Results for “irail”

Assess AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, and agent tool abuse, with MITRE ATLAS mapping and guardrail recommendations.

Add programmable safety guardrails to LLM applications at runtime, including jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, and toxicity detection.

Implements AI safety guardrails including input validation, output filtering, robustness testing, human oversight, and monitoring to prevent harmful outputs and ensure system reliability.

Adds input and output guardrails to an agent, including validation, content filtering, domain scoping, and rate limiting, with TypeScript examples and wiring instructions.

Builds input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs using NeMo Guardrails, Presidio, and Guardrails AI.

Use this skill when the user asks about International Fuel Tax Agreement (IFTA) compliance — quarterly returns, jurisdiction reporting, fuel + miles reconciliation, IFTA-100/101 forms, base jurisdiction selection, IFTA license + decals, recordkeeping requirements, common IFTA audit findings, or how to handle non-IFTA jurisdictions. Cite IFTA Articles of Agreement.

Safety guardrails for autonomous agents. Intercepts destructive actions and vets skills, requires structured approval before irreversible operations, with CLI-backed cryptographic audit trails and policy enforcement.

Identify and exploit Insecure Direct Object Reference (IDOR) or Broken Object Level Authorization (BOLA) vulnerabilities specifically within GraphQL APIs. This skill focuses on manipulating node IDs, changing variables, and utilizing aliases to access unauthorized data.