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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
ESPM-D: Efficient Sparse Polynomial Multiplication for Di...
Jieyu Zheng, Hong Zhang, Le Tian, Zhuo Zhang, Hanyu Wei, Zhiwei · 2024-04-19 · via cs.CR updates on arXiv.org

Dilithium is a lattice-based digital signature scheme standardized by the NIST post-quantum cryptography (PQC) project. In this study, we focus on developing efficient sparse polynomial multiplication implementations of Dilithium for ARM Cortex-M4 and Apple M2, which are both based on the ARM architecture. The ARM Cortex-M4 is commonly utilized in resource-constrained devices such as sensors. Conversely, the Apple M2 is typically found on mobile devices, emphasizing high performance and versatility. Accordingly, our optimization strategies differ between ARM Cortex-M4 and Apple M2. We prioritize optimizing stack usage for the former while enhancing computational efficiency for the latter. Our optimized sparse polynomial multiplication achieves significant speedups of up to 30% on ARM Cortex-M4 and 55% on Apple M2 compared to the state-of-the-art Number-Theoretic Transform (NTT) implementation. Additionally, we integrate the sparse polynomial multiplication with the infinity norm judgments in the Dilithium signing process, further enhancing signing efficiency. Our optimized implementation not only reduces stack usage by 10.8%, 1.2%, and 7.7% in the signing procedure of Dilithium2, Dilithium3, and Dilithium5, respectively, but also enhances signing performance by 0.4% to 0.8% compared to the state-of-the-art ARM Cortex-M4 implementation. Furthermore, we optimize polynomial sampling, rounding functions, and polynomial packing and unpacking using ARM Cortex-M4 DSP instructions, resulting in a 0.4%-3.2% improvement in key generation and verification procedures. On the MacBook Air 2022, our Dilithium implementation achieves 10% to 11% speedups in the signing procedure. To the best of our knowledge, our work sets new performance records for Dilithium on both ARM Cortex-M4 and Apple M2 platforms.