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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
QubitHammer: Remotely Inducing Qubit State Change on Supe...
Yizhuo Tan, Navnil Choudhury, Kanad Basu, Jakub Szefer · 2025-04-10 · via cs.CR updates on arXiv.org

To address the rapidly growing demand for cloud-based quantum computing, various researchers are proposing shifting from the existing single-tenant model to a multi-tenant model that expands resource utilization and improves accessibility. However, while multi-tenancy enables multiple users to access the same quantum computer, it introduces potential for security and reliability vulnerabilities. It therefore becomes important to investigate these vulnerabilities, especially considering realistic attackers who operate without elevated privileges relative to ordinary users. To address this research need, this paper presents and evaluates QubitHammer, the first attack to demonstrate that an adversary can remotely induce unauthorized changes to a victim's quantum circuit's qubit's state within a multi-tenant model by using custom qubit control pulses that are generated within constraints of the public interfaces and without elevated privileges. Through extensive evaluation on real-world superconducting devices from IBM and Rigetti, this work demonstrates that QubitHammer allows an adversary to significantly change the output distribution of a victim quantum circuit. In the experimentation, variational distance is used to evaluate the magnitude of the changes, and variational distance as high as 0.938 is observed. Cross-platform analysis of QubitHammer on a number of quantum computing devices exposes a fundamental susceptibility in superconducting hardware. Further, QubitHammer was also found to evade all currently proposed defenses aimed at ensuring reliable execution in multi-tenant superconducting quantum systems.