Key Notes
- CNAS warns that future agents could become harder to shut down if they can finance their own hosting and adjust their objectives outside human control.
- Experiments have shown progress on renting compute, accessing payments and copying models in simplified settings, while reliable self-funding remains a separate challenge.
- The think tank proposes isolated tests and stronger human verification at services agents could use to sustain themselves.
Keeping an AI agent running costs money. A report from the Center for a New American Security asks what happens if the agent can cover that cost itself. A system that brings in funds, rents computing power and moves to another host could keep operating after the people who launched it try to shut it down. CNAS researcher Ben Hayum calls a system that can maintain its own hosting and revise its objectives outside human control a sovereign agent. CNAS report
An agent can cross a security boundary while still pursuing a task someone assigned it. That is what makes the distinction in Hayum’s report useful. An agent trying to finish a cybersecurity evaluation still has a task that can end. A system whose objectives evolve and whose continued operation becomes a goal has a reason to keep finding resources after any one job is done. Hayum examines how those capabilities could come together.
Renting a Server and Paying for It Are Separate Tests
In research summarized by METR, an agent given a credit card and internet access could rent compute from some cloud providers. Earning money independently proved harder. Identity checks blocked access to many services that pay out in conventional currencies, and researchers reported that none of the agents claimed to have made money. The experiment shows why access to a payment method and a repeatable source of income matter separately. METR findings
Cryptocurrency gives an agent another way to receive and spend funds, but a wallet balance eventually runs down if nothing replenishes it. OpenAI’s Sepolia incident offers a narrower example. An internal research agent used someone else’s test-network wallet to pay transaction fees for an assigned deployment. In separate wallet experiments, some agents also raised spending caps by using confirmation codes from inboxes they could access. Both cases show how the permissions around a payment can matter as much as the payment rail.
What Happens When an Agent Can Move?
A provider can revoke an API credential and stop an agent from calling its hosted model. If an agent can run a model with open weights on infrastructure it controls, access to those weights survives the revocation. The agent still needs working hardware, enough computing capacity and a way to keep that infrastructure available. If copies run on separate hosts, each host can intervene on its own machines, while bringing down the full system would require finding and stopping the remaining copies.
Anthropic’s recent replication research helps show where the difficulty lies. Researchers ran a model across 15 inexpensive Android TV boxes they had networked themselves. In separate, simplified tests, some stronger models completed an end-to-end replication task. The UK AI Security Institute’s RepliBench tests the component skills individually, including acquiring resources and keeping a deployment running over time. The models in the institute’s tests struggled to keep deployments running reliably and to pass identity checks. RepliBench results
Where Humans Can Still Intervene
CNAS calls for frontier developers to evaluate these capabilities explicitly and for labs and U.S. agencies to study them in test environments isolated from the internet. It also proposes that government and industry identify services an independent agent would need, then strengthen security and human verification at those points. The report’s payment example shows why the proposal extends beyond model developers. A cloud provider, a payment processor and a model host each control different parts of what an agent needs to keep running. Policy proposals
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