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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
Future-Proofing Cloud Security Against Quantum Attacks: R...
Yaser Baseri, Abdelhakim Hafid, Arash Habibi Lashkari · 2025-09-19 · via cs.CR updates on arXiv.org

Quantum Computing (QC) threatens the cryptographic foundations of Cloud Computing (CC), exposing distributed infrastructures to novel attack vectors. This survey provides comprehensive analysis of quantum-safe cloud security, examining vulnerabilities, transition strategies, and layer-specific countermeasures across nine architectural layers (application, data, runtime, middleware, OS, virtualization, server, storage, networking). We employ STRIDE-based risk assessment aligned with NIST SP 800-30 to evaluate quantum threats through three transition phases: pre-transition (classical cryptography vulnerabilities), hybrid (migration risks), and post-transition (PQC implementation weaknesses including side-channel attacks). Our security framework integrates hybrid cryptographic strategies (algorithmic combiners, dual/composite certificates, protocol-level migration), cryptographic agility, and risk-prioritized mitigation tailored to cloud environments. We benchmark NIST-standardized PQC algorithms for performance and deployment suitability, assess side-channel and implementation vulnerabilities, and analyze quantum-safe strategies from leading CSPs (AWS, Azure, GCP). The survey delivers layer-specific threat taxonomies, likelihood-impact risk matrices, and CSP-informed deployment roadmaps for cloud architects, policymakers, and researchers. We identify six critical research directions: standardization and interoperability, hardware acceleration and performance optimization, AI-enhanced security and threat mitigation, integration with emerging cloud technologies, systemic preparedness and workforce development, and migration frameworks with crypto-agility.