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

人人都是产品经理
人人都是产品经理
博客园_首页
IT之家
IT之家
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
Vercel News
Vercel News
美团技术团队
D
Docker
WordPress大学
WordPress大学
T
Tailwind CSS Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
The Cloudflare Blog
Y
Y Combinator Blog
F
Fortinet All Blogs
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
G
Google Developers Blog
爱范儿
爱范儿
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
月光博客
月光博客
MongoDB | Blog
MongoDB | Blog
S
SegmentFault 最新的问题
GbyAI
GbyAI
Hugging Face - Blog
Hugging Face - Blog
Microsoft Azure Blog
Microsoft Azure Blog
A
About on SuperTechFans

cs.CR updates on arXiv.org

On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety 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 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
Chaotic Memory Randomization for Securing Embedded Systems
Peter Henderson, Muthucumaru Maheswaran · 2016-11-03 · via cs.CR updates on arXiv.org

Embedded systems permeate through nearly all aspects of modern society. From cars to refrigerators to nuclear refineries, securing these systems has never been more important. Intrusions, such as the Stuxnet malware which broke the centrifuges in Iran's Natanz refinery, can be catastrophic to not only the infected systems, but even to the wellbeing of the surrounding population. Modern day protection mechanisms for these embedded systems generally look only at protecting the network layer, and those that try to discover malware already existing on a system typically aren't efficient enough to run on a standalone embedded system. As such, we present a novel way to ensure that no malware has been inserted into an embedded system. We chaotically randomize the entire memory space of the application, interspersing watchdog-monitor programs throughout, to monitor that the core application hasn't been infiltrated. By validating the original program through conventional methods and creating a clean reset, we can ensure that any inserted malware is purged from the system with minimal effect on the given system. We also present a software prototype to validate the possibility of this approach, but given the limitations and vulnerabilities of the prototype, we also suggest a hardware alternative to the system.