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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
SPREE: Spoofing Resistant GPS Receiver
Aanjhan Ranganathan, Hildur Ólafsdóttir, Srdjan Capkun · 2016-03-17 · via cs.CR updates on arXiv.org

Global Positioning System (GPS) is used ubiquitously in a wide variety of applications ranging from navigation and tracking to modern smart grids and communication networks. However, it has been demonstrated that modern GPS receivers are vulnerable to signal spoofing attacks. For example, today it is possible to change the course of a ship or force a drone to land in an hostile area by simply spoofing GPS signals. Several countermeasures have been proposed in the past to detect GPS spoofing attacks. These countermeasures offer protection only against naive attackers. They are incapable of detecting strong attackers such as those capable of seamlessly taking over a GPS receiver, which is currently receiving legitimate satellite signals, and spoofing them to an arbitrary location. Also, there is no hardware platform that can be used to compare and evaluate the effectiveness of existing countermeasures in real-world scenarios. In this work, we present SPREE, which is, to the best of our knowledge, the first GPS receiver capable of detecting all spoofing attacks described in literature. Our novel spoofing detection technique called auxiliary peak tracking enables detection of even a strong attacker capable of executing the seamless takeover attack. We implement and evaluate our receiver against three different sets of GPS signal traces and show that SPREE constrains even a strong attacker (capable of seamless takeover attack) from spoofing the receiver to a location not more than 1 km away from its true location. This is a significant improvement over modern GPS receivers that can be spoofed to any arbitrary location. Finally, we release our implementation and datasets to the community for further research and development.