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

GbyAI
GbyAI
Y
Y Combinator Blog
F
Fortinet All Blogs
H
Hackread – Cybersecurity News, Data Breaches, AI and More
N
Netflix TechBlog - Medium
T
Tailwind CSS Blog
aimingoo的专栏
aimingoo的专栏
博客园 - Franky
T
The Blog of Author Tim Ferriss
D
DataBreaches.Net
量子位
博客园 - 三生石上(FineUI控件)
I
InfoQ
Engineering at Meta
Engineering at Meta
WordPress大学
WordPress大学
阮一峰的网络日志
阮一峰的网络日志
爱范儿
爱范儿
D
Docker
美团技术团队
雷峰网
雷峰网
U
Unit 42
Stack Overflow Blog
Stack Overflow Blog
Recent Announcements
Recent Announcements
人人都是产品经理
人人都是产品经理

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
Speculose: Analyzing the Security Implications of Specula...
Giorgi Maisuradze, Christian Rossow · 2018-01-12 · via cs.CR updates on arXiv.org

Whenever modern CPUs encounter a conditional branch for which the condition cannot be evaluated yet, they predict the likely branch target and speculatively execute code. Such pipelining is key to optimizing runtime performance and is incorporated in CPUs for more than 15 years. In this paper, to the best of our knowledge, we are the first to study the inner workings and the security implications of such speculative execution. We revisit the assumption that speculatively executed code leaves no traces in case it is not committed. We reveal several measurable side effects that allow adversaries to enumerate mapped memory pages and to read arbitrary memory---all using only speculated code that was never fully executed. To demonstrate the practicality of such attacks, we show how a user-space adversary can probe for kernel pages to reliably break kernel-level ASLR in Linux in under three seconds and reduce the Windows 10 KASLR entropy by 18~bits in less than a second.