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

T
Tailwind CSS Blog
博客园 - 【当耐特】
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - Franky
小众软件
小众软件
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 三生石上(FineUI控件)
B
Blog
有赞技术团队
有赞技术团队
J
Java Code Geeks
云风的 BLOG
云风的 BLOG
Recent Announcements
Recent Announcements
Jina AI
Jina AI
Vercel News
Vercel News
博客园 - 叶小钗
Hugging Face - Blog
Hugging Face - Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
F
Fortinet All Blogs
The Cloudflare Blog
V
V2EX
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园_首页
腾讯CDC

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
Avaddon ransomware: an in-depth analysis and decryption o...
Javier Yuste, Sergio Pastrana · 2021-02-09 · via cs.CR updates on arXiv.org

The commoditization of Malware-as-a-Service (MaaS) allows criminals to obtain financial benefits at a low risk and with little technical background. One such popular product in the underground economy is ransomware. In ransomware attacks, data from infected systems is held hostage (encrypted) until a fee is paid to the criminals. This modus operandi disrupts legitimate businesses, which may become unavailable until the data is restored. A recent blackmailing strategy adopted by criminals is to leak data online from the infected systems if the ransom is not paid. Besides reputational damage, data leakage might produce further economical losses due to fines imposed by data protection laws. Thus, research on prevention and recovery measures to mitigate the impact of such attacks is needed to adapt existing countermeasures to new strains. In this work, we perform an in-depth analysis of Avaddon, a ransomware offered in the underground economy as an affiliate program business. This has infected and leaked data from at least 23 organizations. Additionally, it runs Distributed Denial-of-Service (DDoS) attacks against victims that do not pay the ransom. We first provide an analysis of the criminal business model from the underground economy. Then, we identify and describe its technical capabilities. We provide empirical evidence of links between this variant and a previous family, suggesting that the same group was behind the development and, possibly, the operation of both campaigns. Finally, we describe a method to decrypt files encrypted with Avaddon in real time. We implement and test the decryptor in a tool that can recover the encrypted data from an infected system, thus mitigating the damage caused by the ransomware. The tool is released open-source so it can be incorporated in existing Antivirus engines.