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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
Microservice Vulnerability Analysis: A Literature Review ...
Raveen Kanishka Jayalath, Hussain Ahmad, Diksha Goel, Muhammad S · 2024-07-31 · via cs.CR updates on arXiv.org

Microservice architectures are revolutionizing both small businesses and large corporations, igniting a new era of innovation with their exceptional advantages in maintainability, reusability, and scalability. However, these benefits come with significant security challenges, as the increased complexity of service interactions, expanded attack surfaces, and intricate dependency management introduce a new array of cybersecurity vulnerabilities. While security concerns are mounting, there is a lack of comprehensive research that integrates a review of existing knowledge with empirical analysis of microservice vulnerabilities. This study aims to fill this gap by gathering, analyzing, and synthesizing existing literature on security vulnerabilities associated with microservice architectures. Through a thorough examination of 62 studies, we identify, analyze, and report 126 security vulnerabilities inherent in microservice architectures. This comprehensive analysis enables us to (i) propose a taxonomy that categorizes microservice vulnerabilities based on the distinctive features of microservice architectures; (ii) conduct an empirical analysis by performing vulnerability scans on four diverse microservice benchmark applications using three different scanning tools to validate our taxonomy; and (iii) map our taxonomy vulnerabilities with empirically identified vulnerabilities, providing an in-depth vulnerability analysis at microservice, application, and scanning tool levels. Our study offers crucial guidelines for practitioners and researchers to advance both the state-of-the-practice and the state-of-the-art in securing microservice architectures.