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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
Tyche: Composable Isolation as a Foundation to Manage Tru...
Adrien Ghosn, Charly Castes, Neelu S. Kalani, Yuchen Qian, Mario · 2025-07-17 · via cs.CR updates on arXiv.org

Cloud workloads combine software components from different parties to process sensitive data. Each component has its own trust model - it must protect its assets from the rest of the system, yet share sensitive data with components it cannot trust to keep confidential. This tension requires composing isolation boundaries for confidentiality and encapsulation. Unfortunately, the cloud offers no direct way to compose such boundaries, forcing tenants to assemble, deploy, and maintain their own solutions. This paper shifts that burden back to the infrastructure by making composable, attestable isolation a first-class systems abstraction. We present Tyche, a security monitor that centers isolation around a unified composable abstraction: security domains (SDs). An SD is an execution environment whose access to machine resources - memory, cores, devices - is controlled through explicit capabilities. A small set of capability operations enables SDs to partition, share, and reclaim resources; by nesting recursively, SDs compose attestable trust boundaries for confidentiality and encapsulation. Tyche attests these compositions, providing end-to-end security guarantees for workloads made of mutually distrustful components. As a first-class cloud primitive, this single abstraction subsumes enclaves, sandboxes, CVMs, and their compositions. Tyche provides composable isolation without sacrificing compatibility with existing hardware and software stacks. It runs on commodity x86 64 hardware without security extensions, and a RISC-V prototype demonstrates portability across platforms. Our SDK composes isolation for unmodified workloads within SDs with minimal overhead. In a confidential LLM inference scenario with mutually distrustful users, model owners, and cloud providers, the slowdown is just 2% compared to bare-metal Linux.