Defensive Programming
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Bounds Checking on GPU
Noname manuscript No. (will be inserted by the editor) Bounds Checking on GPU Troels Henriksen Received: date / Accepted: date Abstract We present a simple compilation strategy for safety-checking array indexing in high-level languages on GPUs. Our technique does not depend on hardware support for abnormal termination, and is designed to be efficient in the non-failing case. We rely on certain properties of array languages, namely the absence of arbitrary cross-thread communication, to ensure well-defined execution in the presence of failures. We have implemented our technique in the compiler for the functional array language Futhark, and an empirical eval- uation on 19 benchmarks shows that the geometric mean overhead of checking array indexes is respectively 4% and 6% on two different GPUs. Keywords GPU · functional programming · compilers 1 Introduction Programming languages can be divided roughly into two categories: unsafe languages, where programming errors can lead to unpredictable results at run- time; and safe languages, where all risky operations are guarded by run-time checks. Consider array indexing, where an invalid index will lead an unsafe lan- guage to read from an invalid memory address. At best, the operating system will stop the program, but at worst, the program will silently produce invalid results. A safe language will perform bounds checking to verify that the array index is within the bounds of the array, and if not, signal that something is amiss. Some languages perform an abnormal termination of the program and print an error message pointing to the offending program statement. Other languages throw an exception, allowing the problem to be handled by the pro- gram itself. -
Defensive Programming Is Sometimes Referred to A
Defensive Programming Is Sometimes Referred To A Is Muhammad cubical or giddier after all-star Clarke initiating so selflessly? Urethritic Weider dispersing else while Stefan always will his cassoulet solaces full, he decarbonise so aerobiologically. Reversed and lady-killer Neville never huts his chakra! The defensive programming is sometimes referred to defensive programming a developer to the max, the same approach can create a new and comments without affecting the demilitarization of As defensive programming are sometimes referred to discover which suggests that programs are crucial to break them? Like other codes, is then betatested in a simulated production environment. Throw argument is sometimes referred to refer to. Unexpected errors require interactive debugging to figure out what went wrong. You program is defensive programming in programs based on every type to. How rare you mind what note request is singing? Defensive Programming Assigning NULL to Dangling Pointers. Earth destroyed an Indian satellite in orbit three hundred kilometres away. Summary of defense is sometimes referred to refer to fail from inevitable bugs is! Making the software itself in a predictable manner despite unexpected inputs or user actions. Sign Up For Free! Ruby today, and States not easily agreeing on busy, and confirmed. Pseudocode is sometimes referred to refer to keep in programs stable after a program directly with errors that is often ripple back to contain it! Chapter 4 Defensive Programming NanoPDF. It is likely to internet, namely the rights, sometimes referred to. And now, it is important should be problem of their limitations and liabilities. When getting rid of other with little by the two problems in defensive programming is sometimes to a given me exactly mean that makes a debugging code avoid syntactic one. -
Buffer Overflow Attack Samanvay Gupta 4Th Year 1St Semester (Computer Science Department) Visvesvaraya College of Engineering & Technology
IOSR Journal of Computer Engineering (IOSRJCE) ISSN : 2278-0661 Volume 1, Issue 1 (May-June 2012), PP 10-23 www.iosrjournals.org Buffer Overflow Attack Samanvay Gupta 4th year 1st semester (Computer Science Department) Visvesvaraya College of Engineering & Technology Abstract -- Exploits, vulnerabilities, and buffer-overflow techniques have been used by malicious hackers and virus writers for a long time. In order to attack and get the remote root privilege, using buffer overflow and suidprogram has become the commonly used method for hackers. This paper include vast idea and information regarding the buffer overflow as history of Vulnerabilities, buffers, stack, registers, Buffer Overflow Vulnerabilities and Attacks, current buffer over flow, Shell code, Buffer Overflow Issues, the Source of the Problem, prevention/detection of Buffer Overflow attacks and Finally how to react towards Buffer Overflows. The objective of this study is to take one inside the buffer overflow attack and bridge the gap between the “descriptive account” and the “technically intensive account” Introduction Buffer overflows have been documented and understood as early as 1972[23]. In computer security and programming, a buffer overflow is an anomaly where a program, while writing data to a buffer, overruns the buffer's boundary and overwrites adjacent memory. This is a special case of violation of memory safety. Buffer overflows can be triggered by inputs that are designed to execute code, or alter the way the program operates. This may result in erratic program behavior, including memory access errors, incorrect results, a crash, or a breach of system security. Thus, they are the basis of many software vulnerabilities and can be maliciously exploited. -
Cmsc330 Cybersecurity
cmsc330 Cybersecurity Cybersecurity Breaches Major security breaches of computer systems are a fact of life. They affect companies, governments, and individuals. Focusing on breaches of individuals' information, consider just a few examples: Equifax (2017) - 145 million consumers’ records Adobe (2013) - 150 million records, 38 million users eBay (2014) - 145 million records Anthem (2014) - Records of 80 million customers Target (2013) - 110 million records Heartland (2008) - 160 million records Vulnerabilities: Security-relevant Defects The causes of security breaches are varied but many of them, including those given above, owe to a defect (or bug) or design flaw in a targeted computer system's software. The software problem can be exploited by an attacker. An exploit is a particular, cleverly crafted input, or a series of (usually unintuitive) interactions with the system, which trigger the bug or flaw in a way that helps the attacker. Kinds of Vulnerability One obvious sort of vulnerability is a bug in security policy enforcement code. For example, suppose you are implementing an operating system and you have code to enforce access control policies on files. This is the code that makes sure that if Alice's policy says that only she is allowed to edit certain files, then user Bob will not be allowed to modify them. Perhaps your enforcement code failed to consider a corner case, and as a result Bob is able to write those files even though Alice's policy says he shouldn't. This is a vulnerability. A more surprising sort of vulnerability is a bug in code that seems to have nothing to do with enforcing security all. -
Safe Arrays Via Regions and Dependent Types
Safe Arrays via Regions and Dependent Types Christian Grothoff1 Jens Palsberg1 Vijay Saraswat2 1 UCLA Computer Science Department University of California, Los Angeles [email protected], [email protected] 2 IBM T.J. Watson Research Center P.O. Box 704, Yorktown Heights, NY 10598, USA [email protected] Abstract. Arrays over regions of points were introduced in ZPL in the late 1990s and later adopted in Titanium and X10 as a means of simpli- fying the programming of high-performance software. A region is a set of points, rather than an interval or a product of intervals, and enables the programmer to write a loop that iterates over a region. While conve- nient, regions do not eliminate the risk of array bounds violations. Until now, language implementations have resorted to checking array accesses dynamically or to warning the programmer that bounds violations lead to undefined behavior. In this paper we show that a type system for a language with arrays over regions can guarantee that array bounds vi- olations cannot occur. We have developed a core language and a type system, proved type soundness, settled the complexity of the key deci- sion problems, implemented an X10 version which embodies the ideas of our core language, written a type checker for our X10 version, and ex- perimented with a variety of benchmark programs. Our type system uses dependent types and enables safety without dynamic bounds checks. 1 Introduction Type-safe languages allow programmers to be more productive by eliminating such difficult-to-find errors as memory corruption. However, type safety comes at a cost in runtime performance due to the need for dynamic safety checks. -
Runtime Defenses 1 Baggy Bounds Checking
Runtime Defenses Nicholas Carlini 7 September, 2011 1 Baggy Bounds Checking 1.1 Motivation There are large amounts of legacy C programs which can not be rewritten entirely. Since memory must be manually managed in C, and bounds are not checked automatically on pointer dereferences, C often contains many memory-saftey bugs. One common type of bug is buffer overflows, where the length of an array is not checked before copying data in to the array: data is written past the end of the array and on top of whatever happens to be at that memory location. 1.2 Baggy Bounds Checking Baggy bounds checking was proposed as a possible defense which would detect when pointers wrote to a memory location out of bounds. It is a runtime defense, which uses source code instrumentation. At a very high level, baggy bounds checking keeps track of bounds information on allocated objects. When a pointer which indexes in to allocated memory is dereferenced, bounds checking code ensures that the pointer still is indexing the same allocated memory location. If the pointer points out of bounds of that object, the program is halted. Baggy bounds checking uses a buddy allocator to return objects of size 2n, and fills the remaining unused bytes with 0s. A bounds table is created containing the log of the sizes of every allocated object. Using this table allows for single-memory-access lookups, which is much more efficient than other methods which require multiple memory accesses. 1.3 Baggy Bounds Checking Failures Baggy bounds checking does not work in all situations, however. -
Spring 2015 CS 161 Computer Security Optional Notes Memory
CS 161 Optional Notes Spring 2015 Computer Security 1 Memory safety | Attacks and Defenses In the first few lectures we will be looking at software security|problems associated with the software implementation. You may have a perfect design, a perfect specification, perfect algorithms, but still have implementation vulnerabilities. In fact, after configuration errors, implementation errors are probably the largest single class of security errors exploited in practice. We will start by looking at a particularly prevalent class of software flaws, those that concern memory safety. Memory safety refers to ensuring the integrity of a program's data structures: preventing attackers from reading or writing to memory locations other than those intended by the programmer. Because many security-critical applications have been written in C, and because C has peculiar pitfalls of its own, many of these examples will be C-specific. Implementation flaws can in fact occur at all levels: in improper use of the programming language, the libraries, the operating system, or in the application logic. We will look at some of these others later in the course. 1 Buffer overflow vulnerabilities We'll start with one of the most common types of error|buffer overflow (also called buffer overrun) vulnerabilities. Buffer overflow vulnerabilities are a particular risk in C. Since it is an especially widely used systems programming language, you might not be surprised to hear that buffer overflows are one of the most pervasive kind of implementation flaws around. As a low-level language, we can think of C as a portable assembly language. The programmer is exposed to the bare machine (which is one reason that C is such a popular systems language). -
Implicit Array Bounds Checking on 64-Bit Architectures
Implicit Array Bounds Checking on 64-bit Architectures CHRIS BENTLEY, SCOTT A. WATTERSON, DAVID K. LOWENTHAL, and BARRY ROUNTREE The University of Georgia Several programming languages guarantee that array subscripts are checked to ensure they are within the bounds of the array. While this guarantee improves the correctness and security of array- based code, it adds overhead to array references. This has been an obstacle to using higher-level languages, such as Java, for high-performance parallel computing, where the language specification requires that all array accesses must be checked to ensure they are within bounds. This is because, in practice, array-bounds checking in scientific applications may increase execution time by more than a factor of 2. Previous research has explored optimizations to statically eliminate bounds checks, but the dynamic nature of many scientific codes makes this difficult or impossible. Our ap- proach is, instead, to create a compiler and operating system infrastructure that does not generate explicit bounds checks. It instead places arrays inside of Index Confinement Regions (ICRs), which are large, isolated, mostly unmapped virtual memory regions. Any array reference outside of its bounds will cause a protection violation; this provides implicit bounds checking. Our results show that when applying this infrastructure to high-performance computing programs written in Java, the overhead of bounds checking relative to a program with no bounds checks is reduced from an average of 63% to an average of 9%. Categories and Subject Descriptors: D.3.4 [Programming Languages]: Processors— Optimization General Terms: Measurement, Performance Additional Key Words and Phrases: Array-bounds checking, virtual memory, 64-bit architectures 1. -
Ocaml Inside: a Drop-In Replacement for Libtls
OCaml inside: a drop-in replacement for libtls Enguerrand Decorne (speaker), Jeremy Yallop, David Kaloper-Meršinjak University of Cambridge Computer Laboratory Introduction: openssl to libtls to libnqsb-tls case, these are declarations of the functions in the libtls inter- The C programming language pervades systems software. An op- face, which our library exports. The source files contain definitions erating system in the Unix tradition consists of a kernel, written of those functions, which mediate between the libtls interface in C, and a collection of libraries and executables, also written in and the ocaml-tls implementation, and which can be compiled C, which communicate in large part via APIs defined as C types and linked together with ocaml-tls, the OCaml runtime, and the and functions. Systems built in C typically suffer from a number of OCaml code that implements libnqsb-tls to build a shared li- problems, ranging from buffer overflows and other violations that brary. follow inevitably from unrestricted access to memory, to awkward let tls_server ()= APIs that result from an inexpressive type system and a lack of let tls_server= automatic memory management. { error= None; config= None; fd= None; The openssl library, which implements the cryptographic pro- state=‘NotConfigured; linger= None} in Root.create tls_server |> from_voidp tls tocols TLS and SSL, suffers from both these problems. The lack of bounds checking in C led to the notorious Heartbleed bug in let()=I.internal "tls_server" 2014; a study two years earlier found that almost no clients of (void @-> returning(ptr tls)) tls_server openssl use the library correctly, apparently due to its unhelpful interface (Georgiev et al. -
An Efficient and Scalable Platform for Java Source Code Analysis Using Overlaid Graph Representations
Received March 10, 2020, accepted April 7, 2020, date of publication April 13, 2020, date of current version April 29, 2020. Digital Object Identifier 10.1109/ACCESS.2020.2987631 An Efficient and Scalable Platform for Java Source Code Analysis Using Overlaid Graph Representations OSCAR RODRIGUEZ-PRIETO1, ALAN MYCROFT2, AND FRANCISCO ORTIN 1,3 1Department of Computer Science, University of Oviedo, 33007 Oviedo, Spain 2Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 1TN, U.K. 3Department of Computer Science, Cork Institute of Technology, Cork 021, T12 P928 Ireland Corresponding author: Francisco Ortin ([email protected]) This work was supported in part by the Spanish Department of Science, Innovation and Universities under Project RTI2018-099235-B-I00. The work of Oscar Rodriguez-Prieto and Francisco Ortin was supported by the University of Oviedo through its support to official research groups under Grant GR-2011-0040. ABSTRACT Although source code programs are commonly written as textual information, they enclose syntactic and semantic information that is usually represented as graphs. This information is used for many different purposes, such as static program analysis, advanced code search, coding guideline checking, software metrics computation, and extraction of semantic and syntactic information to create predictive models. Most of the existing systems that provide these kinds of services are designed ad hoc for the particular purpose they are aimed at. For this reason, we created ProgQuery, a platform to allow users to write their own Java program analyses in a declarative fashion, using graph representations. We modify the Java compiler to compute seven syntactic and semantic representations, and store them in a Neo4j graph database. -
Practice, Practice, Practice... Secure Programmer!
Practice, Practice, Practice... Secure Programmer! Melissa Dark: Purdue University Stephen Belcher: National Security Agency Ida Ngambeki: Purdue University Matt Bishop: University of California Davis I. Abstract One of the major weaknesses in software today is the failure to practice defensive or secure programming. Most training programs include only a shallow introduction to secure programming, and fail to integrate and emphasize its importance throughout the curriculum. The addition of an ongoing, practical, mentored "clinic" or Secure Programming Clinic (SPC) is one way of addressing this lack without adding significantly to an already stretched curriculum. In order to properly design this clinic, it is important to identify the knowledge, skills and abilities (KSAs) needed to develop effective programmers. This paper describes the results of a Delphi Study undertaken to determine the primary knowledge areas in secure programming. II. Background The current state of software today is generally poor. The quality of the software in most systems does not support the trust placed in that software. Software security failures are common, and the effects range from inconvenience to severe problems. For example, failing to properly handle an error condition made Facebook inaccessible for a few hours [John10]; the iPhone failed to ring in the New Year in 2010 [Bilt11]; a flaw in an election system used to count votes resulted in some votes from a precinct not being counted [Zett08]; a 2010 FDA study of recalled infusion pumps reported that one of the most common reported problems was software defects [FDA10]; and scientists analyzing on-board computers that control automobiles were able to exploit software vulnerabilities to control the car without physical access [CCK+11]. -
A Comprehensive Approach to Array Bounds Check Elimination for Java
A Comprehensive Approach to Array Bounds Check Elimination for Java Feng Qian, Laurie Hendren, and Clark Verbrugge School of Computer Science, McGill University {fqian,hendren,clump}@cs.mcgill.ca Abstract. This paper reports on a comprehensive approach to elimi- nating array bounds checks in Java. Our approach is based upon three analyses. The first analysis is a flow-sensitive intraprocedural analysis called variable constraint analysis (VCA). This analysis builds a small constraint graph for each important point in a method, and then uses the information encoded in the graph to infer the relationship between array index expressions and the bounds of the array. Using VCA as the base analysis, we also show how two further analyses can improve the results of VCA. Array field analysis is applied on each class and pro- vides information about some arrays stored in fields, while rectangular array analysis is an interprocedural analysis to approximate the shape of arrays, and is useful for finding rectangular (non-ragged) arrays. We have implemented all three analyses using the Soot bytecode opti- mization/annotation framework and we transmit the results of the anal- ysis to virtual machines using class file attributes. We have modified the Kaffe JIT, and IBM’s High Performance Compiler for Java (HPCJ) to make use of these attributes, and we demonstrate significant speedups. 1 Introduction The Java programming language is becoming increasingly popular for the im- plementation of a wide variety of application programs, including loop-intensive programs that use arrays. Java compilers translate high-level programs to Java bytecode and this bytecode is either executed by a Java virtual machine (usu- ally including a JIT compiler), or it is compiled by an ahead-of-time compiler to native code.