Topic starter
10/06/2026 10:40 pm
TL;DR: AI agents that can book travel, manage calendars, and move across accounts are becoming attractive targets because hijacked agents can still look legitimate to the outside world, according to SumSub. The identity problem is no longer just access, but proving whether an action came from the intended agent or a compromised one, which breaks trust models built for human users.
Editorial analysis by NHI Mgmt Group, based on content published by SumSub: “Know Your Agent: The New Frontier of Fraud Prevention”.
Key questions
Q: How do organisations stop a hijacked AI agent from draining accounts?
A: Limit delegated permissions, separate write and transfer capabilities, and require provenance for high-impact actions.
Q: What is the difference between an AI agent and a normal automated workflow for security teams?
A: A normal workflow follows predetermined logic, while an AI agent may interpret instructions and choose actions at runtime.
Q: When should fraud teams treat agent behaviour as suspicious?
A: When the same agent begins touching unfamiliar services, repeating high-impact actions, or executing instructions that do not match the user's normal pattern.
Practitioner guidance
- Define agent identity records Inventory every AI agent that can act on behalf of a user, including the accounts, APIs, and business workflows it can reach.
- Constrain delegated agent permissions Limit each agent to the smallest viable set of calendar, account, and finance permissions, then separate read, write, and transfer capabilities so one compromised agent cannot drain multiple systems.
- Monitor for action provenance gaps Alert when high-impact actions are executed from the same agent context repeatedly, from unusual instruction sources, or without the expected provenance trail linking the decision to the intended agent.
Bottom line: AI agent hijacking turns trusted automation into a governance problem because the compromised actor can still appear legitimate to outside systems.
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This topic was modified 3 days ago by NHI Mgmt Group
11/06/2026 2:48 am
AI agent hijacking is an identity assurance failure, not just a fraud event. The outside world often sees a valid workflow, a trusted account, and a normal-looking action trail, which is exactly why the compromise is hard to detect. That makes agent identity different from human sign-in assurance, because the trust boundary extends into runtime behaviour. Practitioners should treat agent integrity as part of identity governance, not as an adjacent monitoring problem.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials, according to AI Agents: The New Attack Surface report.
A question worth separating out:
Q: Who should own response when an AI agent is compromised?
A: Ownership should sit across IAM, fraud, and application security, because the compromise can affect identity trust, customer impact, and workflow integrity at the same time. The response should include token revocation, connector shutdown, and review of all actions the agent performed while compromised.
👉 Read our full editorial: AI agent hijacking exposes the identity gap in trusted automation
12/06/2026 4:22 am
Trusted automation creates an identity gap, not just a fraud gap: When an AI agent acts across accounts, the security problem is no longer simply who logged in. The harder question is whether an observed action came from the intended automation path or from a compromised control plane behind it. That shift means identity assurance has to move from session trust to action provenance, because the same outward behaviour can mask two very different operators.
A few things that frame the scale:
- Half of companies using generative AI will deploy agentic AI by 2027, according to Deloitte's 2025 Technology Predictions.
A question worth separating out:
Q: Who is accountable when an AI agent acts outside its intended scope?
A: The organisation is accountable, but operational responsibility should sit with a named owner and a governance process that can explain the agent’s purpose, access, and recorded actions. Without that, autonomous behaviour becomes unassignable risk rather than managed automation.
👉 Read our full editorial: AI agent hijacking exposes the identity gap in trusted automation
This post was modified 3 days ago by NHI Mgmt Group