Skill UI

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 evaluate model resilience against jailbreaks, data exfiltration, and harmful outputs. It helps identify exploitable failure modes before attackers do, suitable for new LLM features, third-party model evaluation, and compliance audits. Includes adversarial prompt libraries and automated testing scripts for security assessments.

This skill helps secure Large Language Model (LLM) and AI deployments against various threats including prompt injection, data exfiltration, model theft, and supply chain attacks. It covers implementing guardrails, output filtering, PII scrubbing, and API security measures to ensure compliance with standards like SOC2 or ISO 27001.

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.