TL;DR: Inspired by HardBound, a previously proposed hardware-assisted approach, SoftBound similarly records base and bound information for every pointer as disjoint metadata, which enables SoftBound to provide spatial safety without requiring changes to C source code.
Abstract: 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.
TL;DR: This work presents a pointer and array access checking technique that provides complete error coverage through a simple set of program transformations, and is the first technique that detects all spatial and temporal access errors.
Abstract: We present a pointer and array access checking technique that provides complete error coverage through a simple set of program transformations. Our technique, based on an extended safe pointer representation, has a number of novel aspects. Foremost, it is the first technique that detects all spatial and temporal access errors. Its use is not limited by the expressiveness of the language; that is, it can be applied successfully to compiled or interpreted languages with subscripted and mutable pointers, local references, and explicit and typeless dynamic storage management, e.g., C. Because it is a source level transformation, it is amenable to both compile- and run-time optimization. Finally, its performance, even without compile-time optimization, is quite good. We implemented a prototype translator for the C language and analyzed the checking overheads of six non-trivial, pointer intensive programs. Execution overheads range from 130% to 540%; with text and data size overheads typically below 100%.
TL;DR: A backwards compatible bounds checking technique that substantially reduces performance overhead and is more than two times faster than the fastest previous technique and about five times faster--using less memory--than recording object bounds using a splay tree.
Abstract: Attacks that exploit out-of-bounds errors in C and C++ programs are still prevalent despite many years of research on bounds checking. Previous backwards compatible bounds checking techniques, which can be applied to unmodified C and C++ programs, maintain a data structure with the bounds for each allocated object and perform lookups in this data structure to check if pointers remain within bounds. This data structure can grow large and the lookups are expensive.
In this paper we present a backwards compatible bounds checking technique that substantially reduces performance overhead. The key insight is to constrain the sizes of allocated memory regions and their alignment to enable efficient bounds lookups and hence efficient bounds checks at runtime. Our technique has low overhead in practice--only 8% throughput decrease for Apache-- and is more than two times faster than the fastest previous technique and about five times faster--using less memory--than recording object bounds using a splay tree.
TL;DR: ABCD is a light-weight algorithm for elimination of Array Bounds Checks on Demand that can be applied to a set of frequently executed (hot) bounds checks, which makes it suitable for the dynamic-compilation setting, in which compile-time cost is constrained but hot statements are known.
Abstract: To guarantee typesafe execution, Java and other strongly typed languages require bounds checking of array accesses. Because array-bounds checks may raise exceptions, they block code motion of instructions with side effects, thus preventing many useful code optimizations, such as partial redundancy elimination or instruction scheduling of memory operations. Furthermore, because it is not expressible at bytecode level, the elimination of bounds checks can only be performed at run time, after the bytecode program is loaded. Using existing powerful bounds-check optimizers at run time is not feasible, however, because they are too heavyweight for the dynamic compilation setting.ABCD is a light-weight algorithm for elimination of Array Bounds Checks on Demand. Its design emphasizes simplicity and efficiency. In essence, ABCD works by adding a few edges to the SSA value graph and performing a simple traversal of the graph. Despite its simplicity, ABCD is surprisingly powerful. On our benchmarks, ABCD removes on average 45% of dynamic bound check instructions, sometimes achieving near-ideal optimization. The efficiency of ABCD stems from two factors. First, ABCD works on a sparse representation. As a result, it requires on average fewer than 10 simple analysis steps per bounds check. Second, ABCD is demand-driven. It can be applied to a set of frequently executed (hot) bounds checks, which makes it suitable for the dynamic-compilation setting, in which compile-time cost is constrained but hot statements are known.
TL;DR: The structure of a Java JIT compiler for the Intel Architecture is presented, the lightweight implementation of JIT compilation optimizations are described, and the performance benefits and tradeoffs of the optimizations are evaluated.
Abstract: A "Just-In-Time" (JIT) Java compiler produces native code from Java byte code instructions during program execution. As such, compilation speed is more important in a Java JIT compiler than in a traditional compiler, requiring optimization algorithms to be lightweight and effective. We present the structure of a Java JIT compiler for the Intel Architecture, describe the lightweight implementation of JIT compiler optimizations (e.g., common subexpression elimination, register allocation, and elimination of array bounds checking), and evaluate the performance benefits and tradeoffs of the optimizations. This JIT compiler has been shipped with version 2.5 of Intel's VTune for Java product.