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cs.CR updates on arXiv.org

On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety 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 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
Mutualized oblivious DNS ($μ$ODNS): Hiding a tree in the ...
Jun Kurihara, Takeshi Kubo · 2021-04-28 · via cs.CR updates on arXiv.org

The traditional Domain Name System (DNS) lacks fundamental features of security and privacy in its design. As concerns of privacy increased on the Internet, security and privacy enhancements of DNS have been actively investigated and deployed. Specially for user's privacy in DNS queries, several relay-based anonymization schemes have been recently introduced, however, they are vulnerable to the collusion of a relay with a full-service resolver, i.e., identities of users cannot be hidden to the resolver. This paper introduces a new concept of a multiple-relay-based DNS for user anonymity in DNS queries, called the mutualized oblivious DNS ($μ$ODNS), by extending the concept of existing relay-based schemes. The $μ$ODNS introduces a small and reasonable assumption that each user has at least one trusted/dedicated relay in a network and mutually shares the dedicated one with others. The user just sets the dedicated one as his next-hop, first relay, conveying his queries to the resolver, and randomly chooses its $0$ or more subsequent relays shared by other entities. Under this small assumption, the user's identity is concealed to a target resolver in the $μ$ODNS even if a certain (unknown) subset of relays collude with the resolver. That is, in $μ$ODNS, users can preserve their privacy and anonymity just by paying a small cost of sharing its resource. Moreover, we present a PoC implementation of $μ$ODNS that is publicly available on the Internet. We also show that by measurement of round-trip-time for queries, and our PoC implementation of $μ$ODNS achieves the performance comparable to existing relay-based schemes.