Skill UI

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.

A comprehensive checklist for ensuring LangChain applications are robust, secure, and scalable for production environments. It covers critical areas including configuration management, advanced error handling, observability setup (e.g., LangSmith), performance optimization, security best practices (like prompt injection prevention and PII handling), rigorous testing, and zero-downtime deployment strategies. Use this guide when preparing for a major launch or auditing existing LLM systems.

This guide outlines essential security practices for deploying production-grade LLM applications using LangChain. It covers secure secrets management, defending against prompt injection, implementing sandboxed tool execution, validating model outputs for sensitive data, and setting up comprehensive audit logging, ensuring robust and secure AI deployment.

This guide provides essential security best practices for implementing Langfuse, an LLM observability platform. It covers critical areas such as secure credential management (public vs. secret keys), implementing PII scrubbing techniques before tracing, hardening self-hosted environments, and enforcing the principle of least privilege. Use this guide to ensure your LLM integrations meet strict data privacy standards (e.g., GDPR, HIPAA).

This skill helps detect NTLM relay attacks (T1557.001) by analyzing Windows Security Event ID 4624 (logon type 3) for NTLMSSP usage. It flags critical anomalies such as IP-to-hostname mismatches, evidence of LLMNR/NBT-NS poisoning, rapid cross-domain authentication, and verifies SMB signing status. It is essential for proactive threat hunting and security incident investigation in Active Directory environments.

This guide provides comprehensive security hardening procedures for enterprise Google Workspace environments. It covers advanced topics such as enforcing phishing-resistant Multi-Factor Authentication (MFA), configuring email authentication (DMARC/SPF/DKIM), implementing Data Loss Prevention (DLP), and securing super admin accounts using Advanced Protection Program enrollment. Ideal for achieving high compliance and defending against BEC and phishing attacks.

Implement robust, phishing-resistant authentication using FIDO2/WebAuthn standards. This guide covers integrating WebAuthn APIs, configuring FIDO2 servers, managing passkey enrollment, and migrating systems away from traditional passwords. Essential for meeting high-assurance security standards like NIST SP 800-63B AAL3.

A comprehensive security tool designed to detect prompt injection attacks targeting Large Language Models (LLMs). It employs a multi-layered approach combining regex signature matching, structural anomaly scoring, and advanced DeBERTa transformer classification. It is essential for securing input validation layers in chatbots, AI agents, and RAG pipelines, and for retrospectively auditing LLM interaction logs against OWASP standards.

This skill provides a comprehensive framework for detecting NTLM relay attacks (T1557.001). It achieves this by correlating Windows Security Event 4624 (LogonType 3) to identify IP-to-hostname mismatches, detecting artifacts from Responder/LLMNR poisoning, and auditing SMB/LDAP protocol signing weaknesses. Use it for proactive threat hunting in Active Directory environments, investigating authentication anomalies, or building SIEM correlation rules.

A comprehensive guide for building a FIDO2/WebAuthn relying party server using python-fido2. This skill covers the entire lifecycle of passwordless authentication, including secure credential registration (enrollment of hardware keys like YubiKey and resident passkeys) and robust authentication ceremonies. It is essential for migrating traditional password-based systems to phishing-resistant Multi-Factor Authentication (MFA).

This framework implements robust guardrails for LLM applications, validating both user inputs and model outputs. It detects prompt injection, sensitive PII (Personally Identifiable Information), toxic content, and ensures structured output compliance using advanced tools like NVIDIA NeMo Guardrails and Guardrails AI. Essential for building secure, reliable chatbots and RAG systems.

Assesses AI/ML systems for prompt injection, jailbreak, model inversion, data poisoning, and agent tool abuse using MITRE ATLAS mapping, signature detection, and scoring metrics before deployment.