惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

量子位
I
InfoQ
人人都是产品经理
人人都是产品经理
博客园 - 三生石上(FineUI控件)
爱范儿
爱范儿
Hugging Face - Blog
Hugging Face - Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
S
SegmentFault 最新的问题
美团技术团队
小众软件
小众软件
Blog — PlanetScale
Blog — PlanetScale
Jina AI
Jina AI
aimingoo的专栏
aimingoo的专栏
H
Help Net Security
Last Week in AI
Last Week in AI
博客园_首页
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
L
LangChain Blog
云风的 BLOG
云风的 BLOG
Martin Fowler
Martin Fowler
宝玉的分享
宝玉的分享
G
Google Developers Blog
博客园 - 叶小钗
博客园 - Franky

cs.CR updates on arXiv.org

Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
CHERI-D: Secure and efficient inline object ID for CHERI ...
[Submitted on 17 Jun 2026] · 2026-06-18 · via cs.CR updates on arXiv.org

View PDF HTML (experimental)

Abstract: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.

Submission history

From: Yuecheng Wang [view email]
[v1] Wed, 17 Jun 2026 13:24:23 UTC (1,334 KB)