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On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety 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 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
Cookie Synchronization: Everything You Always Wanted to K...
Panagiotis Papadopoulos, Nicolas Kourtellis, Evangelos P. Markat · 2018-05-27 · via cs.CR updates on arXiv.org

User data is the primary input of digital advertising, fueling the free Internet as we know it. As a result, web companies invest a lot in elaborate tracking mechanisms to acquire user data that can sell to data markets and advertisers. However, with same-origin policy, and cookies as a primary identification mechanism on the web, each tracker knows the same user with a different ID. To mitigate this, Cookie Synchronization (CSync) came to the rescue, facilitating an information sharing channel between third parties that may or not have direct access to the website the user visits. In the background, with CSync, they merge user data they own, but also reconstruct a user's browsing history, bypassing the same origin policy. In this paper, we perform a first to our knowledge in-depth study of CSync in the wild, using a year-long weblog from 850 real mobile users. Through our study, we aim to understand the characteristics of the CSync protocol and the impact it has on web users' privacy. For this, we design and implement CONRAD, a holistic mechanism to detect CSync events at real time, and the privacy loss on the user side, even when the synced IDs are obfuscated. Using CONRAD, we find that 97% of the regular web users are exposed to CSync: most of them within the first week of their browsing, and the median userID gets leaked, on average, to 3.5 different domains. Finally, we see that CSync increases the number of domains that track the user by a factor of 6.75.