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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
Energy-Efficient NTT Sampler for Kyber Benchmarked on FPGA
Paresh Baidya, Rourab Paul, Vikas Srivastava, Sumit Kumar Debnat · 2025-05-03 · via cs.CR updates on arXiv.org

Kyber is a lattice-based key encapsulation mechanism selected for standardization by the NIST Post-Quantum Cryptography (PQC) project. A critical component of Kyber's key generation process is the sampling of matrix elements from a uniform distribution over the ring Rq . This step is one of the most computationally intensive tasks in the scheme, significantly impacting performance in low-power embedded systems such as Internet of Things (IoT), wearable devices, wireless sensor networks (WSNs), smart cards, TPMs (Trusted Platform Modules), etc. Existing approaches to this sampling, notably conventional SampleNTT and Parse-SPDM3, rely on rejection sampling. Both algorithms require a large number of random bytes, which needs at least three SHAKE-128 squeezing steps per polynomial. As a result, it causes significant amount of latency and energy. In this work, we propose a novel and efficient sampling algorithm, namely Modified SampleNTT, which substantially educes the average number of bits required from SHAKE-128 to generate elements in Rq - achieving approximately a 33% reduction compared to conventional SampleNTT. Modified SampleNTT achieves 99.16% success in generating a complete polynomial using only two SHAKE-128 squeezes, outperforming both state-of-the-art methods, which never succeed in two squeezes of SHAKE-128. Furthermore, our algorithm maintains the same average rejection rate as existing techniques and passes all standard statistical tests for randomness quality. FPGA implementation on Artix-7 demonstrates a 33.14% reduction in energy, 33.32% lower latency, and 0.28% fewer slices compared to SampleNTT. Our results confirm that Modified SampleNTT is an efficient and practical alternative for uniform polynomial sampling in PQC schemes such as Kyber, especially for low-power security processors.