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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
Single Round-trip Hierarchical ORAM via Succinct Indices
William Holland, Olga Ohrimenko, Anthony Wirth · 2022-08-16 · via cs.CR updates on arXiv.org

Access patterns to data stored remotely create a side channel that is known to leak information even if the content of the data is encrypted. To protect against access pattern leakage, Oblivious RAM is a cryptographic primitive that obscures the (actual) access trace at the expense of additional access and periodic shuffling of the server's contents. A class of ORAM solutions, known as Hierarchical ORAM, has achieved theoretically \emph{optimal} logarithmic bandwidth overhead. However, to date, Hierarchical ORAMs are seen as only theoretical artifacts. This is because they require a large number of communication round-trips to locate (shuffled) elements at the server and involve complex building blocks such as cuckoo hash tables. To address the limitations of Hierarchical ORAM schemes in practice, we introduce Rank ORAM; the first Hierarchical ORAM that can retrieve data with a single round-trip of communication (as compared to a logarithmic number in previous work). To support non-interactive communication, we introduce a \emph{compressed} client-side data structure that stores, implicitly, the location of each element at the server. In addition, this location metadata enables a simple protocol design that dispenses with the need for complex cuckoo hash tables. Rank ORAM requires asymptotically smaller memory than existing (non-Hierarchical) state-of-the-art practical ORAM schemes (e.g., Ring ORAM) while maintaining comparable bandwidth performance. Our experiments on real network file-system traces demonstrate a reduction in client memory, against existing approaches, of a factor of~$100$. For example, when {outsourcing} a database of $17.5$TB, required client-memory is only $290$MB vs. $40$GB for standard approaches.