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

罗磊的独立博客
爱范儿
爱范儿
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
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_首页
博客园 - 叶小钗
酷 壳 – CoolShell
酷 壳 – CoolShell
Apple Machine Learning Research
Apple Machine Learning Research
云风的 BLOG
云风的 BLOG
量子位
博客园 - 三生石上(FineUI控件)
Stack Overflow Blog
Stack Overflow Blog
小众软件
小众软件
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
V
V2EX
人人都是产品经理
人人都是产品经理
V
Visual Studio Blog
Jina AI
Jina AI
L
LangChain Blog
M
MIT News - Artificial intelligence
MongoDB | Blog
MongoDB | Blog
Last Week in AI
Last Week in AI
Martin Fowler
Martin Fowler
WordPress大学
WordPress大学

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
The Reversing Machine: Reconstructing Memory Assumptions
Mohammad Sina Karvandi, Soroush Meghdadizanjani, Sima Arasteh, S · 2024-05-01 · via cs.CR updates on arXiv.org

Existing anti-malware software and reverse engineering toolkits struggle with stealthy sub-OS rootkits due to limitations of run-time kernel-level monitoring. A malicious kernel-level driver can bypass OS-level anti-virus mechanisms easily. Although static analysis of such malware is possible, obfuscation and packing techniques complicate offline analysis. Moreover, current dynamic analyzers suffer from virtualization performance overhead and create detectable traces that allow modern malware to evade them. To address these issues, we present \textit{The Reversing Machine} (TRM), a new hypervisor-based memory introspection design for reverse engineering, reconstructing memory offsets, and fingerprinting evasive and obfuscated user-level and kernel-level malware. TRM proposes two novel techniques that enable efficient and transparent analysis of evasive malware: hooking a binary using suspended process creation for hypervisor-based memory introspection, and leveraging Mode-Based Execution Control (MBEC) to detect user/kernel mode transitions and memory access patterns. Unlike existing malware detection environments, TRM can extract full memory traces in user and kernel spaces and hook the entire target memory map to reconstruct arrays, structures within the operating system, and possible rootkits. We perform TRM-assisted reverse engineering of kernel-level structures and show that it can speed up manual reverse engineering by 75\% on average. We obfuscate known malware with the latest packing tools and successfully perform similarity detection. Furthermore, we demonstrate a real-world attack by deploying a modified rootkit onto a driver that bypasses state-of-the-art security auditing tools. We show that TRM can detect each threat and that, out of 24 state-of-the-art AV solutions, only TRM can detect the most advanced threats.