arXiv subjects

Kevin Cheang

Publications and source records attributed to Kevin Cheang.

6 recordsLinked to original sources

arXiv2026

Learning Context-Free Grammars for Grammar-Constrained Decoding via Declarative Agentic Programming with Guarantees

Language models (LMs) are increasingly used to interact with external services via programs written in domain-specific languages (DSLs). Unfortunately, since DSLs are often low-resource and esoteric, LMs frequently produce syntactically invalid programs in these languages. Grammar-constrained decoding can eliminate such failures, but requires syntactic constraints. These are usually in the form of a context-free grammar for the target language, an artifact that is hard to come by for third-party DSLs. In this work, we define an agent, called Autogrammar, that automatically learns context-free grammars from documentation and execution data. Autogrammar is formalized as a Kripke structure whose nondeterministic choices are resolved by a language model, enabling declarative control of agent behavior via linear temporal logic constraints. We evaluate four versions of Autogrammar on three DSLs (i.e., Amazon CloudWatch Logs Insights, Dynatrace Query Language, and Datadog Search Syntax) and find that it generates grammars that achieve near perfect precision on unseen data; that temporal restrictions reduce execution time by 3.8x without incurring statistically-significant loss in precision; that execution data is crucial while documentation is dispensable; and that grammar-constrained decoding using Autogrammar-generated grammars significantly improves end-to-end LM performance on eight out of ten real tasks, matching or exceeding the performance of a professionally-maintained grammar. In comparison, the context-free grammars generated by existing LM baselines and a state-of-the-art formal technique perform significantly worse over the same evaluation.

cs.PL↗

arXiv2025

Learning to Triage Vulnerability Reports from Program Analysis: An Empirical Study in Node.js

Program analysis tools often produce large volumes of candidate vulnerability reports that require costly manual review, creating a practical challenge: how can security analysts prioritize the reports most likely to be true vulnerabilities? This paper investigates whether machine learning can be applied to prioritizing vulnerabilities reported by program analysis tools. We focus on Node$.$js packages and collect a benchmark of 1,883 Node$.$js packages, each containing one reported ACE or ACI vulnerability. We evaluate a variety of machine learning approaches, including classical models, graph neural networks (GNNs), large language models (LLMs), and hybrid models that combine GNNs and LLMs, trained on data derived from program analysis tool outputs (NodeMedic-FINE in our case study). The top LLM achieves $F_{1}{=}0.915$, while the best provenance-graph-based method achieves $F_{1}{=}0.904$. On the reports that the upstream tool flags but cannot automatically confirm, at a target of recovering 90% of the exploitable reports, the leading model eliminates 75% of the benign reports from manual review. If the best model is tuned to operate at a precision level of 0.8 (i.e., allowing 20% false positives among all warnings), our approach can report 99.2% of exploitable taint flows while missing only 0.8%, demonstrating strong potential for real-world vulnerability triage.

cs.CR↗

arXiv2022

Verifying RISC-V Physical Memory Protection

We formally verify an open-source hardware implementation of physical memory protection (PMP) in RISC-V, which is a standard feature used for memory isolation in security critical systems such as the Keystone trusted execution environment. PMP provides per-hardware-thread machine-mode control registers that specify the access privileges for physical memory regions. We first formalize the functional property of the PMP rules based on the RISC-V ISA manual. Then, we use the LIME tool to translate an open-source implementation of the PMP hardware module written in Chisel to the UCLID5 formal verification language. We encode the formal specification in UCLID5 and verify the functional correctness of the hardware. This is an initial effort towards verifying the Keystone framework, where the trusted computing base (TCB) relies on PMP to provide security guarantees such as integrity and confidentiality.

cs.CR↗

arXiv2022

Cerberus: A Formal Approach to Secure and Efficient Enclave Memory Sharing

Hardware enclaves rely on a disjoint memory model, which maps each physical address to an enclave to achieve strong memory isolation. However, this severely limits the performance and programmability of enclave programs. While some prior work proposes enclave memory sharing, it does not provide a formal model or verification of their designs. This paper presents Cerberus, a formal approach to secure and efficient enclave memory sharing. To reduce the burden of formal verification, we compare different sharing models and choose a simple yet powerful sharing model. Based on the sharing model, Cerberus extends an enclave platform such that enclave memory can be made immutable and shareable across multiple enclaves via additional operations. We use incremental verification starting with an existing formal model called the Trusted Abstract Platform (TAP). Using our extended TAP model, we formally verify that Cerberus does not break or weaken the security guarantees of the enclaves despite allowing memory sharing. More specifically, we prove the Secure Remote Execution (SRE) property on our formal model. Finally, the paper shows the feasibility of Cerberus by implementing it in an existing enclave platform, RISC-V Keystone.

cs.CR↗

arXiv2022

UCLID5: Multi-Modal Formal Modeling, Verification, and Synthesis

UCLID5 is a tool for the multi-modal formal modeling, verification, and synthesis of systems. It enables one to tackle verification problems for heterogeneous systems such as combinations of hardware and software, or those that have multiple, varied specifications, or systems that require hybrid modes of modeling. A novel aspect of \uclid is an emphasis on the use of syntax-guided and inductive synthesis to automate steps in modeling and verification. This tool paper presents new developments in the \uclid tool including new language features, integration with new techniques for syntax-guided synthesis and satisfiability solving, support for hyperproperties and combinations of axiomatic and operational modeling, demonstrations on new problem classes, and a robust implementation.

cs.LO↗

arXiv2020

Synthesis in Uclid5

We describe an integration of program synthesis into Uclid5, a formal modelling and verification tool. To the best of our knowledge, the new version of Uclid5 is the only tool that supports program synthesis with bounded model checking, k-induction, sequential program verification, and hyperproperty verification. We use the integration to generate 25 program synthesis benchmarks with simple, known solutions that are out of reach of current synthesis engines, and we release the benchmarks to the community.

cs.PL↗