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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
COOKIEGRAPH: Understanding and Detecting First-Party Trac...
Shaoor Munir, Sandra Siby, Umar Iqbal, Steven Englehardt, Zubair · 2022-08-26 · via cs.CR updates on arXiv.org

As third-party cookie blocking is becoming the norm in browsers, advertisers and trackers have started to use first-party cookies for tracking. We conduct a differential measurement study on 10K websites with third-party cookies allowed and blocked. This study reveals that first-party cookies are used to store and exfiltrate identifiers to known trackers even when third-party cookies are blocked. As opposed to third-party cookie blocking, outright first-party cookie blocking is not practical because it would result in major functionality breakage. We propose CookieGraph, a machine learning-based approach that can accurately and robustly detect first-party tracking cookies. CookieGraph detects first-party tracking cookies with 90.20% accuracy, outperforming the state-of-the-art CookieBlock approach by 17.75%. We show that CookieGraph is fully robust against cookie name manipulation while CookieBlock's acuracy drops by 15.68%. While blocking all first-party cookies results in major breakage on 32% of the sites with SSO logins, and CookieBlock reduces it to 10%, we show that CookieGraph does not cause any major breakage on these sites. Our deployment of CookieGraph shows that first-party tracking cookies are used on 93.43% of the 10K websites. We also find that first-party tracking cookies are set by fingerprinting scripts. The most prevalent first-party tracking cookies are set by major advertising entities such as Google, Facebook, and TikTok.