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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
Towards Zero Trust Security in Connected Vehicles: A Comp...
Malak Annabi, Abdelhafid Zeroual, Nadhir Messai · 2025-04-08 · via cs.CR updates on arXiv.org

Zero Trust is the new cybersecurity model that challenges the traditional one by promoting continuous verification of users, devices, and applications, whatever their position or origin. This model is critical for reducing the attack surface and preventing lateral movement without relying on implicit trust. Adopting the zero trust principle in Intelligent Transportation Systems (ITS), especially in the context of connected vehicles (CVs), presents an adequate solution in the face of increasing cyber threats, thereby strengthening the ITS environment. This paper offers an understanding of Zero Trust security through a comprehensive review of existing literature, principles, and challenges. It specifically examines its applications in emerging technologies, particularly within connected vehicles, addressing potential issues and cyber threats faced by CVs. Inclusion/exclusion criteria for the systematic literature review were planned alongside a bibliometric analysis. Moreover, keyword co-occurrence analysis was done, which indicates trends and general themes for the Zero Trust model, Zero Trust implementation, and Zero Trust application. Furthermore, the paper explores various ZT models proposed in the literature for connected vehicles, shedding light on the challenges associated with their integration into CV systems. Future directions of this research will focus on incorporating Zero Trust principles within Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication paradigms. This initiative intends to enhance the security posture and safety protocols within interconnected vehicular networks. The proposed research seeks to address the unique cybersecurity vulnerabilities inherent in the highly dynamic nature of vehicular communication systems.