惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

WordPress大学
WordPress大学
MyScale Blog
MyScale Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
人人都是产品经理
人人都是产品经理
C
Check Point Blog
宝玉的分享
宝玉的分享
B
Blog RSS Feed
博客园 - 三生石上(FineUI控件)
量子位
Martin Fowler
Martin Fowler
酷 壳 – CoolShell
酷 壳 – CoolShell
Jina AI
Jina AI
IT之家
IT之家
阮一峰的网络日志
阮一峰的网络日志
博客园 - 叶小钗
J
Java Code Geeks
The Cloudflare Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
腾讯CDC
P
Proofpoint News Feed
美团技术团队
H
Help Net Security
B
Blog
博客园_首页

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
Securing HHL Quantum Algorithm against Quantum Computer A...
Yizhuo Tan, Hrvoje Kukina, Jakub Szefer · 2024-10-10 · via cs.CR updates on arXiv.org

As the quantum research community expands and new quantum algorithms are created and implemented, it is essential to consider the security implications and potential threats that could lead to the compromise the information processed by them. This work focuses on securing the HHL quantum algorithm against attacks while it executes on a quantum computer. Specifically, two types of potential attacks could be deployed on a cloud-based quantum computer by an attacker circuit attempting to interfere with the victim HHL circuit: the Improper Initialization Attack (IIA) and the Higher Energy Attack (HEA). To protect the HHL algorithm from IIA and HEA, this work proposes first-of-a-kind defense strategies against these attacks on the HHL quantum algorithm. Next, this work demonstrates an implementation of a new quantum circuit for the HHL quantum algorithm that incorporates these defenses. The redesigned quantum circuit is necessary to successfully apply and realize all proposed defense strategies. Finally, this work illustrates how these defense strategies function in practice in the redesigned circuit, specifically how they can protect the HHL quantum algorithm from both IIA and HEA across multiple qubits involving all three types of qubits used in the HHL algorithms: ancilla, clock, and b. The defense requires minimal modification to the circuit, and has only a very small effect on the fidelity of the circuits. The circuits have been tested and validated in both simulation, and also on real IBM quantum computer hardware. The work further analyzes how the modified HHL circuit with the defenses is affected by noise during quantum computation. This work in the end demonstrates that it is practical to add protections to quantum circuits so that they not only perform correct computation, but also self-detect if an attack has occured during the execution.