Skill UI

This tool guides the process of reviewing security findings from DAST and SAST scanners. It uses the OWASP risk rating methodology to classify vulnerabilities (true positive, false positive, needs review) and calculate a comprehensive risk score based on likelihood and impact, ensuring development teams prioritize the most critical issues.

A comprehensive framework for testing REST and GraphQL APIs against Broken Object Level Authorization (BOLA) and Insecure Direct Object Reference (IDOR). This tool simulates an attacker's behavior by systematically intercepting and modifying object identifiers (IDs, UUIDs, slugs) within API calls. It helps identify critical authorization flaws, ensuring that an authenticated user cannot access or modify resources belonging to other tenants or users, adhering to OWASP API Security Top 10.

This tool systematically tests API endpoints for mass assignment (over-posting) vulnerabilities. It simulates an attacker injecting undocumented or privileged fields (such as 'role' or 'balance') into standard API requests. By analyzing the server's response, it determines if the API binds all client-supplied parameters to the data model without proper filtering, which could allow unauthorized privilege escalation or data modification. This is critical for achieving OWASP API Top 10 compliance.

This comprehensive guide implements External Attack Surface Management (EASM). It integrates leading industry tools like Shodan, Censys, subfinder, httpx, and nuclei for deep reconnaissance. Users can systematically discover assets, enumerate subdomains, fingerprint services, scan for vulnerabilities, and calculate weighted exposure scores based on professional OWASP methodology. Ideal for continuous security monitoring and deep penetration testing.

Checks AI agent implementations against the OWASP Agentic Security Initiative Top 10 risks, producing findings on controls, vulnerabilities, and recommendations for prompt injection, tool governance, escalation, logging, identity, policy, supply chain, and behavioral monitoring before production or audit.

This framework implements the OWASP Top 10 standards, specifically tailored for Agentic Applications. It provides a standardized methodology to classify and map diverse security findings—such as prompt injection, tool misuse, and privilege abuse—to ten core risk categories (ASI01-ASI10). It is essential for security teams to systematically assess, prioritize, and remediate vulnerabilities inherent in complex AI agent systems.

A comprehensive methodology for identifying critical vulnerabilities in transactional applications (e-commerce, fintech, SaaS). This goes beyond standard OWASP Top 10 checks, focusing on flaws like workflow bypass, state machine violations, price manipulation, and role boundary abuse. It guides testers to map application flows, identify hidden endpoints, and quantify the potential financial impact of logic failures.

A comprehensive CI/CD pipeline for Quarkus applications, designed to verify code quality and deployment readiness. It covers all phases: build, static code analysis (SonarQube, Checkstyle), rigorous functional testing (Unit, Integration, API), security scanning (OWASP, dependency checks), native image compatibility testing, performance load testing (K6), and container image validation (Trivy, Grype).

This tool provides a comprehensive security assessment framework for Retrieval-Augmented Generation (RAG) systems and vector databases. It tests critical vulnerabilities such as embedding inversion, cross-tenant data leakage, knowledge-base poisoning, and retrieval manipulation, aligning with OWASP LLM08:2025 standards. It is essential for validating the security posture of multi-tenant and vector-search applications.

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.

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.