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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
Cyber Situation Awareness Monitoring and Proactive Respon...
Hootan Alavizadeh, Hooman Alavizadeh, Julian Jang-Jaccard · 2020-09-03 · via cs.CR updates on arXiv.org

The cloud model allows many enterprises able to outsource computing resources at an affordable price without having to commit the expense upfront. Although the cloud providers are responsible for the security of the cloud, there are still many security concerns due to inherently complex model the cloud providers operate on (e.g.,multi-tenancy). In addition, the enterprises whose services have migrated into the cloud have a preference for their own cybersecurity situation awareness capability on top of the security mechanisms provided by the cloud providers. In this way, the enterprises can monitor the performance of the security offerings of the cloud and have a choice to decide and select potential response strategies more appropriate to the enterprise in the presence of the attack where the defense provided by the cloud doesn't work for them. However, some response strategies, such as Moving Target Defense (MTD) techniques shown to be effective to secure cloud, cannot be deployed by the enterprise themselves. In this paper, we propose a framework that enables better collaboration between enterprises and cloud providers. Our proposed framework, which offers more in-depth security analysis based on the set of most advanced security metrics, allows the security experts of the enterprise to obtain better situational awareness in the cloud. With better and more effective situation awareness of cloud security, our framework can support better decision making and further allows to deploy more appropriate threat responses to protect the outsourced resources. We also propose a secure protocol which can facilitate more secure communication between the enterprises and cloud provider. Using our proposed secure protocol, which is based on authentication and key exchange mechanism, the enterprises can send a secure request to the cloud provider to perform a selected defensive strategy.