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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 Evaluation of Circuit Selection Methods on Tor
Mohsen Imani, Mehrdad Amirabadi, Matthew Wright · 2017-06-18 · via cs.CR updates on arXiv.org

Tor provides anonymity online by routing traffic through encrypted tunnels, called circuits, over paths of anonymizing relays. To enable users to connect to their selected destination servers without waiting for the circuit to be build, the Tor client maintains a few circuits at all times. Nevertheless, Tor is slower to use than directly connecting to the destination server. In this paper, we propose to have the Tor client measure the performance of the pre-built circuits and select the fastest circuits for users to send their traffic over. To this end, we define and evaluate nine metrics for selecting which pre-built circuit to use based on different combinations of circuit length, Round Trip Time (RTT), and congestion. We also explore the effect on performance of the number of pre-built circuits at the time of the selection. Through whole-network experiments in Shadow, we show that using circuit RTT with at least three pre-built circuits allows the Tor client to identify fast circuits and improves median time to first byte (TTFB) by 22% over Tor and 15% over congestion-aware routing, the state-of-the-art in Tor circuit selection. We evaluate the security of the proposed circuit selection mechanism against both a relay-level and a network-level adversary and find no loss of security compared with Tor.