ai-security

Security for LLM chat apps and agent systems - direct and indirect prompt injection, untrusted content boundaries, least-privilege tool permissions, output handling and sanitisation, data exfiltration channels, PII and secrets in prompts and logs, tenant isolation in RAG, jailbreaks and abuse, rate and cost abuse, human approval for risky actions, and red-teaming with regression cases. Load when exposing tools to a model, ingesting external or user documents, designing agent permissions, handling model output, or reviewing an AI feature for risk.