Skill UI

Offensive cloud security methodology for AWS, Azure, and GCP. Covers identity discovery, credential harvesting, enumeration, privilege escalation, persistence, data exfiltration, lateral movement, serverless abuse, Kubernetes paths, and detection evasion during authorized assessments.

A comprehensive expert playbook detailing advanced Large Language Model (LLM) prompt injection techniques. Covers direct methods (instruction override, role play), indirect attacks (RAG poisoning, web data manipulation), and sophisticated tool/function calling abuse for data exfiltration and security bypass. Essential for rigorous AI application security testing.

This skill audits the security and integrity of legal AI components, including skills, prompts, workflows, and tools. It proactively detects prompt injection, malicious code, data exfiltration paths, and supply-chain risks before deployment or trust. Essential for maintaining confidentiality, privilege, and adhering to legal standards in AI environments.

This skill provides defense-in-depth security controls for AI agents, covering prompt injection detection, input validation, tool abuse prevention, data exfiltration mitigation, and privilege escalation protection. It includes threat modeling with STRIDE, code examples for input sanitization, and guidance for secure agent deployment.

This skill enables structured AI red team exercises to test for jailbreak resistance, data exfiltration risks, and harmful output controls. It helps discover exploitable failure modes before attackers do, suitable for new LLM features, third-party model evaluation, and security audits. Includes Python scripts for automated testing against various attack vectors like prompt injection and tool abuse.

Secure LLM and AI systems against prompt injection, data exfiltration, model theft, and supply chain attacks in production environments. This skill covers input sanitization, output filtering, PII scrubbing, and API endpoint protection to ensure compliance with SOC2 or ISO 27001 standards.

This skill focuses on identifying security vulnerabilities in Large Language Model (LLM) and AI features. It covers prompt injection, system prompt leakage, cross-tenant access, and data exfiltration techniques. Users must verify findings against false positives using reproducibility checks and out-of-band callbacks before reporting.

Detects data exfiltration via URL path encoding and chained web_fetch navigation, covering fake trusted UI injection, indirect prompt injection, letter-level path exfiltration, and multi-hop navigation hijacking in agent memory/context scenarios.