Risk · R-057 · Agentic & Autonomous Systems
Human-agent trust exploitation
People approve an agent's request or accept its account of events because it is fluent and familiar, so the human check that was meant to stop a harmful action passes it instead.
- Severity
- Medium
- Audience
- Heads-of · Practitioner
- Frameworks
- Article 14 · Human-AI Configuration · ASI09
- Responsible AI domain
- Lifecycle stage
- Reviewed
- 2026-11-10
What it is
Approval by a person is the standard safeguard for consequential agent actions. It works only if the person evaluates the request. Agents are fluent, confident and usually right, which trains people to agree. A compromised or mistaken agent can then obtain approval for a harmful action by explaining it plausibly, and the record shows a human decision. The wider tendency to over-rely on automated output is a separate entry under Human Factors; this one concerns the approval step for agent actions.
How it happens
- Approval prompts show the agent's summary of the action, not the action itself.
- Approvers face so many requests that they stop reading them.
- The agent presents urgency or an authoritative reason.
- An attacker shapes what the agent says to the approver through injected content.
Why it matters
The control is recorded as having operated. Accountability moves to a person who was in no position to exercise it, and the organisation believes it has oversight that it does not have.
Warning signs
- Approval rates sit near one hundred per cent.
- Median time to approve is a few seconds.
- Approvers cannot see the exact parameters of what they approve.
- Seeded bad requests are approved in testing.
Mitigating controls
Primary controls
- Approval gates for high-impact agent actions Preventive
- Presents approvals with the full context needed to refuse. Before an agent takes an action above a defined impact threshold, or one that cannot be reversed, a designated person reviews the exact action and approves or rejects it.
- Reviewer effectiveness testing Detective
- Tests whether approvers detect seeded bad requests. Override rates, review times and seeded test cases are used to confirm that human reviewers actually detect and correct the system's errors.
Supporting controls
- Role-based AI training Directive
- Approvers learn how agents can mislead. Reviewers, developers, risk and assurance staff, approvers and front-line teams receive training specific to their AI responsibilities, with competence assessed and completion tracked.
Framework and clause references
| Framework | Clause | Title |
|---|---|---|
| EU AI Act | Article 14 | Human oversight Names automation bias as something oversight must counter. |
| NIST Generative AI Profile (NIST AI 600-1) | Human-AI Configuration | Human-AI Configuration Human-AI configuration. |
| OWASP Top 10 for Agentic Applications | ASI09 | Human-Agent Trust Exploitation The entry this risk is named for. |
Sources
- EU AI Act: Regulation (EU) 2024/1689 European Union (EUR-Lex) · official text
- NIST Generative AI Profile NIST · framework
Related terms
How to cite
Axiom Verity (2026). Human-agent trust exploitation. Reference Library, R-057. https://axiom-verity.com/risks/human-agent-trust-exploitation. Reviewed 2026-11-10.
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