Skill UI

Use Mailtrap Email Sandbox to capture and inspect outbound emails without sending them to real recipients. This is crucial for development, staging, and CI/CD environments. It allows developers to test email content, headers, attachments, and spam scores using dedicated APIs, SMTP settings, or SDKs, ensuring mail integrity before deployment. Ideal for automation and rigorous quality checks.

A comprehensive, pre-deployment, defense-in-depth security audit tool for Solidity smart contracts built with Foundry. It combines static analysis (Slither, Aderyn), symbolic execution (Mythril), property-based fuzz testing, and key hygiene checks to proactively detect critical vulnerabilities like reentrancy, access control flaws, and arithmetic bugs before deployment to any EVM chain.

This guide details the architecture and implementation of a ransomware-resilient backup strategy, adhering to the extended 3-2-1-1-0 methodology. It covers critical steps including defining RPO/RTO, establishing immutable/air-gapped copies, isolating backup credentials from the production domain, and automating periodic restore testing. Use this framework to ensure business continuity against sophisticated cyberattacks.

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.

Automate security regression testing for Large Language Model (LLM) applications by integrating Promptfoo and DeepTeam into CI/CD pipelines. This skill enforces a critical security gate, automatically testing endpoints against OWASP LLM Top 10, OWASP Agentic threats, and common jailbreaks/prompt injections. It ensures that security patches do not regress over time due to prompt or model changes, providing continuous risk monitoring.

Stratus Red Team is an open-source tool designed for detection engineering and purple teaming. It programmatically detonates granular, documented offensive techniques across major cloud platforms (AWS, Azure, GCP, and Kubernetes). It validates existing security detections (like CloudTrail/GuardDuty) by simulating real attacker activity in a controlled, reversible environment. The tool ensures deterministic lifecycles, guaranteeing that all created resources are cleaned up after testing.

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 powerful offensive security toolkit for red-teaming and penetration testing. It systematically enumerates and exploits various misconfigurations within Active Directory Certificate Services (AD CS) using Certipy. This allows attackers to forge privileged certificates, perform PKINIT authentication, and achieve domain compromise and privilege escalation, even without knowing user passwords. Ideal for validating security hardening.

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 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.

A comprehensive red-teaming skill for assessing the security posture of Retrieval-Augmented Generation (RAG) pipelines. It systematically probes two critical injection surfaces: poisoned retrieved context (indirect prompt injection) and embedding manipulation. Use this skill to validate retrieval guardrails, ensure data leak prevention, and demonstrate vulnerabilities in LLM-powered knowledge assistants.