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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
Co-Simulation Framework For Network Attack Generation and...
Oceane Bel, Joonseok Kim, William J Hofer, Manisha Maharjan, Sum · 2023-07-01 · via cs.CR updates on arXiv.org

Resilience assessment is a critical requirement of a power grid to maintain high availability, security, and quality of service. Most grid research work that is currently pursued does not have the capability to have hardware testbeds. Additionally, with the integration of distributed energy resources, the attack surface of the grid is increasing. This increases the need for reliable and realistic modeling techniques that are usable by the wider research community. Therefore, simulation testbeds have been used to model a real-world power grid topology and measure the impact of various perturbations. Existing co-simulation platforms for powergrid focus on a limited components of the overall system, such as focusing only on the dynamics of the physical layer. Additionally a significant number of existing platforms need specialized hardware that may be too expensive for most researchers. Finally, not many platforms support realistic modeling of the communication layer, which requires use of Supervisory Control and Data Acquisition communication protocol such as DNP3 while modeling cybersecurity scenarios. We present Network Attack Testbed in [Power] Grid (NATI[P]G), (pronounced natig), a standalone, containerized, and reusable environment to enable cyber analysts and researchers to run different cybersecurity and performance scenarios on powergrid. Our tool combines GridLAB-D, a grid simulator, HELICS, a co-simulation framework, and NS-3, a network simulator, to create an end-to-end simulation environment for the power grid. We demonstrate use cases by generating a library of datasets for several scenarios. These datasets can be used to detect cyberattacks at the cyber layer, and develop counter measures to these adverse scenarios.