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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
Adaptive t Design Dummy-Gate Obfuscation for Cryogenic Sc...
Samuel Punch, Krishnendu Guha · 2025-08-31 · via cs.CR updates on arXiv.org

Cloud quantum services can reveal circuit structure and timing through scheduler metadata, latency patterns, and co-tenant interference. We introduce NADGO (Noise-Adaptive Dummy-Gate Obfuscation), a scheduling and obfuscation stack that enforces operational privacy for gate-model workloads by applying per-interval limits on observable information leakage. To support confidentiality and fair multi-tenancy, operators require a method to audit compliance at acceptable overheads. NADGO combines: (i) hardware-aware t-design padding for structured cover traffic, (ii) particle-filter timing randomization to mask queue patterns, (iii) CASQUE subcircuit routing across heterogeneous backends, and (iv) a per-interval leakage estimator with locked calibration artifacts and a dual-threshold kill-switch. We prototype the approach on a 4-qubit superconducting tile with cryo-CMOS control and evaluate both depth-varied local-random circuits and small QAOA instances. Monitoring runs at a 6.3 microsecond control interval, and per-interval decisions are recorded in an append-only, hash-chained audit log. Across Monte Carlo (Tier 1) and cloud-hardware emulation (Tier 2) evaluations, NADGO maintains leakage within budget in nominal operation (interval-abort rate below 1 percent) and under attack yields high separation with concentrated aborts. At matched leakage targets, microbenchmarks indicate lower latency and cryogenic power consumption than static padding, while end-to-end workloads maintain competitive cost envelopes.