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It's shorthand for the emerging multi-agent AI ecosystem — autonomous agents that call tools, call each other, and take actions without a human approving each step. The term varies; the architecture and its risks don't.
No. Traditional AI security largely focused on the model — adversarial inputs, data poisoning, model theft. Agentic security is about what the model can do: which tools it can call, which agents it can talk to, and what happens when that access gets abused or chained. Training built around the old framing misses most of this.
Trust inheritance across agent handoffs. When one agent passes a task to another without re-verifying scope and permissions, you get the equivalent of broken object-level authorization, but across an entire decision chain instead of one API call.
The design-level ones, no. SAST scans code paths; it doesn't model the decision paths an agent can take at runtime based on context or tool results. These tools still matter for the code around the agent — they just don't cover the agent's behavior itself, which is exactly what hands-on training needs to fill in.
When the model has tool access — file systems, APIs, databases, deploy pipelines — a successful injection isn't bad output, it's an attacker's instruction executing with the agent's permissions. The severity scales with what the agent is allowed to touch.
Map the decision graph instead of just the data flow: what triggers the agent, what tools and data it can access, what other agents or systems it can call, and where trust gets assumed instead of checked at each step.
Both. Developers are the ones wiring up tool-calling schemas, MCP servers, and agent permissions, often under deadline, often without a security review built into that workflow. The vulnerability gets introduced at build time. Training needs to reach builders, not just reviewers.
Real sandboxes where you build, attack, and defend tool-using agents. You watch an agent take a tool call it shouldn't have, trace why, and fix the authorization boundary, in an environment built for exactly that.
As often as the tooling changes, which right now is faster than most training cycles. A program built around static modules will lag by design. One built around live, updatable labs has a chance of keeping pace.

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Koushik M.
"Exceptional Hands-On Security Learning Platform"

Varunsainadh K.
"Practical Security Training with Real-World Labs"

Gaël Z.
"A new generation platform showing both attacks and remediations"

Nanak S.
"Best resource to learn for appsec and product security"





.png)



Koushik M.
"Exceptional Hands-On Security Learning Platform"

Varunsainadh K.
"Practical Security Training with Real-World Labs"

Gaël Z.
"A new generation platform showing both attacks and remediations"

Nanak S.
"Best resource to learn for appsec and product security"




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