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

Microsoft Security Blog
Microsoft Security Blog
量子位
大猫的无限游戏
大猫的无限游戏
酷 壳 – CoolShell
酷 壳 – CoolShell
IT之家
IT之家
博客园 - 三生石上(FineUI控件)
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 - Franky
美团技术团队
Last Week in AI
Last Week in AI
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
人人都是产品经理
人人都是产品经理
罗磊的独立博客
Jina AI
Jina AI
小众软件
小众软件
S
SegmentFault 最新的问题
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
雷峰网
雷峰网
博客园 - 聂微东
博客园_首页
The Cloudflare Blog
WordPress大学
WordPress大学
Apple Machine Learning Research
Apple Machine Learning Research
有赞技术团队
有赞技术团队

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 Automated Potential Primary Asset Identification i...
Subroto Kumer Deb Nath, Benjamin Tan · 2025-02-07 · via cs.CR updates on arXiv.org

With greater design complexity, the challenge to anticipate and mitigate security issues provides more responsibility for the designer. As hardware provides the foundation of a secure system, we need tools and techniques that support engineers to improve trust and help them address security concerns. Knowing the security assets in a design is fundamental to downstream security analyses, such as threat modeling, weakness identification, and verification. This paper proposes an automated approach for the initial identification of potential security assets in a Verilog design. Taking inspiration from manual asset identification methodologies, we analyze open-source hardware designs in three IP families and identify patterns and commonalities likely to indicate structural assets. Through iterative refinement, we provide a potential set of primary security assets and thus help to reduce the manual search space.