Skill UI

Guides systematic detection and exploitation of cross-site scripting and HTML injection flaws, covering stored/reflected/DOM vectors, payload crafting, and remediation advice for web security assessments.

LangChain accelerates building LLM-powered agents, RAG pipelines, and chatbots by orchestrating models, chains, tools, memory, and vector stores across major providers for rapid prototyping or production deployment.

This module implements comprehensive Role-Based Access Control (RBAC) for multi-tenant LangChain applications. It provides granular permission models for controlling model access, tool execution, and chain invocation. Key features include multi-tenant data isolation in vector stores, enforcing usage quotas (token limits), and integrating middleware for robust security enforcement. Ideal for building secure, scalable, and compliant enterprise LLM systems.

Guides systematic detection and exploitation of cross-site scripting and HTML injection flaws, covering stored/reflected/DOM vectors, payload crafting, and remediation advice for web security assessments.

This skill guides the secure implementation of AES-256 in GCM mode (FIPS 197) for protecting files and data stores at rest. It covers best practices for key derivation from passwords (using PBKDF2/Argon2), proper management of initialization vectors (IVs)/nonces, and applying authenticated encryption (AEAD) to detect tampering. Use this when establishing robust security controls or performing security assessments.

This skill provides a comprehensive solution for monitoring brand impersonation attacks across multiple digital vectors. It systematically scans domains, social media platforms, mobile app stores, and the dark web to detect phishing campaigns, typosquatting, and unauthorized brand usage. It is essential for security teams conducting proactive threat intelligence gathering and vulnerability assessments to safeguard brand integrity.

This low-level SDK enables .NET developers to create and manage persistent AI agents on Azure. It supports threads, messages, runs, and tools integration. Includes features for function calling, vector store management, and file search. Ideal for building complex AI applications with C# and Azure services.

Identify and exploit vector-store and embedding-layer weaknesses in RAG pipelines, including persistent corpus poisoning and cross-tenant IDOR, for authorized security assessments.