Ranjit Jhala

Adithya Murali, Lucas Peña, Ranjit Jhala et al.

Several practical tools for automatically verifying functional programs (e.g., Liquid Haskell and Leon for Scala programs) rely on a heuristic based on unrolling recursive function definitions followed by quantifier-free reasoning using SMT solvers. We uncover foundational theoretical properties of this heuristic, revealing that it can be generalized and formalized as a technique that is in fact complete for reasoning with combined First-Order theories of algebraic datatypes and background theories, where background theories support decidable quantifier-free reasoning. The theory developed in this paper explains the efficacy of these heuristics when they succeed, explains why they fail when they fail, and the precise role that user help plays in making proofs succeed.

Luca de Alfaro, Mathis Aubert, Ranjit Jhala et al.

Jupyter Notebooks have become widely adopted in data science, as they allow the sharing of reproducible computational analysis. They are, however, accessible only to people who understand computer code. To reach the broader audience of scientists interested in data analysis and computation, but unfamiliar with code, we introduce Plainbook, notebooks centered on natural language rather than code. Plainbook is based on two principles: promote the natural language descriptions, and verify the values. In plainbook, the natural language descriptions are preserved, rather than the resulting code; the code is generated automatically from the cell descriptions. As natural language is read top to bottom, Plainbook adopts a linear execution semantics, in which cells are guaranteed to be executed in the order in which they appear; there is no "hidden state" or out-of-order execution as in Jupyter. To allow users who may not understand code to verify the correctness of the computation, we have built into Plainbook verification mechanisms centered on values and value inspection. These include mechanisms that focus on individual cells, akin to unit tests, as well as global mechanisms. Both the linear execution semantics, and the verification mechanisms, are underpinned by a snapshot kernel that caches execution states and makes execution and verification efficient.

Klaus v. Gleissenthall, Rami Gökhan Kıcı, Deian Stefan et al.

To be secure, cryptographic algorithms crucially rely on the underlying hardware to avoid inadvertent leakage of secrets through timing side channels. Unfortunately, such timing channels are ubiquitous in modern hardware, due to its labyrinthine fast-paths and optimizations. A promising way to avoid timing vulnerabilities is to devise --- and verify --- conditions under which a hardware design is free of timing variability, i.e., executes in constant-time. In this paper, we present Iodine: a clock precise, constant-time approach to eliminating timing side channels in hardware. Iodine succeeds in verifying various open source hardware designs in seconds and with little developer effort. Iodine also discovered two constant-time violations: one in a floating-point unit and another one in an RSA encryption module.

Byron Cook, Remi Delmas, Zyad Hassan et al.

Rust's type system prevents many classes of memory errors, yet its standard library relies heavily on unsafe code whose correctness is validated through testing, including dynamic checks under Miri, but lacks static verification. We present what is, to the best of our knowledge, the largest verification campaign reported for a software library: an open, crowdsourced effort that integrates complementary verification tools into the continuous integration of a verification repository forked from the Rust standard library. We analyze the campaign's effectiveness, discuss the practical value of machine-checked proofs for a subset of undefined behaviors (e.g., out-of-bounds access, null and dangling pointer dereferences, and use of uninitialized memory), and frame the remaining obstacles as open challenges for the formal-methods community.

Matthew Kolosick, Shravan Narayan, Evan Johnson et al.

Software sandboxing or software-based fault isolation (SFI) is a lightweight approach to building secure systems out of untrusted components. Mozilla, for example, uses SFI to harden the Firefox browser by sandboxing third-party libraries, and companies like Fastly and Cloudflare use SFI to safely co-locate untrusted tenants on their edge clouds. While there have been significant efforts to optimize and verify SFI enforcement, context switching in SFI systems remains largely unexplored: almost all SFI systems use \emph{heavyweight transitions} that are not only error-prone but incur significant performance overhead from saving, clearing, and restoring registers when context switching. We identify a set of \emph{zero-cost conditions} that characterize when sandboxed code has sufficient structured to guarantee security via lightweight \emph{zero-cost} transitions (simple function calls). We modify the Lucet Wasm compiler and its runtime to use zero-cost transitions, eliminating the undue performance tax on systems that rely on Lucet for sandboxing (e.g., we speed up image and font rendering in Firefox by up to 29.7\% and 10\% respectively). To remove the Lucet compiler and its correct implementation of the Wasm specification from the trusted computing base, we (1) develop a \emph{static binary verifier}, VeriZero, which (in seconds) checks that binaries produced by Lucet satisfy our zero-cost conditions, and (2) prove the soundness of VeriZero by developing a logical relation that captures when a compiled Wasm function is semantically well-behaved with respect to our zero-cost conditions. Finally, we show that our model is useful beyond Wasm by describing a new, purpose-built SFI system, SegmentZero32, that uses x86 segmentation and LLVM with mostly off-the-shelf passes to enforce our zero-cost conditions; our prototype performs on-par with the state-of-the-art Native Client SFI system.

Rami Gokhan Kici, Klaus v. Gleissenthall, Deian Stefan et al.

We present Xenon, a solver-aided method for formally verifying that Verilog hardware executes in constant-time. Xenon scales to realistic hardware designs by drastically reducing the effort needed to localize the root cause of verification failures via a new notion of constant-time counterexamples, which Xenon uses to automatically synthesize a minimal set of secrecy assumptions. Xenon further exploits modularity in Verilog code via a notion of module summaries, thereby avoiding duplicate work across multiple module instantiations. We show how Xenon's assumption synthesis and summaries enable the verification of a variety of circuits including AES, a highly modular AES-256 implementation where modularity cuts verification from six hours to under three seconds, and ScarV, a timing channel hardened RISC-V micro-controller whose size exceeds previously verified designs by an order of magnitude.

Niki Vazou, Alexander Bakst, Ranjit Jhala

We present a notion of bounded quantification for refinement types and show how it expands the expressiveness of refinement typing by using it to develop typed combinators for: (1) relational algebra and safe database access, (2) Floyd-Hoare logic within a state transformer monad equipped with combinators for branching and looping, and (3) using the above to implement a refined IO monad that tracks capabilities and resource usage. This leap in expressiveness comes via a translation to "ghost" functions, which lets us retain the automated and decidable SMT based checking and inference that makes refinement typing effective in practice.

Krishnendu Chatterjee, Thomas A. Henzinger, Ranjit Jhala et al.

Planning in adversarial and uncertain environments can be modeled as the problem of devising strategies in stochastic perfect information games. These games are generalizations of Markov decision processes (MDPs): there are two (adversarial) players, and a source of randomness. The main practical obstacle to computing winning strategies in such games is the size of the state space. In practice therefore, one typically works with abstractions of the model. The diffculty is to come up with an abstraction that is neither too coarse to remove all winning strategies (plans), nor too fine to be intractable. In verification, the paradigm of counterexample-guided abstraction refinement has been successful to construct useful but parsimonious abstractions automatically. We extend this paradigm to probabilistic models (namely, perfect information games and, as a special case, MDPs). This allows us to apply the counterexample-guided abstraction paradigm to the AI planning problem. As special cases, we get planning algorithms for MDPs and deterministic systems that automatically construct system abstractions.