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

雷峰网
雷峰网
L
LangChain Blog
GbyAI
GbyAI
F
Fortinet All Blogs
腾讯CDC
Last Week in AI
Last Week in AI
A
About on SuperTechFans
J
Java Code Geeks
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - Franky
B
Blog
D
Docker
G
Google Developers Blog
月光博客
月光博客
博客园 - 三生石上(FineUI控件)
S
SegmentFault 最新的问题
Apple Machine Learning Research
Apple Machine Learning Research
酷 壳 – CoolShell
酷 壳 – CoolShell
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
T
Tailwind CSS Blog
宝玉的分享
宝玉的分享
U
Unit 42
Blog — PlanetScale
Blog — PlanetScale
B
Blog RSS Feed

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
Opening the Doors to Dynamic Camouflaging: Harnessing the...
Nikhil Rangarajan, Satwik Patnaik, Johann Knechtel, Ramesh Karri · 2018-11-15 · via cs.CR updates on arXiv.org

The era of widespread globalization has led to the emergence of hardware-centric security threats throughout the IC supply chain. Prior defenses like logic locking, layout camouflaging, and split manufacturing have been researched extensively to protect against intellectual property (IP) piracy at different stages. In this work, we present dynamic camouflaging as a new technique to thwart IP reverse engineering at all stages in the supply chain, viz., the foundry, the test facility, and the end-user. Toward this end, we exploit the multi-functionality, post-fabrication reconfigurability, and run-time polymorphism of spin-based devices, specifically the magneto-electric spin-orbit (MESO) device. Leveraging these unique properties, dynamic camouflaging is shown to be resilient against state-of-the-art analytical SAT-based attacks and test-data mining attacks. Such dynamic reconfigurability is not afforded in CMOS owing to fundamental differences in operation. For such MESO-based camouflaging, we also anticipate massive savings in power, performance, and area over other spin-based camouflaging schemes, due to the energy-efficient electric-field driven reversal of the MESO device. Based on thorough experimentation, we outline the promises of dynamic camouflaging in securing the supply chain end-to-end along with a case study, demonstrating the efficacy of dynamic camouflaging in securing error-tolerant image processing IP.