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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
A Quantitative Security Analysis of S-boxes in the NIST L...
Mahnoor Naseer, Sundas Tariq, Naveed Riaz, Naveed Ahmed, Shah Fa · 2024-04-09 · via cs.CR updates on arXiv.org

Lightweight cryptography was primarily inspired by the design criteria of symmetric cryptography. It plays a vital role in ensuring the security, privacy, and reliability of microelectronic devices without compromising the overall functionality and efficiency. However, the increasingly platform specific design requirements prompted the development of a standard lightweight algorithm. In 2017, NIST put forward security requirements for a standard lightweight scheme - security strength of at least 112 bits against known cryptanalysis attacks, mitigation against side channel and fault injection attacks, and implementation efficiency. After three rounds of review, ASCON was crowned as the winner of the competition. Evaluating the individual components used in any cryptographic algorithm is an important step in the verification of security claims. A fundamental component used to ensure Shannon's property of confusion in cryptographic primitives is an S-box. Hence, the quality of an S-box is a significant contributing factor in the security strength of a cipher. In this paper, we evaluate the S-boxes of 6 NIST LWC competition finalists based on well-known cryptographic properties, and comment on how the results reflect upon NIST security requirements. Our findings have revealed that these S-boxes do not comply with the basic notions of avalanche, making it vulnerable to high-order sophisticated cryptanalysis.