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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
Securing the Insecure: A First-Line-of-Defense for Nanosc...
Waqas Aman, M. Mahboob Ur Rahman, Hassan T. Abbas, Muhammad Arsl · 2020-07-14 · via cs.CR updates on arXiv.org

Nanoscale communication systems operating in Ter-ahertz (THz) band are anticipated to revolutionise the healthcaresystems of the future. Global wireless data traffic is undergoinga rapid growth. However, wireless systems, due to their broad-casting nature, are vulnerable to malicious security breaches. Inaddition, advances in quantum computing poses a risk to existingcrypto-based information security. It is of the utmost importanceto make the THz systems resilient to potential active and passiveattacks which may lead to devastating consequences, especiallywhen handling sensitive patient data in healthcare systems. Newstrategies are needed to analyse these malicious attacks and topropose viable countermeasures. In this manuscript, we presenta new authentication mechanism for nanoscale communicationsystems operating in THz band at the physical layer. We assessedan impersonation attack on a THz system. We propose usingpath loss as a fingerprint to conduct authentication via two-stephypothesis testing for a transmission device. We used hiddenMarkov Model (HMM) viterbi algorithm to enhance the outputof hypothesis testing. We also conducted transmitter identificationusing maximum likelihood and Gaussian mixture model (GMM)expectation maximization algorithms. Our simulations showedthat the error probabilities are a decreasing functions of SNR. At 10 dB with 0.2 false alarm, the detection probability was almostone. We further observed that HMM out-performs hypothesistesting at low SNR regime (10% increase in accuracy is recordedat SNR =5 dB) whereas the GMM is useful when groundtruths are noisy. Our work addresses major security gaps facedby communication system either through malicious breachesor quantum computing, enabling new applications of nanoscalesystems for Industry 4.0.