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

IT之家
IT之家
U
Unit 42
大猫的无限游戏
大猫的无限游戏
H
Help Net Security
G
Google Developers Blog
Recent Announcements
Recent Announcements
B
Blog RSS Feed
罗磊的独立博客
博客园 - Franky
J
Java Code Geeks
S
SegmentFault 最新的问题
D
DataBreaches.Net
C
Check Point Blog
Blog — PlanetScale
Blog — PlanetScale
T
The Blog of Author Tim Ferriss
有赞技术团队
有赞技术团队
腾讯CDC
博客园_首页
美团技术团队
V
Visual Studio Blog
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
GbyAI
GbyAI
The Cloudflare Blog
aimingoo的专栏
aimingoo的专栏

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
Toward a Lightweight, Scalable, and Parallel Secure Encry...
Rasha Karakchi, Rye Stahle-Smith, Nishant Chinnasami, Tiffany Yu · 2025-06-18 · via cs.CR updates on arXiv.org

The exponential growth of Internet of Things (IoT) applications has intensified the demand for efficient, high-throughput, and energy-efficient data processing at the edge. Conventional CPU-centric encryption methods suffer from performance bottlenecks and excessive data movement, especially in latency-sensitive and resource-constrained environments. In this paper, we present SPiME, a lightweight, scalable, and FPGA-compatible Secure Processor-in-Memory Encryption architecture that integrates the Advanced Encryption Standard (AES-128) directly into a Processing-in-Memory (PiM) framework. SPiME is designed as a modular array of parallel PiM units, each combining an AES core with a minimal control unit to enable distributed in-place encryption with minimal overhead. The architecture is fully implemented in Verilog and tested on multiple AMD UltraScale and UltraScale+ FPGAs. Evaluation results show that SPiME can scale beyond 4,000 parallel units while maintaining less than 5\% utilization of key FPGA resources on high-end devices. It delivers over 25~Gbps in sustained encryption throughput with predictable, low-latency performance. The design's portability, configurability, and resource efficiency make it a compelling solution for secure edge computing, embedded cryptographic systems, and customizable hardware accelerators.