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 guide details how to detect advanced process injection techniques (T1055) by analyzing rich Sysmon telemetry. It focuses on identifying cross-process memory operations, such as remote thread creation (Event 8), suspicious process access (Event 10), and memory divergence (ProcessTampering/Event 25), which are hallmarks of DLL injection and process hollowing. Ideal for threat hunters and security analysts investigating sophisticated defense evasion.

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.

A comprehensive guide to deploying FIDO2/WebAuthn passwordless authentication using security keys and biometric platform authenticators. It covers crucial steps such as WebAuthn API integration, FIDO2 server configuration, and passkey enrollment, ensuring secure migration away from traditional password-based systems and aligning with stringent compliance standards like NIST SP 800-63B AAL3.

A multi-layered defense system designed to detect sophisticated prompt injection attacks targeting LLM-powered applications. It combines regex pattern matching for known signatures, heuristic scoring for structural anomalies, and advanced DeBERTa-based classification. Ideal for building robust input validation layers, securing RAG pipelines, and performing proactive AI security scanning.

This framework detects NTLM relay attacks across Active Directory environments by correlating Windows Security Event 4624 (LogonType 3). It identifies key indicators such as IP-to-hostname mismatches, Responder/LLMNR poisoning artifacts, and non-enforced SMB/LDAP signing, providing comprehensive threat detection for T1557.001.

This framework implements robust input and output validation guardrails for LLM-powered applications. It prevents prompt injection, detects and redacts PII, filters toxic content, and validates model outputs against defined schemas. Use it when deploying AI agents that handle sensitive data or require strict adherence to corporate policies, ensuring a secure and trustworthy AI interaction layer.

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.

This skill provides comprehensive security and monitoring for Large Language Model (LLM) powered trading agents. It helps to audit trade decisions, ensure compliance with risk parameters, and mitigate potential risks associated with automated financial operations.