Agentic AI Security: Red Team, Prompt Injection, 93 Tools

About this course

This course contains the use of artificial intelligence. 93 tools for security testing of AI agents in one short course: 78 ready-to-use record templates, checklists and test procedures, 8 downloadable tool files and 7 models. Open a lecture, take the file, run the test in the supplied sandbox. Why prompt testing is not enough for agents Traditional prompt testing often ends when the model returns a response. An AI agent can fail later: after a retrieved document changes its goal, after an allowed tool result feeds another call, after a poisoned memory record returns in a new session, or after one peer passes tainted authority to another. A final refusal can hide an earlier planning deviation, and a successful tool block can contain impact without fixing the source. Effective agentic red-teaming follows the complete trajectory. What changes after this course You move from isolated prompts to bounded security experiments. You define protected properties before each run, compare clean and adversarial conditions from the same frozen state, keep the causal links between events, and verify both containment and retained utility. You can name the first changed field, the control that allowed or blocked it, the downstream components affected and the final simulated state. Safe by design Every exercise uses a fictional security operations workflow and a synthetic incident. The environment contains mock identities, fake canaries, local sinks, reversible state, deterministic scripts and explicit stop conditions. No exercise requires production credentials, customer data, public targets, destructive payloads or third-party systems. The Sandboxed Agent Lab is a self-contained browser file with no external network, no model call and no live integration. What is inside: 93 tools, counted lecture by lecture You do not have to take this number on trust.

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