The serious bugs and security vulnerabilities facilitated by C/C++'s lack of bounds checking are well known, yet C and C++ remain in widespread use. Unfortunately, C's arbitrary pointer arithmetic, conflation of pointers and arrays, and programmer-visible memory layout make retrofitting C/C++ with spatial safety guarantees extremely challenging. Existing approaches suffer from incompleteness, have high runtime overhead, or require non-trivial changes to the C source code. Thus far, these deficiencies have prevented widespread adoption of such techniques.This paper proposes SoftBound, a compile-time transformation for enforcing spatial safety of C. Inspired by HardBound, a previously proposed hardware-assisted approach, SoftBound similarly records base and bound information for every pointer as disjoint metadata. This decoupling enables SoftBound to provide spatial safety without requiring changes to C source code. Unlike HardBound, SoftBound is a software-only approach and performs metadata manipulation only when loading or storing pointer values. A formal proof shows that this is sufficient to provide spatial safety even in the presence of arbitrary casts. SoftBound's full checking mode provides complete spatial violation detection with 67% runtime overhead on average. To further reduce overheads, SoftBound has a store-only checking mode that successfully detects all the security vulnerabilities in a test suite at the cost of only 22% runtime overhead on average.

/pdf/softbound-highly-compatible-and-complete-spatial-memory-4uj78o9vvd.pdf

SoftBound: highly compatible and complete spatial memory safety for c

Computer systems often fail due to many factors such as software bugs or administrator errors. Diagnosing such production run failures is an important but challenging task since it is difficult to reproduce them in house due to various reasons: (1) unavailability of users' inputs and file content due to privacy concerns; (2) difficulty in building the exact same execution environment; and (3) non-determinism of concurrent executions on multi-processors. Therefore, programmers often have to diagnose a production run failure based on logs collected back from customers and the corresponding source code. Such diagnosis requires expert knowledge and is also too time-consuming, tedious to narrow down root causes. To address this problem, we propose a tool, called SherLog, that analyzes source code by leveraging information provided by run-time logs to infer what must or may have happened during the failed production run. It requires neither re-execution of the program nor knowledge on the log's semantics. It infers both control and data value information regarding to the failed execution. We evaluate SherLog with 8 representative real world software failures (6 software bugs and 2 configuration errors) from 7 applications including 3 servers. Information inferred by SherLog are very useful for programmers to diagnose these evaluated failures. Our results also show that SherLog can analyze large server applications such as Apache with thousands of logging messages within only 40 minutes.

/pdf/sherlog-error-diagnosis-by-connecting-clues-from-run-time-ajltw8nbv1.pdf

SherLog: error diagnosis by connecting clues from run-time logs

Inputs causing a program to fail are usually large and often contain information irrelevant to the failure. It thus helps debugging to simplify program inputs. The Delta Debugging algorithm is a general technique applicable to minimizing all failure-inducing inputs for more effective debugging. In this paper, we present HDD, a simple but effective algorithm that significantly speeds up Delta Debugging and increases its output quality on tree structured inputs such as XML. Instead of treating the inputs as one flat atomic list, we apply Delta Debugging to the very structure of the data. In particular, we apply the original Delta Debugging algorithm to each level of a program's input, working from the coarsest to the finest levels. We are thus able to prune the large irrelevant portions of the input early. All the generated input configurations are syntactically valid, reducing the number of inconclusive configurations that need to be tested and accordingly the amount of time spent simplifying. We have implemented HDD and evaluated it on a number of real failure-inducing inputs from the GCC and Mozilla bugzilla databases. Our Hierarchical Delta Debugging algorithm produces simpler outputs and takes orders of magnitude fewer test cases than the original Delta Debugging algorithm. It is able to scale to inputs of considerable size that the original Delta Debugging algorithm cannot process in practice. We argue that HDD is an effective tool for automatic debugging of programs expecting structured inputs.

/pdf/hdd-hierarchical-delta-debugging-qs5r4y8t3e.pdf

HDD: hierarchical delta debugging

Runtime property checking (as implemented in tools like Purify or Valgrind) checks whether a program execution satisfies a property. Active property checking extends runtime checking by checking whether the property is satisfied by all program executions that follow the same program path. This check is performed on a symbolic execution of the given program path using a constraint solver. If the check fails, the constraint solver generates an alternative program input triggering a new program execution that follows the same program path but exhibits a property violation. Combined with systematic dynamic test generation, which attempts to exercise all feasible paths in a program, active property checking defines a new form of dynamic software model checking (program verification). In this paper, we formalize and study active property checking. We show how static and dynamic type checking can be extended with active type checking. Then, we discuss how to implement active property checking efficiently. Finally, we discuss results of experiments with media playing applications on Windows, where active property checking was able to detect several new security-related bugs.

/pdf/active-property-checking-5ga1jwfy6d.pdf

Active property checking

This paper addresses the challenge of sound typestate verification, with acceptable precision, for real-world Java programs. We present a novel framework for verification of typestate properties, including several new techniques to precisely treat aliases without undue performance costs. In particular, we present a flowsensitive, context-sensitive, integrated verifier that utilizes a parametric abstract domain combining typestate and aliasing information.To scale to real programs without compromising precision, we present a staged verification system in which faster verifiers run as early stages which reduce the workload for later, more precise, stages.We have evaluated our framework on a number of real Java programs, checking correct API usage for various Java standard libraries. The results show that our approach scales to hundreds of thousands of lines of code, and verifies correctness for 93% of the potential points of failure.

/pdf/effective-typestate-verification-in-the-presence-of-aliasing-2t8ht2amsd.pdf

Effective typestate verification in the presence of aliasing

Techniques and tools relating to annotating program source code facilitate inferring annotations from source code based at least in part on a description (or specification) generated with a programmable tool. Described techniques and tools provide flexibility in annotation inference across different code bases and program states or properties of interest, and can reduce the overhead of adding annotations to “legacy” source code. For example, a specification is generated with a programmable specification tool that is separate from an inference engine. In the inference engine, one or more annotations for a computer program are inferred based at least in part on the specification.

Programmable annotation inference

In this paper, we present a new algorithm for tracking the flow of values through a program. Our algorithm represents a substantial improvement over the state of the art. Previously described value flow analyses that are control-flow sensitive do not scale well, nor do they eliminate value flow information from infeasible execution paths (i.e., they are path-insensitive). Our algorithm scales to large programs, and it is path-sensitive.The efficiency of our algorithm arises from three insights: The value flow problem can be "bit-vectorized" by tracking the flow of one value at a time; dataflow facts from different execution paths with the same value flow information can be merged; and information about complex aliasing that affects value flow can be plugged in from a different analysis.We have incorporated our analysis in ESP, a software validation tool. We have used ESP to validate the Windows operating system kernel (a million lines of code) against an important security property. This experience suggests that our algorithm scales to large programs, and is accurate enough to trace the flow of values in real code.

Software validation via scalable path-sensitive value flow analysis

We describe an ongoing project, the deployment of a modular checker to statically find and prevent every buffer overflow in future versions of a Microsoft product. Lightweight annotations specify requirements for safely using each buffer, and functions are checked individually to ensure they obey these requirements and do not overflow. Our focus is on the incremental deployment of this technology: by layering the annotation language, using aggressive inference techniques, and slicing warnings by checker confidence, teams must pay only part of the cost of annotating a program to achieve part of the benefit, which provides incentive for further annotation. To date over 400,000 annotations have been added to specify buffer usage in the source code for this product, of which over 150,000 were automatically inferred, and over 3,000 potential buffer overflows have been found and fixed.

/pdf/modular-checking-for-buffer-overflows-in-the-large-dh5pl6p1zc.pdf

Modular checking for buffer overflows in the large

In this paper, we describe PSE (Postmortem Symbolic Evaluation), a static analysis algorithm that can be used by programmers to diagnose software failures. The algorithm requires minimal information about a failure, namely its kind (e.g. NULL dereference), and its location in the program's source code. It produces a set of execution traces along which the program can be driven to the given failure.PSE tracks the flow of a single value of interest from the point in the program where the failure occurred back to the points in the program where the value may have originated. The algorithm combines a novel dataflow analysis and memory alias analysis in a manner that allows for precise exploration of the program's behavior in polynomial time.We have applied PSE to the problem of diagnosing potential NULL-dereference errors in a suite of C programs, including several SPEC benchmarks and a large commercial operating system. In most cases, the analysis is able to either validate a pointer dereference, or find precise error traces demonstrating a NULL value for the pointer, in less than a second.

/pdf/pse-explaining-program-failures-via-postmortem-static-258vc02oan.pdf

PSE: explaining program failures via postmortem static analysis

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Programmable annotation inference

Software validation via scalable path-sensitive value flow analysis

Modular checking for buffer overflows in the large

PSE: explaining program failures via postmortem static analysis