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Secure LLM-powered applications with input validation, output controls, tenant isolation, and abuse prevention.
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Based on SOC occupation classification
Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls.
Run structured AI red team exercises for jailbreak resistance, data exfiltration risk, harmful output controls, and agent tool abuse resilience.
Searches and explores Burp Suite project files (.burp) from the command line. Use when searching response headers or bodies with regex patterns, extracting security audit findings, dumping proxy history or site map data, or analyzing HTTP traffic captured in a Burp project.
Use when assessing security for Python FastAPI applications. Focuses on Pydantic exploitation, dependency injection gaps, OpenAPI exposure, ASGI middleware security, and Python-specific vulnerabilities like template injection and SSRF.
Security assessment methodology for Google Firebase applications, covering Firestore, Realtime Database, Cloud Storage, and Cloud Functions.
Use when testing GraphQL APIs for common vulnerabilities including introspection exploitation, authorization bypasses, batching abuse, and denial of service. Covers endpoint discovery, schema analysis, and exploitation of GraphQL-specific features like federation and directives.
| name | llm-app-security |
| description | Secure LLM-powered applications with input validation, output controls, tenant isolation, and abuse prevention. |
| license | MIT |
| metadata | {"author":"devops-skills","version":"1.0"} |
Harden chatbots and AI features embedded in web and mobile products.
