Automated static dataflow analysis is an effective technique for detecting security critical issues like sensitive data leak, and vulnerability to injection attacks. Ensuring high precision and recall requires an analysis that is context, field and object sensitive. However, it is challenging to attain high precision and recall and scale to large industrial code bases. Compositional style analyses in which individual software components are analyzed separately, independent from their usage contexts, compute reusable summaries of components. This is an essential feature when deploying such analyses in CI/CD at code-review time or when scanning deployed container images. In both these settings the majority of software components stay the same between subsequent scans. However, it is not obvious how to extend such analyses to check the kind of contextual taint specifications that arise in practice, while maintaining compositionality. In this work we present contextual dataflow modeling, an extension to the compositional analysis to check complex taint specifications and significantly increasing recall and precision. Furthermore, we show how such high-fidelity analysis can scale in production using three key optimizations: (i) discarding intermediate results for previously-analyzed components, an optimization exploiting the compositional nature of our analysis; (ii) a scope-reduction analysis to reduce the scope of the taint analysis w.r.t. the taint specifications being checked, and (iii) caching of analysis models. We show a 9.85% reduction in false positive rate on a comprehensive test suite comprising the OWASP open-source benchmarks as well as internal real-world code samples. We measure the performance and scalability impact of each individual optimization using open source JVM packages from the Maven central repository and internal AWS service codebases. This combination of high precision, recall, performance, and scalability has allowed us to enforce security policies at scale both internally within Amazon as well as for external customers.

We present RAPID, an industrial-strength analysis developed at AWS that aims to help developers by providing automatic, fast and actionable feedback about correct usage of cloud-service APIs. RAPID's design is based on the insight that cloud service APIs are structured around short-lived request- and response-objects whose usage patterns can be specified as value-dependent type-state automata and be verified by combining local type-state with global value-flow analyses. We describe various challenges that arose to deploy RAPID at scale. Finally, we present an evaluation that validates our design choices, deployment heuristics, and shows that RAPID is able to quickly and precisely report a wide variety of useful API misuse violations in large, industrial-strength code bases.

While smart contracts have the potential to revolutionize many important applications like banking, trade, and supply-chain, their reliable deployment begs for rigorous formal verification. Since most smart contracts are not annotated with formal specifications, general verification of functional properties is impeded. In this work, we propose an automated approach to verify unannotated smart contracts against specifications ascribed to a few manually-annotated contracts. In particular, we propose a notion of behavioral refinement, which implies inheritance of functional properties. Furthermore, we propose an automated approach to inductive proof, by synthesizing simulation relations on the states of related contracts. Empirically, we demonstrate that behavioral simulations can be synthesized automatically for several ubiquitous classes like tokens, auctions, and escrow, thus enabling the verification of unannotated contracts against functional specifications.

Testing-based methodologies like fuzzing are able to analyze complex software which is not amenable to traditional formal approaches like verification, model checking, and abstract interpretation. Despite enormous success at exposing countless security vulnerabilities in many popular software projects, applications of testing-based approaches have mainly targeted checking traditional safety properties like memory safety. While unquestionably important, this class of properties does not precisely characterize other important security aspects such as information leakage, e.g., through side channels. In this work we extend testing-based software analysis methodologies to two-safety properties, which enables the precise discovery of information leaks in complex software. In particular, we present the ct-fuzz tool, which lends coverage-guided greybox fuzzers the ability to detect two-safety property violations. Our approach is capable of exposing violations to any two-safety property expressed as equality between two program traces. Empirically, we demonstrate that ct-fuzz swiftly reveals timing leaks in popular cryptographic implementations.

Multithreaded programs generally leverage efficient and thread-safe concurrent objects like sets, key-value maps, and queues. While some concurrent-object operations are designed to behave atomically, each witnessing the atomic effects of predecessors in a linearization order, others forego such strong consistency to avoid complex control and synchronization bottlenecks. For example, contains (value) methods of key-value maps may iterate through key-value entries without blocking concurrent updates, to avoid unwanted performance bottlenecks, and consequently overlook the effects of some linearization-order predecessors. While such weakly-consistent operations may not be atomic, they still offer guarantees, e.g., only observing values that have been present. In this work we develop a methodology for proving that concurrent object implementations adhere to weak-consistency specifications. In particular, we consider (forward) simulation-based proofs of implementations against relaxed-visibility specifications, which allow designated operations to overlook some of their linearization-order predecessors, i.e., behaving as if they never occurred. Besides annotating implementation code to identify linearization points, i.e., points at which operations' logical effects occur, we also annotate code to identify visible operations, i.e., operations whose effects are observed; in practice this annotation can be done automatically by tracking the writers to each accessed memory location. We formalize our methodology over a general notion of transition systems, agnostic to any particular programming language or memory model, and demonstrate its application, using automated theorem provers, by verifying models of Java concurrent object implementations.

Effective software specifications enable modular reasoning, allowing clients to establish program properties without knowing the details of module implementations. While some modules’ operations behave atomically, others admit weaker consistencies to increase performance. Consequently, since current methodologies do not capture the guarantees provided by operations of varying non-atomic consistencies, specifications are ineffective, forfeiting the ability to establish properties of programs that invoke non-atomic operations. In this work we develop a methodology for specifying software modules whose operations satisfy multiple distinct consistency levels. In particular, we develop a simple annotation language for specifying weakly-consistent operations via visibility relaxation, wherein annotations impose varying constraints on the visibility among operations. To integrate with modern software platforms, we identify a novel characterization of consistency called sequential happens-before consistency, which admits effective validation. Empirically, we demonstrate the efficacy of our approach by deriving and validating relaxed-visibility specifications for Java concurrent objects. Furthermore, we demonstrate an optimality of our annotation language, empirically, by establishing that even finer-grained languages do not capture stronger specifications for Java objects.

Recent distributed systems have introduced variations of familiar abstract data types (ADTs) like counters, registers, flags, and sets, that provide high availability and partition tolerance. These conflict-free replicated data types (CRDTs) utilize mechanisms to resolve the effects of concurrent updates to replicated data. Naturally these objects weaken their consistency guarantees to achieve availability and partition-tolerance, and various notions of weak consistency capture those guarantees. In this work we study the tractability of CRDT-consistency checking. To capture guarantees precisely, and facilitate symbolic reasoning, we propose novel logical characterizations. By developing novel reductions from propositional satisfiability problems, and novel consistency-checking algorithms, we discover both positive and negative results. In particular, we show intractability for replicated flags, sets, counters, and registers, yet tractability for replicated growable arrays. Furthermore, we demonstrate that tractability can be redeemed for registers when each value is written at most once, for counters when the number of replicas is fixed, and for sets and flags when the number of replicas and variables is fixed.

High-performance multithreaded software often relies on optimized implementations of common abstract data types (ADTs) like counters, key-value stores, and queues, i.e., concurrent objects. By using fine-grained and non-blocking mechanisms for efficient inter-thread synchronization, these implementations are vulnerable to violations of ADT-consistency which are difficult to detect: bugs can depend on specific combinations of method invocations and argument values, as well as rarely-occurring thread interleavings. Even given a bug-triggering interleaving, detection generally requires unintuitive test assertions to capture inconsistent combinations of invocation return values. In this work we describe the Violat tool for generating tests that witness violations to atomicity, or weaker consistency properties. Violat generates self-contained and efficient programs that test observational refinement, i.e., substitutability of a given ADT with a given implementation. Our approach is both sound and complete in the limit: for every consistency violation there is a failed execution of some test program, and every failed test signals an actual consistency violation. In practice we compromise soundness for efficiency via random exploration of test programs, yielding probabilistic soundness instead. Violat's tests reliably expose ADT-consistency violations using off-the-shelf approaches to concurrent test validation, including stress testing and explicit-state model checking.

Continued advances in IoT technology have prompted new investigation into its usage for military operations, both to augment and complement existing military sensing assets and support next-generation artificial intelligence and machine learning systems. Under the emerging Internet of Battlefield Things (IoBT) paradigm, current operational conditions necessitate the development of novel security techniques, centered on establishment of trust for individual assets and supporting resilience of broader systems. To advance current IoBT efforts, a collection of prior-developed cybersecurity techniques is reviewed for applicability to conditions presented by IoBT operational environments (e.g., diverse asset ownership, degraded networking infrastructure, adversary activities) through use of supporting case study examples. The research techniques covered focus on two themes: (1) Supporting trust assessment for known/unknown IoT assets; (2) ensuring continued trust of known IoT assets and IoBT systems.

Timing-based side-channel attacks are a serious security risk for modern cryptosystems. The time-balancing countermeasure used by several TLS implementations (e.g. s2n, GnuTLS) ensures that execution timing is negligibly influenced by secrets, and hence no attacker-observable timing behavior depends on secrets. These implementations can be difficult to validate, since time-balancing countermeasures depend on global properties across multiple executions. In this work we introduce the tool SIDETRAIL, which we use to prove the correctness of time-balancing countermeasures in s2n, the open-source TLS implementation used across a range of products from AWS, including S3. SIDETRAIL is used in s2n's continuous integration process, and has detected three side-channel issues that the s2n team confirmed and repaired before the affected code was deployed to production systems.

High-performance implementations of distributed and multicore shared objects often guarantee only the weak consistency of their concurrent operations, foregoing the de-facto yet performance-restrictive consistency criterion of linearizability. While such weak consistency is often vital for achieving performance requirements, practical automation for checking weak-consistency is lacking. In principle, algorithmically checking the consistency of executions according to various weak-consistency criteria is hard: in addition to the enumeration of linearizations of an execution's operations, such criteria generally demand the enumeration of possible visibility relations among the linearized operations; a priori, both enumerations are exponential. In this work we identify an optimization to weak-consistency checking: rather than enumerating every possible visibility relation, it suffices to consider only the minimal visibility relations which adhere to the various constraints of the given criterion, for a significant class of consistency criteria. We demonstrate the soundness of this optimization, and describe an associated minimal-visibility consistency checking algorithm. Empirically, we show that our algorithm significantly outperforms the baseline weak-consistency checking algorithm, which naïvely enumerates all visibilities, and adds only modest overhead to the baseline linearizability checking algorithm, which does not enumerate visibilities.

Multithreaded software is typically built with specialized concurrent objects like atomic integers, queues, and maps. These objects' methods are designed to behave according to certain consistency criteria like atomicity, despite being optimized to avoid blocking and exploit parallelism, e.g., by using atomic machine instructions like compare and exchange (cmpxchg). Exposing atomicity violations is important since they generally lead to elusive bugs that are difficult to identify, reproduce, and ultimately repair. In this work we expose atomicity violations in concurrent object implementations from the most widely-used software development kit: The Java Development Kit (JDK). We witness atomicity violations via simple test harnesses containing few concurrent method invocations. While stress testing is effective at exposing violations given catalytic test harnesses and lightweight means of falsifying atomicity, divining effectual catalysts can be difficult, and atomicity checks are generally cumbersome. We overcome these problems by automating test-harness search, and establishing atomicity via membership in precomputed sets of acceptable return-value outcomes. Our approach enables testing millions of executions of each harness each second (per processor core). This scale is important since atomicity violations are observed in very few executions (tens to hundreds out of millions) of very few harnesses (one out of hundreds to thousands). Our implementation is open source and publicly available.

While many program properties like the validity of assertions and in-bounds array accesses admit nearly-trivial monitoring algorithms, the standard correctness criterion for concurrent data structures does not. Given an implementation of an arbitrary abstract data type, checking whether the operations invoked in one single concurrent execution are linearizable, i.e., indistinguishable from an execution where the same operations are invoked atomically, requires exponential time in the number of operations. In this work we identify a class of collection abstract data types which admit polynomial-time linearizability monitors. Collections capture the majority of concurrent data structures available in practice, including stacks, queues, sets, and maps. Although monitoring executions of arbitrary abstract data types requires enumerating exponentially-many possible linearizations, collections enjoy combinatorial properties which avoid the enumeration. We leverage these properties to reduce linearizability to Horn satisfiability. As far as we know, ours is the first sound, complete, and tractable algorithm for monitoring linearizability for types beyond single-value registers.

Linearizability is the standard correctness criterion for concurrent data structures such as stacks and queues. It allows to establish observational refinement between a concurrent implementation and an atomic reference implementation. Proving linearizability requires identifying linearization points for each method invocation along all possible computations, leading to valid sequential executions, or alternatively, establishing forward and backward simulations. In both cases, carrying out proofs is hard and complex in general. In particular, backward reasoning is difficult in the context of programs with data structures, and strategies for identifying statically linearization points cannot be defined for all existing implementations. In this paper, we show that, contrary to common belief, many such complex implementations, including, e.g., the Herlihy and Wing Queue and the Time-Stamped Stack, can be proved correct using only forward simulation arguments. This leads to simple and natural correctness proofs for these implementations that are amenable to automation.

We define a correctness criterion, called robustness against concurrency, for a class of event-driven asynchronous programs that are at the basis of modern UI frameworks in Android, iOS, and Javascript. A program is robust when all possible behaviors admitted by the program under arbitrary procedure and event interleavings are admitted even if asynchronous procedures respectively, events are assumed to execute serially, one after the other, accessing shared memory in isolation. We characterize robustness as a conjunction of two correctness criteria: event-serializability i.e., events can be seen as atomic and event-determinism executions within each event are insensitive to the interleavings between concurrent tasks dynamically spawned by the event. Then, we provide efficient algorithms for checking these two criteria based on polynomial reductions to reachability problems in sequential programs. This result is surprising because it allows to avoid explicit handling of all concurrent executions in the analysis, which leads to an important gain in complexity. We demonstrate via case studies on Android apps that the typical mistakes programmers make are captured as robustness violations, and that violations can be detected efficiently using our approach.

Formal specification is a vital ingredient to scalable verification of software systems. In the case of efficient implementations of concurrent objects like atomic registers, queues, and locks, symbolic formal representations of their abstract data types (ADTs) enable efficient modular reasoning, decoupling clients from implementations. Writing adequate formal specifications, however, is a complex task requiring rare expertise. In practice, programmers write reference implementations as informal specifications. In this work we demonstrate that effective symbolic ADT representations can be automatically generated from the executions of reference implementations. Our approach exploits two key features of naturally-occurring ADTs: violations can be decomposed into a small set of representative patterns, and these patterns manifest in executions with few operations. By identifying certain algebraic properties of naturally-occurring ADTs, and exhaustively sampling executions up to a small number of operations, we generate concise symbolic ADT representations which are complete in practice, enabling the application of efficient symbolic verification algorithms without the burden of manual specification. Furthermore, the concise ADT violation patterns we generate are human-readable, and can serve as useful, formal documentation.

Tool prototyping is an essential step in developing novel software verification algorithms and techniques. However, implementing a verifier prototype that can handle real-world programs is a huge endeavor, which hinders researchers by forcing them to spend more time engineering tools, and less time innovating. In this paper, we present the SMACK software verification toolchain. The toolchain provides a modular and extensible software verification ecosystem that decouples the front-end source language details from back-end verification algorithms. It achieves that by translating from the LLVM compiler intermediate representation into the Boogie intermediate verification language. SMACK benefits the software verification community in several ways: (i) it can be used as an off-the-shelf software verifier in an applied software verification project, (ii) it enables researchers to rapidly develop and release new verification algorithms, (iii) it allows for adding support for new languages in its front-end. We have used SMACK to verify numerous C/C++ programs, including industry examples, showing it is mature and competitive. Likewise, SMACK is already being used in several existing verification research prototypes. Our demonstration of SMACK can be found on YouTube at the following address: https://youtu.be/SPPSC1KdRzs

The constant-time programming discipline is an effective countermeasure against timing attacks, which can lead to complete breaks of otherwise secure systems. However, adhering to constant-time programming is hard on its own, and extremely hard under additional efficiency and legacy constraints. This makes automated verification of constant-time code an essential component for building secure software.We propose a novel approach for verifying constant-time security of real-world code. Our approach is able to validate implementations that locally and intentionally violate the constant-time policy, when such violations are benign and leak no more information than the public outputs of the computation. Such implementations, which are used in cryptographic libraries to obtain important speedups or to comply with legacy APIs, would be declared insecure by all prior solutions.We implement our approach in a publicly available, cross-platform, and fully automated prototype, ct-verif, that leverages the SMACK and Boogie tools and verifies optimized LLVM implementations. We present verification results obtained over a wide range of constant-time components from the NaCl, OpenSSL, FourQ and other off-the-shelf libraries. The diversity and scale of our examples, as well as the fact that we deal with top-level APIs rather than being limited to low-level leaf functions, distinguishes ct-verif from prior tools.Our approach is based on a simple reduction of constant-time security of a program P to safety of a product program Q that simulates two executions of P. We formalize and verify the reduction for a core high-level language using the Coq proof assistant.