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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
Bad Neighbors: On Understanding VPN Provider Networks
2024-10-11 · via cs.CR updates on arXiv.org

Virtual Private Network (VPN) solutions are used to connect private networks securely over the Internet. Besides their benefits in corporate environments, VPNs are also marketed to privacy-minded users to preserve their privacy, and to bypass geolocation-based content blocking and censorship. This has created a market for turnkey VPN services offering a multitude of vantage points all over the world for a monthly price. While VPN providers are heavily using privacy and security benefits in their marketing, such claims are generally hard to measure and substantiate. While there exist some studies on the VPN ecosystem, all prior works omit a critical part in their analyses: (i) How well do the providers configure and secure their own network infrastructure? and (ii) How well are they protecting their customers from other customers? To answer these questions, we have developed an automated measurement system with which we conduct a large-scale analysis of VPN providers and their thousands of VPN endpoints. Considering the fact that VPNs work internally using non-Internet-routable IP addresses, they might enable access to otherwise inaccessible networks. If not properly secured, this can inadvertently expose internal networks of these providers, or worse, even other clients connected to their services. Our results indicate a widespread lack of traffic filtering towards internally routable networks on the majority of tested VPN service providers, even in cases where no other VPN customers were directly exposed. We have disclosed our findings to the affected providers and other stakeholders, and offered guidance to improve the situation.