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Confused deputy

A well-documented computer security failure, named by Norman Hardy in 1988, in which a system with legitimate authority is tricked, through an otherwise normal and correctly authenticated request, into misusing that authority on an attacker's behalf, because it cannot distinguish a legitimate request from a malicious one that arrives through an otherwise-trusted channel. The reason authentication alone never fully closes T12; a confused-deputy attack does not require breaking the authentication scheme, only manipulating what an already-trusted party is persuaded to do. Named in the CSA's protocol-layer findings as a risk all tool-calling agents share. (see Topic 5.6)

Defined in 2 GAGE programs, which carry 9 distinct definitions of it. The wording above is taught in Certified Agentic AI Governance Professional (CAAGP).

How each discipline defines it

The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.

Certified Agentic AI Governance Professional (CAAGP)

A well-documented computer security failure, named by Norman Hardy in 1988, in which a system with legitimate authority is tricked, through an otherwise normal and correctly authenticated request, into misusing that authority on an attacker's behalf, because it cannot distinguish a legitimate request from a malicious one that arrives through an otherwise-trusted channel. The reason authentication alone never fully closes T12; a confused-deputy attack does not require breaking the authentication scheme, only manipulating what an already-trusted party is persuaded to do. Named in the CSA's protocol-layer findings as a risk all tool-calling agents share. (see Topic 5.6)

Certified AI Practitioner: Workplace Foundations

A privileged program (such as an agent holding your credentials) tricked by a less-privileged caller (an attacker's planted text) into misusing its authority on the caller's behalf. First described by Norm Hardy (1988); re-emerging as a high-severity AI agent pattern (Cloud Security Alliance, 2025 to 2026).

Certified Agentic AI Governance Professional (CAAGP)

A general security term for a system that has legitimate authority to act but is tricked, by an untrusted party, into using that authority on the tricked party's behalf. The GitHub MCP case is a concrete instance of this pattern applied to an AI agent's tool access. (Owned in threat-taxonomy depth by (see Topic 5.6).)

Certified Agentic AI Governance Professional (CAAGP)

A program that holds more authority than the party currently directing it, and that can be tricked into using that authority on an attacker's behalf because it cannot distinguish a legitimate instruction from an injected one.

Certified Agentic AI Governance Professional (CAAGP)

A threat pattern, named in the CSA Addendum's MCP annex, where a component with legitimate high privilege is tricked, through untrusted input it trusts too readily, into acting on behalf of a request it should not have honored; a direct illustration of what happens when no isolation boundary exists between tainted input and a privileged action (see Topic 5.6).

Where it is taught

The exact lessons this term appears in. The first module of every program is free with a free account.

Where it shows up at work

A definition is where Confused deputy starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.

Agentic systems engineeringTechnical Evaluation

Designs multi step agent systems: orchestration graphs, tool contracts, memory, interruptibility, and the evaluation of agent behavior before it is allowed to act.

3 lessons on this page teach it as a primary outcome.

AI security fundamentalsSecurity and Resilience

Understands prompt injection, data poisoning, model theft, insecure integrations and excessive agent privileges, and the controls that reduce each.

2 lessons on this page teach it as a primary outcome.

Governs AI agents that take actions: tool access, least privilege, interruptibility, cascading actions and accountability for what an agent did.

2 lessons on this page teach it as a primary outcome.

Roles that hire for it

Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.

How job descriptions say it

Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.

“multi agent”“agent orchestration”“tool calling”“model context protocol”“computer use”“ai security”

Where the frameworks place it: IMDA Model AI Governance Framework for Agentic AI; OWASP Top 10 for LLM Applications; Google SAIF; NIST AI RMF MEASURE 2.

One interview question about it

An agent you built can call five tools. How do you keep a bad plan from becoming a bad action, and how do you test that before launch?

Asked of candidates for agentic systems engineering. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.

If you come from another field

Some backgrounds already carry this competency, and the readiness check gives credit for it.

Terms it appears with

Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.