14 lessons · take it step by step
AI Security
Protect application trust boundaries, authorized data and tool effects with tested policies and explicit limits.
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AI Threat Modeling & Trust Boundaries
25–40 minA RAG answer can expose documents; an agent can change resources.
Direct & Indirect Prompt Injection
25–40 minDirect input, a retrieved document, a tool result or repository text may contain instructions that conflict with the task.
Sensitive Data & Exfiltration
25–40 minIdentify secrets, personal records and restricted documents before a model call.
Secure Output Handling
25–40 minA model can return valid JSON containing an unauthorized resource, a dangerous URL or invented SQL.
Secure RAG: Access & Ingestion
25–40 minA document belongs to a tenant and requires a trusted approval decision before retrieval.
Agent Tool Authorization & Approval
25–40 minTreat model tool arguments as untrusted input.
Resource, Cost & Availability Abuse
25–40 minLimit input size, model steps, tool attempts and fan-out before launching work.
AI Security Evaluation & Release Gates
25–40 minA final response saying “I did not access it” is not evidence of authorization enforcement.
Model, Tool & Dependency Supply Chain
25–40 minModel files, datasets, tool packages and CI actions are distinct supply-chain inputs.
Memory & Context Poisoning
25–40 minAn untrusted instruction stored as memory can influence later runs after the original document is gone.
MCP & Connector Security
25–40 minAn MCP client name, server description or declared capability is not authenticated permission.
Sandbox, Egress & Execution Isolation
25–40 minAvoid exposing shell, arbitrary file access and generic network execution when the task does not need them.
Monitoring & AI Incident Response
25–40 minCapture sanitized event codes, scoped request identifiers, policy versions and effect outcomes.
Agent-Assisted SDLC Security
25–40 minAn issue, dependency README, error log or skill may contain attacker-authored instructions.