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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
Universal Session Protocol: Mitigating Unauthenticated Re...
Jonathon Anderson · 2023-06-26 · via cs.CR updates on arXiv.org

Currently, the TCP/IP model enables exploitation of vulnerabilities anonymously by unconditionally fulfilling every request for a connection into an application; the model only incorporates authentication within applications themselves, rather than as a precondition for access into applications. I am proposing the Universal Session Protocol as a change to the architecture of the TCP/IP model to include a session layer featuring a structured generalized process for authentication negotiation and fulfillment. The Universal Session Protocol addresses an urgent and vital need to eliminate unauthenticated data processing on security critical systems. Previous work regarding TCP/IP security has focused on the application design and implementation and existing protocol layers, but has failed to posit the addition of a session layer as a mitigating control. Failing to implement a distinct authentication layer leaves every resource connected to the global Internet, including life and security critical infrastructure, vulnerable to attacks from anonymous and untraceable sources. The Universal Session Protocol provides a solution by establishing a TCP/IP Session Layer that explicitly provides authentication before a data stream is accessible within an application. After authentication, an identity is associated with the data stream so that all data may be related back to that identity for forensic purposes. If authentication fails, the application will never process user data, rendering the service safe from anonymous bad actors.