Skill UI

This skill automates the entire phishing incident response workflow using the Splunk SOAR REST API. It parses suspicious email headers and bodies to extract artifacts (URLs, IPs, sender details), creates a structured incident container, triggers a comprehensive automated investigation playbook, and compiles a final verdict report for security teams.

This skill analyzes ransomware cryptocurrency payment flows by utilizing major blockchain intelligence APIs (like Chainalysis and WalletExplorer). It identifies wallet clusters, tracks movements through obfuscation services (mixers, tumblers), and determines fund flows to exchanges, supporting forensic investigations and law enforcement attribution efforts.

This skill enforces a rigorous data validation protocol by establishing hard boundaries at every point data enters the system (API requests, user input, environment variables, webhooks, etc.). It mandates a four-phase process: detection, trust classification, mandatory filtering (schema validation, sanitization, type checking), and final verification. This prevents untrusted data from reaching core business logic, mitigating risks like SQL injection and XSS.

This skill demonstrates how to exploit the Windows Data Protection API (DPAPI) to extract sensitive, encrypted secrets, including saved user credentials, browser logins, RDP passwords, and vault data. It covers online, offline, and domain-wide decryption techniques, essential for post-exploitation red team engagements and penetration testing.

This guide details the architecture and deployment of resilient Command and Control (C2) redirectors. By using tools like Nginx and Apache, operators establish an intermediary layer that filters beacon traffic based on malleable C2 profiles. This setup protects the true team server from detection, takedown, and analysis (OPSEC), enabling advanced techniques like domain fronting and traffic shaping for low attribution.

This skill provides methods and frameworks to detect sophisticated AI attacks, including model stealing, membership inference, and model inversion. It focuses on monitoring query patterns, analyzing confidence exposure, and implementing defensive controls on inference APIs to protect intellectual property and data privacy.

This skill simulates high-impact container breakout techniques, demonstrating how to achieve root compromise on the underlying host operating system. It covers exploiting misconfigurations (e.g., privileged pods, hostPath mounts, exposed Docker sockets) and leveraging known vulnerabilities in runC/containerd, making it essential for authorized penetration testing and assessing the blast radius of a compromised microservice.

A comprehensive read-only client for querying the Go package discovery API (pkg.go.dev). It provides detailed documentation, API signatures, usage examples, version history, dependency graphs, and known CVE vulnerabilities for any Go module or package. Ideal for understanding external library usage, reviewing API details, or checking dependency health without needing write access.

Chainsaw is a fast, Rust-based tool for digital forensics and threat hunting. It enables analysts to rapidly perform Sigma rule checks and keyword searches across collected Windows Event Logs (.evtx) and specialized artifacts (like shimcache and SRUM). Ideal for first-response and offline analysis, it provides detection capabilities without needing a SIEM ingestion pipeline, allowing quick hypothesis testing in the field or air-gapped environments.

This PowerShell toolset performs advanced post-exploitation, reconnaissance, persistence, and data exfiltration against Microsoft 365/Entra ID tenants using the Microsoft Graph API. It operationalizes stolen Graph tokens to enumerate users, dump policies, escalate privileges, and search sensitive data across mailboxes, SharePoint, and Teams. Essential for authorized red teaming engagements.

This skill utilizes NVIDIA's open-source garak framework to conduct comprehensive red-teaming assessments on Large Language Models (LLMs). It tests for critical vulnerabilities such as prompt injection, jailbreaks, data leakage, and toxic content generation by sending thousands of adversarial probes. Ideal for pre-deployment security validation, API guardrail testing, and generating defensible evidence for AI risk assessments.

This guide provides a structured approach to red-teaming Large Language Models (LLMs) to detect sensitive data leakage from system prompts. It simulates advanced attacks—including prompt injection, instruction override, and encoding tricks—to identify embedded secrets, API keys, and proprietary business logic. Essential for validating adherence to OWASP LLM07 and securing AI applications against data exfiltration.