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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
Goldilocks Isolation: High Performance VMs with Edera
Marina Moore, Alex Zenla · 2025-01-08 · via cs.CR updates on arXiv.org

Organizations run applications on cloud infrastructure shared between multiple users and organizations. Popular tooling for this shared infrastructure, including Docker and Kubernetes, supports such multi-tenancy through the use of operating system virtualization. With operating system virtualization (known as containerization), multiple applications share the same kernel, reducing the runtime overhead. However, this shared kernel presents a large attack surface and has led to a proliferation of container escape attacks in which a kernel exploit lets an attacker escape the isolation of operating system virtualization to access other applications or the operating system itself. To address this, some systems have proposed a return to hypervisor virtualization for stronger isolation between applications. However, no existing system has achieved both the isolation of hypervisor virtualization and the performance and usability of operating system virtualization. We present Edera, an optimized type 1 hypervisor that uses paravirtualization to improve the runtime of hypervisor virtualization. We illustrate Edera's usability and performance through two use cases. First, we create a container runtime compatible with Kubernetes that runs on the Edera hypervisor. This implementation can be used as a drop-in replacement for the Kubernetes runtime and is compatible with all the tooling in the Kubernetes ecosystem. Second, we use Edera to provide driver isolation for hardware drivers, including those for networking, storage, and GPUs. This use of isolation protects the hypervisor and other applications from driver vulnerabilities. We find that Edera has runtime comparable to Docker with .9% slower cpu speeds, an average of 3% faster system call performance, and memory performance 0-7% faster. It achieves this with a 648 millisecond increase in startup time from Docker's 177.4 milliseconds.