboxes are extensively used for this type of analysis. The Index Terms— API Hooking, Hooking, DLL injection, Detour malware is debugged while running using a debugger to watch the behavior of the malware step by step while its instructions are being processed by the processor and their live effects on I.
    [Show full text]
  • Neufuzz: Efficient Fuzzing with Deep Neural Network
    Received January 15, 2019, accepted February 6, 2019, date of current version April 2, 2019. Digital Object Identifier 10.1109/ACCESS.2019.2903291 NeuFuzz: Efficient Fuzzing With Deep Neural Network YUNCHAO WANG , ZEHUI WU, QIANG WEI, AND QINGXIAN WANG China National Digital Switching System Engineering and Technological Research Center, Zhengzhou 450000, China Corresponding author: Qiang Wei ([email protected]) This work was supported by National Key R&D Program of China under Grant 2017YFB0802901. ABSTRACT Coverage-guided graybox fuzzing is one of the most popular and effective techniques for discovering vulnerabilities due to its nature of high speed and scalability. However, the existing techniques generally focus on code coverage but not on vulnerable code. These techniques aim to cover as many paths as possible rather than to explore paths that are more likely to be vulnerable. When selecting the seeds to test, the existing fuzzers usually treat all seed inputs equally, ignoring the fact that paths exercised by different seed inputs are not equally vulnerable. This results in wasting time testing uninteresting paths rather than vulnerable paths, thus reducing the efficiency of vulnerability detection. In this paper, we present a solution, NeuFuzz, using the deep neural network to guide intelligent seed selection during graybox fuzzing to alleviate the aforementioned limitation. In particular, the deep neural network is used to learn the hidden vulnerability pattern from a large number of vulnerable and clean program paths to train a prediction model to classify whether paths are vulnerable. The fuzzer then prioritizes seed inputs that are capable of covering the likely to be vulnerable paths and assigns more mutation energy (i.e., the number of inputs to be generated) to these seeds.
    [Show full text]
  • Intel® Software Guard Extensions: Data Center Attestation Primitives
    Intel® Software Guard Extensions Data Center Attestation Primitives Installation Guide For Windows* OS Revision <1.0> <3/10/2020> Table of Contents Introduction .......................................................................................................................... 3 Components – Detailed Description ....................................................................................... 4 Platform Configuration .......................................................................................................... 6 Windows* Server OS Support ................................................................................................. 7 Installation Instructions ......................................................................................................... 8 Windows* Server 2016 LTSC ................................................................................................................. 8 Downloading the Software ........................................................................................................................... 8 Installation .................................................................................................................................................... 8 Windows* Server 2019 Installation ....................................................................................................... 9 Downloading the Software ........................................................................................................................... 9 Installation
    [Show full text]
  • Fine-Grained Energy Profiling for Power-Aware Application Design
    Fine-Grained Energy Profiling for Power-Aware Application Design Aman Kansal Feng Zhao Microsoft Research Microsoft Research One Microsoft Way, Redmond, WA One Microsoft Way, Redmond, WA [email protected] [email protected] ABSTRACT changing from double precision to single), or quality of service pro- Significant opportunities for power optimization exist at applica- vided [10]. Third, energy usage at the application layer may be tion design stage and are not yet fully exploited by system and ap- made dynamic [8]. For instance, an application hosted in a data plication designers. We describe the challenges developers face in center may decide to turn off certain low utility features if the en- optimizing software for energy efficiency by exploiting application- ergy budget is being exceeded, and an application on a mobile de- level knowledge. To address these challenges, we propose the de- vice may reduce its display quality [11] when battery is low. This velopment of automated tools that profile the energy usage of vari- is different from system layer techniques that may have to throttle ous resource components used by an application and guide the de- the throughput resulting in users being denied service. sign choices accordingly. We use a preliminary version of a tool While many application specific energy optimizations have been we have developed to demonstrate how automated energy profiling researched, there is a lack of generic tools that a developer may use helps a developer choose between alternative designs in the energy- at design time. Application specific optimizations require signif- performance trade-off space. icant development effort and are often only applicable to specific scenarios.
    [Show full text]
  • A Malware Analysis and Artifact Capture Tool Dallas Wright Dakota State University
    Dakota State University Beadle Scholar Masters Theses & Doctoral Dissertations Spring 3-2019 A Malware Analysis and Artifact Capture Tool Dallas Wright Dakota State University Follow this and additional works at: https://scholar.dsu.edu/theses Part of the Information Security Commons, and the Systems Architecture Commons Recommended Citation Wright, Dallas, "A Malware Analysis and Artifact Capture Tool" (2019). Masters Theses & Doctoral Dissertations. 327. https://scholar.dsu.edu/theses/327 This Dissertation is brought to you for free and open access by Beadle Scholar. It has been accepted for inclusion in Masters Theses & Doctoral Dissertations by an authorized administrator of Beadle Scholar. For more information, please contact [email protected]. A MALWARE ANALYSIS AND ARTIFACT CAPTURE TOOL A dissertation submitted to Dakota State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Cyber Operations March 2019 By Dallas Wright Dissertation Committee: Dr. Wayne Pauli Dr. Josh Stroschein Dr. Jun Liu ii iii Abstract Malware authors attempt to obfuscate and hide their execution objectives in their program’s static and dynamic states. This paper provides a novel approach to aid analysis by introducing a malware analysis tool which is quick to set up and use with respect to other existing tools. The tool allows for the intercepting and capturing of malware artifacts while providing dynamic control of process flow. Capturing malware artifacts allows an analyst to more quickly and comprehensively understand malware behavior and obfuscation techniques and doing so interactively allows multiple code paths to be explored. The faster that malware can be analyzed the quicker the systems and data compromised by it can be determined and its infection stopped.
    [Show full text]
  • Adding Self-Healing Capabilities to the Common Language Runtime
    Adding Self-healing capabilities to the Common Language Runtime Rean Griffith Gail Kaiser Columbia University Columbia University [email protected] [email protected] Abstract systems can leverage to maintain high system availability is to perform repairs in a degraded mode of operation[23, 10]. Self-healing systems require that repair mechanisms are Conceptually, a self-managing system is composed of available to resolve problems that arise while the system ex- four (4) key capabilities [12]; Monitoring to collect data ecutes. Managed execution environments such as the Com- about its execution and operating environment, performing mon Language Runtime (CLR) and Java Virtual Machine Analysis over the data collected from monitoring, Planning (JVM) provide a number of application services (applica- an appropriate course of action and Executing the plan. tion isolation, security sandboxing, garbage collection and Each of the four functions participating in the Monitor- structured exception handling) which are geared primar- Analyze-Plan-Execute (MAPE) loop consumes and pro- ily at making managed applications more robust. How- duces knowledgewhich is integral to the correct functioning ever, none of these services directly enables applications of the system. Over its execution lifetime the system builds to perform repairs or consistency checks of their compo- and refines a knowledge-base of its behavior and environ- nents. From a design and implementation standpoint, the ment. Information in the knowledge-base could include preferred way to enable repair in a self-healing system is patterns of resource utilization and a “scorecard” tracking to use an externalized repair/adaptation architecture rather the success of applying specific repair actions to detected or than hardwiring adaptation logic inside the system where it predicted problems.
    [Show full text]
  • Formalized Mathematics in the Lean Proof Assistant
    Formalized mathematics in the Lean proof assistant Robert Y. Lewis Vrije Universiteit Amsterdam CARMA Workshop on Computer-Aided Proof Newcastle, NSW June 6, 2019 Credits Thanks to the following people for some of the contents of this talk: Leonardo de Moura Jeremy Avigad Mario Carneiro Johannes Hölzl 1 38 Table of Contents 1 Proof assistants in mathematics 2 Dependent type theory 3 The Lean theorem prover 4 Lean’s mathematical libraries 5 Automation in Lean 6 The good and the bad 2 38 bfseries Proof assistants in mathematics Computers in mathematics Mathematicians use computers in various ways. typesetting numerical calculations symbolic calculations visualization exploration Let’s add to this list: checking proofs. 3 38 Interactive theorem proving We want a language (and an implementation of this language) for: Defining mathematical objects Stating properties of these objects Showing that these properties hold Checking that these proofs are correct Automatically generating these proofs This language should be: Expressive User-friendly Computationally eicient 4 38 Interactive theorem proving Working with a proof assistant, users construct definitions, theorems, and proofs. The proof assistant makes sure that definitions are well-formed and unambiguous, theorem statements make sense, and proofs actually establish what they claim. In many systems, this proof object can be extracted and verified independently. Much of the system is “untrusted.” Only a small core has to be relied on. 5 38 Interactive theorem proving Some systems with large
    [Show full text]
  • 1. with Examples of Different Programming Languages Show How Programming Languages Are Organized Along the Given Rubrics: I
    AGBOOLA ABIOLA CSC302 17/SCI01/007 COMPUTER SCIENCE ASSIGNMENT ​ 1. With examples of different programming languages show how programming languages are organized along the given rubrics: i. Unstructured, structured, modular, object oriented, aspect oriented, activity oriented and event oriented programming requirement. ii. Based on domain requirements. iii. Based on requirements i and ii above. 2. Give brief preview of the evolution of programming languages in a chronological order. 3. Vividly distinguish between modular programming paradigm and object oriented programming paradigm. Answer 1i). UNSTRUCTURED LANGUAGE DEVELOPER DATE Assembly Language 1949 FORTRAN John Backus 1957 COBOL CODASYL, ANSI, ISO 1959 JOSS Cliff Shaw, RAND 1963 BASIC John G. Kemeny, Thomas E. Kurtz 1964 TELCOMP BBN 1965 MUMPS Neil Pappalardo 1966 FOCAL Richard Merrill, DEC 1968 STRUCTURED LANGUAGE DEVELOPER DATE ALGOL 58 Friedrich L. Bauer, and co. 1958 ALGOL 60 Backus, Bauer and co. 1960 ABC CWI 1980 Ada United States Department of Defence 1980 Accent R NIS 1980 Action! Optimized Systems Software 1983 Alef Phil Winterbottom 1992 DASL Sun Micro-systems Laboratories 1999-2003 MODULAR LANGUAGE DEVELOPER DATE ALGOL W Niklaus Wirth, Tony Hoare 1966 APL Larry Breed, Dick Lathwell and co. 1966 ALGOL 68 A. Van Wijngaarden and co. 1968 AMOS BASIC FranÇois Lionet anConstantin Stiropoulos 1990 Alice ML Saarland University 2000 Agda Ulf Norell;Catarina coquand(1.0) 2007 Arc Paul Graham, Robert Morris and co. 2008 Bosque Mark Marron 2019 OBJECT-ORIENTED LANGUAGE DEVELOPER DATE C* Thinking Machine 1987 Actor Charles Duff 1988 Aldor Thomas J. Watson Research Center 1990 Amiga E Wouter van Oortmerssen 1993 Action Script Macromedia 1998 BeanShell JCP 1999 AngelScript Andreas Jönsson 2003 Boo Rodrigo B.
    [Show full text]
  • Practical Malware Analysis
    PRAISE FOR PRACTICAL MALWARE ANALYSIS Digital Forensics Book of the Year, FORENSIC 4CAST AWARDS 2013 “A hands-on introduction to malware analysis. I’d recommend it to anyone who wants to dissect Windows malware.” —Ilfak Guilfanov, CREATOR OF IDA PRO “The book every malware analyst should keep handy.” —Richard Bejtlich, CSO OF MANDIANT & FOUNDER OF TAOSECURITY “This book does exactly what it promises on the cover; it’s crammed with detail and has an intensely practical approach, but it’s well organised enough that you can keep it around as handy reference.” —Mary Branscombe, ZDNET “If you’re starting out in malware analysis, or if you are are coming to analysis from another discipline, I’d recommend having a nose.” —Paul Baccas, NAKED SECURITY FROM SOPHOS “An excellent crash course in malware analysis.” —Dino Dai Zovi, INDEPENDENT SECURITY CONSULTANT “The most comprehensive guide to analysis of malware, offering detailed coverage of all the essential skills required to understand the specific challenges presented by modern malware.” —Chris Eagle, SENIOR LECTURER OF COMPUTER SCIENCE AT THE NAVAL POSTGRADUATE SCHOOL “A great introduction to malware analysis. All chapters contain detailed technical explanations and hands-on lab exercises to get you immediate exposure to real malware.” —Sebastian Porst, GOOGLE SOFTWARE ENGINEER “Brings reverse-engineering to readers of all skill levels. Technically rich and accessible, the labs will lead you to a deeper understanding of the art and science of reverse-engineering. I strongly believe this will become the defacto text for learning malware analysis in the future.” —Danny Quist, PHD, FOUNDER OF OFFENSIVE COMPUTING “An awesome book .
    [Show full text]