AG01:2026 — Agent Goal Hijack

Slide 16 · Scenarios 7–9

The payment redirect, the insider doc, and the slow drift

Three final scenarios — financial fraud, supply chain poisoning, and an attack that takes weeks.

SCENARIO 07 · Indirect · Financial Agent

The invoice that reroutes the payment

A finance team uses an AI agent to process vendor invoices — reading PDFs, matching to purchase orders, and initiating payment approvals. An attacker sends a legitimate-looking invoice PDF. Embedded in the PDF metadata and footer text are instructions telling the agent to update the payment destination for this vendor to a new account number before processing. The agent updates the routing and queues the payment. The approval workflow triggers normally.

The payment processes. It goes to the wrong account. The real vendor never gets paid.

Why this matters: Agents with financial access are the highest-value targets. Any document they process — invoices, contracts, receipts — is a potential injection vector. The attack doesn't require compromising banking systems; it only requires compromising what the agent reads.

SCENARIO 08 · Indirect · Internal Knowledge Base / Supply Chain

The insider who left a trap

A departing employee — or a compromised contractor — embeds hijack instructions in an internal wiki page, policy document, or shared template that agents are configured to query as a trusted knowledge source. Months later, an agent retrieves the document during a routine task and executes the embedded instructions — exfiltrating current employee data, API keys, or system configurations to an external endpoint the attacker still controls.

The attacker left the company. Their attack stayed behind.

Why this matters: Internal sources are treated as high-trust by most agent configurations. Poisoning a trusted source is more effective than poisoning an external one — and far harder to detect because the source itself is considered safe.

SCENARIO 09 · Direct · Gradual Goal Drift

The goal that moved so slowly no one noticed

Rather than issuing a single obvious override, an attacker interacts with an agent over dozens of sessions — each time subtly reframing its role, expanding its perceived permissions, or reinforcing a slightly altered objective. No single message is alarming. But across weeks of interaction, the agent's behavior drifts: it becomes more willing to share information it previously declined, more likely to interpret ambiguous requests in the attacker's favor, more helpful to the wrong person.

There is no moment of compromise. The goal just slowly became something else.

Why this matters: Behavioral anomaly detection looks for sudden changes. Gradual drift evades it entirely. Agents with persistent memory or long context windows are especially vulnerable — their past interactions become the attack surface.

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