arXiv subjects

Yuecheng Wang

Publications and source records attributed to Yuecheng Wang.

4 recordsLinked to original sources

arXiv2026

CHERI-D Reincarnate: efficient multicore CHERI temporal memory safety through allocation reincarnation (draft version)

We propose CHERI-D Reincarnate (Reinc), an architectural extension to CHERI for scalable and efficient temporal memory safety. Prior work CHERI-D has a finite-width generation ID stored at a fixed location, requiring an object to be quarantined when its ID is exhausted. Reinc further provides use-after-free mitigation while permitting immediate freed memory reuse for objects through allocation reincarnation: rather than quarantining an allocation slot upon ID exhaustion, Reinc dynamically assigns a new ID to that slot when its current ID is exhausted. Exhausted IDs are quarantined and later reclaimed, while the underlying memory remains available for immediate reuse. By quarantining IDs rather than memory, Reinc enables continuous reuse of memory in the common case, substantially reducing both memory-sweep frequency and quarantine memory overhead. Reinc further introduces coherent ID caching while retaining a fully decentralized ID organization. Temporal metadata remains colocated with the memory it protects, preserving locality while avoiding centralized metadata structures. To support multicore execution, Reinc connects physical coherence events to the virtually addressed ObjID buffer using lightweight reverse-map and filter-based mechanisms. We implement Reinc as a hardware-software co-design spanning CHERI-Toooba (superscalar FPGA softcore), QEMU, LLVM/Clang and CheriBSD. Across our evaluated workloads, Reinc substantially reduces memory-sweep frequency and memory quarantine while incurring low performance and hardware overhead.

cs.AR↗

arXiv2026

CHERI-D: Secure and efficient inline object ID for CHERI temporal memory safety

We propose CHERI-D, an architectural extension to CHERI that supports efficient temporal memory safety. Efficient memory safety is an increasing priority for programming languages, operating systems, and hardware designs, and CHERI is a leading hardware/software system that provides native spatial safety and a foundation for temporal memory safety. Due to CHERI lacking intrinsic architectural support for temporal memory safety, the state-of-the-art CHERI temporal safety solution, Cornucopia Reloaded, is a software-based solution that provides use-after-reallocation (UAR) protections instead of the stronger use-after-free (UAF) mitigation, and suffers performance overhead due to delayed reallocation and revocation. CHERI-D associates object identification (ID) metadata with capability pointers to provide temporal integrity of allocations. CHERI spatial safety allows CHERI-D to store object IDs safely inline with allocation data, potentially within unused fragmentation. Evaluated in simulation and in hardware, CHERI-D significantly reduces the revocation overhead of Cornucopia Reloaded while allowing it to support strict use-after-free mitigation.

cs.AR↗

arXiv2026

PoisonCap: Efficient Hierarchical Temporal Safety for CHERI

In this paper, we present PoisonCap: scalable temporal safety with strict use-after-free protection and initialisation safety for CHERI systems. Efficient memory safety is an increasing priority for programming languages, operating systems, and hardware designs, and CHERI is a leading hardware/software system that provides native spatial safety and a foundation for temporal memory safety. Cornucopia Reloaded, the current state-of-the-art CHERI temporal safety solution, provides use-after-reallocation safety instead of stronger use-after-free safety, and is not able to enforce initialisation safety. We show that a new 'poison' capability format can be used to enforce strict use-after-free and initialisation safety, and also to communicate memory state to the microarchitecture for efficient cache management of quarantined memory. We enable elegant delegation of memory poisoning privilege using capability bounds to allow nested allocators to enforce safety on their consumers without disturbing upstream allocators. PoisonCap can replace the Cornucopia shadow bitmap, and also automatically zeros memory on reallocation, or optionally traps on read-before-write to enforce initialisation safety. As a result, it incurs no fundamental overhead relative to a Cornucopia baseline that zeros before reallocation, strengthening CHERI temporal safety without performance overhead.

cs.AR↗

arXiv2025

STAR-Edge: Structure-aware Local Spherical Curve Representation for Thin-walled Edge Extraction from Unstructured Point Clouds

Extracting geometric edges from unstructured point clouds remains a significant challenge, particularly in thin-walled structures that are commonly found in everyday objects. Traditional geometric methods and recent learning-based approaches frequently struggle with these structures, as both rely heavily on sufficient contextual information from local point neighborhoods. However, 3D measurement data of thin-walled structures often lack the accurate, dense, and regular neighborhood sampling required for reliable edge extraction, resulting in degraded performance. In this work, we introduce STAR-Edge, a novel approach designed for detecting and refining edge points in thin-walled structures. Our method leverages a unique representation-the local spherical curve-to create structure-aware neighborhoods that emphasize co-planar points while reducing interference from close-by, non-co-planar surfaces. This representation is transformed into a rotation-invariant descriptor, which, combined with a lightweight multi-layer perceptron, enables robust edge point classification even in the presence of noise and sparse or irregular sampling. Besides, we also use the local spherical curve representation to estimate more precise normals and introduce an optimization function to project initially identified edge points exactly on the true edges. Experiments conducted on the ABC dataset and thin-walled structure-specific datasets demonstrate that STAR-Edge outperforms existing edge detection methods, showcasing better robustness under various challenging conditions.

cs.CV↗