Detecting PHP-Based Cross-Site Scripting Vulnerabilities Using Static Program Analysis
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Advanced Systems Security: Symbolic Execution
Systems and Internet Infrastructure Security Network and Security Research Center Department of Computer Science and Engineering Pennsylvania State University, University Park PA Advanced Systems Security:! Symbolic Execution Trent Jaeger Systems and Internet Infrastructure Security (SIIS) Lab Computer Science and Engineering Department Pennsylvania State University Systems and Internet Infrastructure Security (SIIS) Laboratory Page 1 Problem • Programs have flaws ‣ Can we find (and fix) them before adversaries can reach and exploit them? • Then, they become “vulnerabilities” Systems and Internet Infrastructure Security (SIIS) Laboratory Page 2 Vulnerabilities • A program vulnerability consists of three elements: ‣ A flaw ‣ Accessible to an adversary ‣ Adversary has the capability to exploit the flaw • Which one should we focus on to find and fix vulnerabilities? ‣ Different methods are used for each Systems and Internet Infrastructure Security (SIIS) Laboratory Page 3 Is It a Flaw? • Problem: Are these flaws? ‣ Failing to check the bounds on a buffer write? ‣ printf(char *n); ‣ strcpy(char *ptr, char *user_data); ‣ gets(char *ptr) • From man page: “Never use gets().” ‣ sprintf(char *s, const char *template, ...) • From gnu.org: “Warning: The sprintf function can be dangerous because it can potentially output more characters than can fit in the allocation size of the string s.” • Should you fix all of these? Systems and Internet Infrastructure Security (SIIS) Laboratory Page 4 Is It a Flaw? • Problem: Are these flaws? ‣ open( filename, O_CREAT ); -
Opportunities and Open Problems for Static and Dynamic Program Analysis Mark Harman∗, Peter O’Hearn∗ ∗Facebook London and University College London, UK
1 From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis Mark Harman∗, Peter O’Hearn∗ ∗Facebook London and University College London, UK Abstract—This paper1 describes some of the challenges and research questions that target the most productive intersection opportunities when deploying static and dynamic analysis at we have yet witnessed: that between exciting, intellectually scale, drawing on the authors’ experience with the Infer and challenging science, and real-world deployment impact. Sapienz Technologies at Facebook, each of which started life as a research-led start-up that was subsequently deployed at scale, Many industrialists have perhaps tended to regard it unlikely impacting billions of people worldwide. that much academic work will prove relevant to their most The paper identifies open problems that have yet to receive pressing industrial concerns. On the other hand, it is not significant attention from the scientific community, yet which uncommon for academic and scientific researchers to believe have potential for profound real world impact, formulating these that most of the problems faced by industrialists are either as research questions that, we believe, are ripe for exploration and that would make excellent topics for research projects. boring, tedious or scientifically uninteresting. This sociological phenomenon has led to a great deal of miscommunication between the academic and industrial sectors. I. INTRODUCTION We hope that we can make a small contribution by focusing on the intersection of challenging and interesting scientific How do we transition research on static and dynamic problems with pressing industrial deployment needs. Our aim analysis techniques from the testing and verification research is to move the debate beyond relatively unhelpful observations communities to industrial practice? Many have asked this we have typically encountered in, for example, conference question, and others related to it. -
The Art, Science, and Engineering of Fuzzing: a Survey
1 The Art, Science, and Engineering of Fuzzing: A Survey Valentin J.M. Manes,` HyungSeok Han, Choongwoo Han, Sang Kil Cha, Manuel Egele, Edward J. Schwartz, and Maverick Woo Abstract—Among the many software vulnerability discovery techniques available today, fuzzing has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering real-world software vulnerabilities. At a high level, fuzzing refers to a process of repeatedly running a program with generated inputs that may be syntactically or semantically malformed. While researchers and practitioners alike have invested a large and diverse effort towards improving fuzzing in recent years, this surge of work has also made it difficult to gain a comprehensive and coherent view of fuzzing. To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current fuzzing literature. We methodically explore the design decisions at every stage of our model fuzzer by surveying the related literature and innovations in the art, science, and engineering that make modern-day fuzzers effective. Index Terms—software security, automated software testing, fuzzing. ✦ 1 INTRODUCTION Figure 1 on p. 5) and an increasing number of fuzzing Ever since its introduction in the early 1990s [152], fuzzing studies appear at major security conferences (e.g. [225], has remained one of the most widely-deployed techniques [52], [37], [176], [83], [239]). In addition, the blogosphere is to discover software security vulnerabilities. At a high level, filled with many success stories of fuzzing, some of which fuzzing refers to a process of repeatedly running a program also contain what we consider to be gems that warrant a with generated inputs that may be syntactically or seman- permanent place in the literature. -
Static Program Analysis As a Fuzzing Aid
Static Program Analysis as a Fuzzing Aid Bhargava Shastry1( ), Markus Leutner1, Tobias Fiebig1, Kashyap Thimmaraju1, Fabian Yamaguchi2, Konrad Rieck2, Stefan Schmid3, Jean-Pierre Seifert1, and Anja Feldmann1 1 TU Berlin, Berlin, Germany [email protected] 2 TU Braunschweig, Braunschweig, Germany 3 Aalborg University, Aalborg, Denmark Abstract. Fuzz testing is an effective and scalable technique to per- form software security assessments. Yet, contemporary fuzzers fall short of thoroughly testing applications with a high degree of control-flow di- versity, such as firewalls and network packet analyzers. In this paper, we demonstrate how static program analysis can guide fuzzing by aug- menting existing program models maintained by the fuzzer. Based on the insight that code patterns reflect the data format of inputs pro- cessed by a program, we automatically construct an input dictionary by statically analyzing program control and data flow. Our analysis is performed before fuzzing commences, and the input dictionary is sup- plied to an off-the-shelf fuzzer to influence input generation. Evaluations show that our technique not only increases test coverage by 10{15% over baseline fuzzers such as afl but also reduces the time required to expose vulnerabilities by up to an order of magnitude. As a case study, we have evaluated our approach on two classes of network applications: nDPI, a deep packet inspection library, and tcpdump, a network packet analyzer. Using our approach, we have uncovered 15 zero-day vulnerabilities in the evaluated software that were not found by stand-alone fuzzers. Our work not only provides a practical method to conduct security evalua- tions more effectively but also demonstrates that the synergy between program analysis and testing can be exploited for a better outcome. -
A Randomized Dynamic Program Analysis Technique for Detecting Real Deadlocks
A Randomized Dynamic Program Analysis Technique for Detecting Real Deadlocks Pallavi Joshi Chang-Seo Park Mayur Naik Koushik Sen Intel Research, Berkeley, USA EECS Department, UC Berkeley, USA [email protected] {pallavi,parkcs,ksen}@cs.berkeley.edu Abstract cipline followed by the parent software and this sometimes results We present a novel dynamic analysis technique that finds real dead- in deadlock bugs [17]. locks in multi-threaded programs. Our technique runs in two stages. Deadlocks are often difficult to find during the testing phase In the first stage, we use an imprecise dynamic analysis technique because they happen under very specific thread schedules. Coming to find potential deadlocks in a multi-threaded program by observ- up with these subtle thread schedules through stress testing or ing an execution of the program. In the second stage, we control random testing is often difficult. Model checking [15, 11, 7, 14, 6] a random thread scheduler to create the potential deadlocks with removes these limitations of testing by systematically exploring high probability. Unlike other dynamic analysis techniques, our ap- all thread schedules. However, model checking fails to scale for proach has the advantage that it does not give any false warnings. large multi-threaded programs due to the exponential increase in We have implemented the technique in a prototype tool for Java, the number of thread schedules with execution length. and have experimented on a number of large multi-threaded Java Several program analysis techniques, both static [19, 10, 2, 9, programs. We report a number of previously known and unknown 27, 29, 21] and dynamic [12, 13, 4, 1], have been developed to de- real deadlocks that were found in these benchmarks. -
Lecture Notes on Language-Based Security
Lecture Notes on Language-Based Security Erik Poll Radboud University Nijmegen Updated September 2019 These lecture notes discuss language-based security, which is the term loosely used for the collection of features and mechanisms that a programming language can provide to help in building secure applications. These features include: memory safety and typing, as offered by so-called safe pro- gramming languages; language mechanisms to enforce various forms of access con- trol (such as sandboxing), similar to access control traditionally offered by operating systems; mechanisms that go beyond what typical operating systems do, such as information flow control. Contents 1 Introduction 4 2 Operating system based security 7 2.1 Operating system abstractions and access control . .7 2.2 Imperfections in abstractions . 10 2.3 What goes wrong . 10 3 Safe programming languages 12 3.1 Safety & (un)definedness . 13 3.1.1 Safety and security . 14 3.2 Memory safety . 15 3.2.1 Ensuring memory safety . 15 3.2.2 Memory safety and undefinedness . 16 3.2.3 Stronger notions of memory safety . 16 3.3 Type safety . 17 3.3.1 Expressivity . 18 3.3.2 Breaking type safety . 18 3.3.3 Ensuring type safety . 19 3.4 Other notions of safety . 19 3.4.1 Safety concerns in low-level languages . 20 3.4.2 Safe arithmetic . 20 3.4.3 Thread safety . 21 3.5 Other language-level guarantees . 22 3.5.1 Visibility . 22 3.5.2 Constant values and immutable data structures . 23 4 Language-based access control 26 4.1 Language platforms . -
Seminar on Program Analysis ECS 289C (Programming Languages and Compilers) Winter 2015 Preliminary Reading List
Seminar on Program Analysis ECS 289C (Programming Languages and Compilers) Winter 2015 Preliminary Reading List 1. Abstract Interpretation and Dataflow Analysis (a) Global Data Flow Analysis and Iterative Algorithms [J.ACM'76] (b) Abstract Interpretation: A Unified Lattice Model for Static Analysis of Programs by Construction or Approximation of Fixpoints [POPL'77] (c) Precise Interprocedural Dataflow Analysis via Graph Reachability [POPL'95] 2. Pointer Analysis (a) Points-to Analysis in Almost Linear Time [POPL'96] (b) Pointer Analysis: Haven't We Solved This Problem Yet? [PASTE'01] (c) The Ant and the Grasshoper: Fast and Accurate Pointer Analysis for Millions of Lines of Code [PLDI'07] 3. Program Slicing (a) Interprocedural Slicing Using Dependence Graphs [PLDI'88] (b) Dynamic Program Slicing [PLDI'90] 4. Dynamic Symbolic Execution and Test Generation (a) DART: Directed Automated Random Testing [PLDI'05] (b) CUTE: A Concolic Unit Testing Engine for C [FSE'05] (c) KLEE: Unassisted and Automatic Generation of High-Coverage Tests for Complex System Programs [OSDI'08] 5. Model Checking (a) Counterexample-Guided Abstraction Refinement [CAV'00] (b) Automatic predicate abstraction of C programs [PLDI'01] (c) Lazy Abstraction [POPL'02] 6. Concurrency (a) Learning from Mistakes: A Comprehensive Study on Real World Concurrency Bug Characteristics [ASPLOS'08] (b) Finding and Reproducing Heisenbugs in Concurrent Programs [OSDI'08] 1 (c) Life, Death, and the Critical Transition: Finding Liveness Bugs in Systems Code [NSDI'07] (d) RacerX: Effective, Static Detection of Race Conditions and Deadlocks [SOSP'03] (e) FastTrack: Efficient and Precise Dynamic Race Detection [PLDI'09] (f) A Randomized Dynamic Program Analysis Technique for Detecting Real Dead- locks [PLDI09] 7. -
Decoupling Dynamic Program Analysis from Execution in Virtual Environments
Decoupling dynamic program analysis from execution in virtual environments Jim Chow Tal Garfinkel Peter M. Chen VMware Abstract 40x [18, 26], rendering many production workloads un- Analyzing the behavior of running programs has a wide usable. In non-production settings, such as program de- variety of compelling applications, from intrusion detec- velopment or quality assurance, this overhead may dis- tion and prevention to bug discovery. Unfortunately, the suade use in longer, more realistic tests. Further, the per- high runtime overheads imposed by complex analysis formance perturbations introduced by these analyses can techniques makes their deployment impractical in most lead to Heisenberg effects, where the phenomena under settings. We present a virtual machine based architec- observation is changed or lost due to the measurement ture called Aftersight ameliorates this, providing a flex- itself [25]. ible and practical way to run heavyweight analyses on We describe a system called Aftersight that overcomes production workloads. these limitations via an approach called decoupled analy- Aftersight decouples analysis from normal execution sis. Decoupled analysis moves analysis off the computer by logging nondeterministic VM inputs and replaying that is executing the main workload by separating execu- them on a separate analysis platform. VM output can tion and analysis into two tasks: recording, where system be gated on the results of an analysis for intrusion pre- execution is recorded in full with minimal interference, vention or analysis can run at its own pace for intrusion and analysis, where the log of the execution is replayed detection and best effort prevention. Logs can also be and analyzed. stored for later analysis offline for bug finding or foren- Aftersight is able to record program execution effi- sics, allowing analyses that would otherwise be unusable ciently using virtual machine recording and replay [4, 9, to be applied ubiquitously. -
Practical Concurrency Testing Or: How I Learned to Stop Worrying and Love the Exponential Explosion
Practical Concurrency Testing or: How I Learned to Stop Worrying and Love the Exponential Explosion Ben Blum Decembruary 2018 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Garth Gibson, Chair David A. Eckhardt Brandon Lucia Haryadi Gunawi, University of Chicago Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Copyright © 2018 Ben Blum September 20, 2018 DRAFT Keywords: landslide terminal, baggage claim, ground transportation, ticketing September 20, 2018 DRAFT For my family, my teachers, and my students. September 20, 2018 DRAFT iv September 20, 2018 DRAFT Abstract Concurrent programming presents a challenge to students and experts alike because of the complexity of multithreaded interactions and the diffi- culty to reproduce and reason about bugs. Stateless model checking is a con- currency testing approach which forces a program to interleave its threads in many different ways, checking for bugs each time. This technique is power- ful, in principle capable of finding any nondeterministic bug in finite time, but suffers from exponential explosion as program size increases. Checking an exponential number of thread interleavings is not a practical or predictable approach for programmers to find concurrency bugs before their project dead- lines. In this thesis, I develop several new techniques to make stateless model checking more practical for human use. I have built Landslide, a stateless model checker specializing in undergraduate operating systems class projects. Landslide extends the traditional model checking algorithm with a new frame- work for automatically managing multiple state spaces according to their esti- mated completion times, which I show quickly finds bugs should they exist and also quickly verifies correctness otherwise. -
Introduction to Dynamic Program Analysis with Disl
Introduction to Dynamic Program Analysis with DiSL Lukáš Marek Yudi Zheng Danilo Ansaloni Charles University University of Lugano University of Lugano Prague, Czech Republic Lugano, Switzerland Lugano, Switzerland [email protected] fi[email protected] fi[email protected] Lubomír Bulej Aibek Sarimbekov Walter Binder Charles University University of Lugano University of Lugano Prague, Czech Republic Lugano, Switzerland Lugano, Switzerland [email protected] fi[email protected] fi[email protected] Zhengwei Qi Shanghai Jiao Tong University Shanghai, China [email protected] ABSTRACT give the expert developer many opprtunities to optimize the DiSL is a new domain-specific language for bytecode instru- inserted code, enabling efficient DPA tools. mentation with complete bytecode coverage. It reconciles Some researchers have proposed the use of aspect-oriented expressiveness and efficiency of low-level bytecode manipu- programming (AOP) for creating DPA tools. Thanks to lation libraries with a convenient, high-level programming its pointcut/advice mechanism, AOP offers a high abstrac- model inspired by aspect-oriented programming. This paper tion level that allows to concisely specify certain kinds of summarizes the language features of DiSL and gives a brief instrumentations. Consequently, the use of AOP promises overview of several dynamic program analysis tools that were to significantly reduce the development effort for building ported to DiSL. DiSL is available as open-source under the DPA tools. Unfortunately, mainstream AOP languages such Apache 2.0 license. as AspectJ lack join points at the level of e.g. basic blocks of code or individual bytecodes that would be needed for cer- tain DPA tools. -
Combining Static and Dynamic Program Analysis Techniques for Checking Relational Properties
Combining Static and Dynamic Program Analysis Techniques for Checking Relational Properties zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften von der KIT-Fakultät für Informatik des Karlsruher Instituts für Technologie (KIT) genehmigte Dissertation von Mihai Herda aus Bras, ov, Rumänien Tag der mündlichen Prüfung: 13.12.2019 Erster Gutachter: Prof. Dr. Bernhard Beckert Zweiter Gutachter: Prof. Dr. Shmuel Tyszberowicz Combining Static and Dynamic Program Analysis Techniques for Checking Relational Properties Mihai Herda Ich versichere wahrheitsgemäß, die Dissertation bis auf die dort angegebe- nen Hilfen selbständig angefertigt, alle benutzten Hilfsmittel vollständig und genau angegeben und alles kenntlich gemacht zu haben, was aus Arbeiten anderer und eigenen Veröffentlichungen unverändert oder mit Änderungen entnommen wurde. Karlsruhe, Mihai Herda Acknowledgements I thank Prof. Bernhard Beckert for giving me the opportunity to pursue a PhD in his research group and for his useful advice throughout my time as a PhD student. I thank Prof. Shmuel Tyszberowicz for the great collaboration that we had, for his feedback, and for agreeing to be the second reviewer of this thesis. I thank my current and former colleagues, Aboubakr Achraf El Ghazi, Thorsten Bormer, Stephan Falke, David Farago, Christoph Gladisch, Da- niel Grahl, Sarah Grebing, Markus Iser, Michael Kirsten, Jonas Klamroth, Vladimir Klebanov, Marko Kleine Büning, Tianhai Liu, Simone Meinhart, Florian Merz, Jonas Schiffl, Christoph Scheben, Prof. Peter Schmitt, Prof. Carsten Sinz, Mattias Ulbrich, and Alexander Weigl, for the great collabora- tion and for the great discussions that we had during this time. Working with you is and has been a pleasure. I thank the students Etienne Brunner, Stephan Gocht, Holger Klein, Daniel Lentzsch, Joachim Müssig, Joana Plewnia, Ulla Scheler, Chiara Staudenmaier, Benedikt Wagner, and Pascal Zwick, for their work that I had the pleasure to supervise. -
Experiences with Building Security Checking and Understanding Tool
RC24243 (W0704-095) April 18, 2007 Computer Science IBM Research Report Experiences with Building Security Checking and Understanding Tool Ted Habeck, Larry Koved, Orlando Marquez, Vugranam C. Sreedhar, Michael Steiner, Wietse Venema, Samuel Weber IBM Research Division Thomas J. Watson Research Center P.O. Box 704 Yorktown Heights, NY 10598 Gabriela Cretu Columbia University New York, NY 10027 Krishnaprasad Vikram Cornell University Ithaca, NY 14850 Research Division Almaden - Austin - Beijing - Haifa - India - T. J. Watson - Tokyo - Zurich LIMITED DISTRIBUTION NOTICE: This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g. , payment of royalties). Copies may be requested from IBM T. J. Watson Research Center , P. O. Box 218, Yorktown Heights, NY 10598 USA (email: [email protected]). Some reports are available on the internet at http://domino.watson.ibm.com/library/CyberDig.nsf/home . Experiences With Building Security Checking and Understanding Tool Ted Habeck Vugranam C. Sreedhar Gabriela Cretu ∗ Larry Koved Michael Steiner Columbia University Orlando Marquez Wietse Venema New York, NY 10027 IBM TJ Watson Research Center Samuel Weber [email protected] Hawthrone, NY 10532 IBM TJ Watson Research Center {habeck,koved,omarquez}@us.ibm.com Hawthrone, NY 10532 {vugranam,msteiner,wietse}@us.ibm.com, [email protected] Krishnaprasad Vikram∗ Cornell University Ithaca, NY 14850 [email protected] Abstract tive assistance to this large and growing community.