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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
Evaluating Susceptibility of VPN Implementations to DoS A...
Fabio Streun, Joel Wanner, Adrian Perrig · 2021-10-01 · via cs.CR updates on arXiv.org

Many systems today rely heavily on virtual private network (VPN) technology to connect networks and protect their services on the Internet. While prior studies compare the performance of different implementations, they do not consider adversarial settings. To address this gap, we evaluate the resilience of VPN implementations to flooding-based denial-of-service (DoS) attacks. We focus on a class of stateless flooding attacks, which are particularly threatening to real connections, as they can be carried out by an off-path attacker using spoofed IP addresses. We have implemented various attacks to evaluate DoS resilience for three major open-source VPN solutions, with surprising results: On high-performance hardware with a $40\,\mathrm{Gb/s}$ interface, data transfer over established WireGuard connections can be fully denied with $700\,\mathrm{Mb/s}$ of attack traffic. For strongSwan (IPsec), an adversary can block any legitimate connections from being established using only $75\,\mathrm{Mb/s}$ of attack traffic. OpenVPN can be overwhelmed with $100\,\mathrm{Mb/s}$ of flood traffic denying data transfer through the VPN connection as well as connection establishment completely. Further analysis has revealed implementation bugs and major inefficiencies in the implementations related to concurrency aspects. These findings demonstrate a need for more adversarial testing of VPN implementations with respect to DoS resilience.