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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
Security Protocol Review Method Analyzer(SPRMAN)
A. S. Syed Navaz, H. Iyyappa Narayanan, R. Vinoth · 2013-08-23 · via cs.CR updates on arXiv.org

This Paper is designed using J2EE (JSP, SERVLET), HTML as front end and a Oracle 9i is back end. SPRMAN is been developed for the client British Telecom (BT) UK., Telecom company. Actually the requirement of BT is, they are providing Network Security Related Products to their IT customers like Virtusa,Wipro,HCL etc., This product is framed out by set of protocols and these protocols are been associated with set of components. By grouping all these protocols and components together, product is been developed. After framing out the product, it is been subscribed to their individual customers. Once a customer subscribed the product, then he will be raising a request to the client (BT) for updating any policy or component in the product. The customer has been given read/write access to the subscribed product. The customer user having read/write access is only allowed to raise a request for the product, but not the user having only the read access. The group of request is been managed as manage work queue in client area. Management of this protocol inside the product is considering as Security Protocol Review Method Analyzer. SPRMAN helps BT to overcome all the hurdles faced by them while processing the requests of their various clients using their already existing software applications. SPRMAN emphasizes on nature of the request and gives priority to issues based on their degree of future consequences. Thus SPRMAN builds a good relationship between BT and its customers.