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

F
Fortinet All Blogs
小众软件
小众软件
大猫的无限游戏
大猫的无限游戏
B
Blog RSS Feed
WordPress大学
WordPress大学
A
About on SuperTechFans
Apple Machine Learning Research
Apple Machine Learning Research
博客园 - 司徒正美
I
InfoQ
MyScale Blog
MyScale Blog
量子位
博客园 - 【当耐特】
M
MIT News - Artificial intelligence
H
Hackread – Cybersecurity News, Data Breaches, AI and More
博客园 - 三生石上(FineUI控件)
博客园 - 聂微东
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
博客园 - 叶小钗
J
Java Code Geeks
L
LangChain Blog
T
The Blog of Author Tim Ferriss
有赞技术团队
有赞技术团队
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
阮一峰的网络日志
阮一峰的网络日志

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
Building Your Own Trusted Execution Environments Using FPGA
Md Armanuzzaman, Ahmad-Reza Sadeghi, Ziming Zhao · 2022-03-09 · via cs.CR updates on arXiv.org

In recent years, we have witnessed unprecedented growth in using hardware-assisted Trusted Execution Environments (TEE) or enclaves to protect sensitive code and data on commodity devices thanks to new hardware security features, such as Intel SGX and Arm TrustZone. Even though the proprietary TEEs bring many benefits, they have been criticized for lack of transparency, vulnerabilities, and various restrictions. For example, existing TEEs only provide a static and fixed hardware Trusted Computing Base (TCB), which cannot be customized for different applications. Existing TEEs time-share a processor core with the Rich Execution Environment (REE), making execution less efficient and vulnerable to cache side-channel attacks. Moreover, TrustZone lacks hardware support for multiple TEEs, remote attestation, and memory encryption. In this paper, we present BYOTee (Build Your Own Trusted Execution Environments), which is an easy-to-use infrastructure for building multiple equally secure enclaves by utilizing commodity Field Programmable Gate Arrays (FPGA) devices. BYOTee creates enclaves with customized hardware TCBs, which include softcore CPUs, block RAMs, and peripheral connections, in FPGA on demand. Additionally, BYOTee provides mechanisms to attest the integrity of the customized enclaves' hardware and software stacks, including bitstream, firmware, and the Security-Sensitive Applications (SSA) along with their inputs and outputs to remote verifiers. We implement a BYOTee system for the Xilinx System-on-Chip (SoC) FPGA. The evaluations on the low-end Zynq-7000 system for four SSAs and 12 benchmark applications demonstrate the usage, security, effectiveness, and performance of the BYOTee framework.