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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
Estimating the Relative Speed of RF Jammers in VANETs
Dimitrios Kosmanos, Antonios Argyriou, Leandros Maglaras · 2018-12-31 · via cs.CR updates on arXiv.org

Vehicular Ad-Hoc Networks (VANETs) aim at enhancing road safety and providing a comfortable driving environment by delivering early warning and infotainment messages to the drivers. Jamming attacks, however, pose a significant threat to their performance. In this paper, we propose a novel Relative Speed Estimation Algorithm (RSEA) of a moving interfering vehicle that approaches a Transmitter ($Tx$) - Receiver ($Rx$) pair, that interferes with their Radio Frequency (RF) communication by conducting a Denial of Service (DoS) attack. Our scheme is completely sensorless and passive and uses a pilot-based received signal without hardware or computational cost in order to, firstly, estimate the combined channel between the transmitter - receiver and jammer - receiver and secondly, to estimate the jamming signal and the relative speed between the jammer - receiver using the RF Doppler shift. Moreover, the relative speed metric exploits the Angle of Projection (AOP) of the speed vector of the jammer in the axis of its motion in order to form a two-dimensional representation of the geographical area. This approach can effectively be applied both for a jamming signal completely unknown to the receiver and for a jamming signal partly known to the receiver. Our speed estimator method is proven to have quite accurate performance, with a Mean Absolute Error (MAE) value of approximately $10\%$ compared to the optimal zero MAE value under different jamming attack scenarios.