Two documented architectures, two different places for the boundary. A comparison of what each publishes, and what neither has been shown to prove.
Part of the Grok Bot security series.
Basis of this analysis. Written from xAI's and Cursor's Grok Bot documentation and Meta's engineering post "How We Built Safety Into Muse" (September 8, 2026). HexTyx has not tested either product and is a security vendor. This is a comparison of documented architecture, not a ranking of which product is safer. For HexTyx's earlier analysis of Muse, see Meta Muse VM escape and AI agent sandboxing.
Two AI agent products, two documented architectures. The question is not which logo to trust. It is where each design puts its boundaries, and what happens to the user if an agent is manipulated.
| Criterion | Grok Bot (xAI / Cursor docs) | Meta Muse (Meta's post) |
|---|---|---|
| Unit of isolation | Per user: dedicated Firecracker microVM | Per user: persistent dedicated cloud VM, plus a sealed "runtime cell" for the agent harness |
| Isolation between an individual's agents | None: all Bots share one computer | Not addressed in the post we read. The harness runs in a sealed cell with its own root filesystem and filtered syscalls, which separates it from the host |
| Who authorizes actions | Per-action approvals and model-based Auto Review | Sentinel, a host-side component described as "the sole permission authority for connector actions and network egress" |
| Credentials | Secrets masked; connector tokens stay on Cursor's backend; logins and CLI credentials on the computer are shared across Bots | Credentials live outside the cell. The agent sees surrogate tokens that are swapped for real ones at the network boundary after authorization |
| Where approvals appear | In the product's approval flow; unattended approvals expire after about 10 minutes | In the client UI, not in chat. Grants can be one-time, session, task, time-bounded or perpetual |
| Spending | Not described in the sources we read | Purchases use single-use, merchant- and amount-limited card numbers |
| Network control | Enterprise only. Default without a policy is allow-all | Sentinel mediates network egress |
| Prompt-injection stance | Defenses "reduce, but don't eliminate, risk" | "Prompt injection remains an open problem" |
Read the table as two different choices, not a scorecard. Muse's post describes mechanisms. Grok Bot's documentation is blunt about what is shared. Neither tells us how either holds up against a determined attacker.
Grok Bot's launch copy talks about Bots having "their own computer." The documentation says all of a user's Bots share one, and advises against using separate Bots as a security boundary. For a user who wants a high-trust agent and a low-trust agent, the documented route is a separate Cursor user. See Grok Bot Security.
A VM isolates the agent from the host and from other users. It does not by itself decide what the agent may do with the accounts inside it. Muse's design addresses this with a separate authority. The agent harness is in a sealed cell, and Sentinel, outside the cell, decides allow, deny or ask for connector actions and network egress. That architecture puts the decision where a manipulated agent cannot rewrite it.
Grok Bot's equivalent decision points are the approval step, Auto Review (model-based, and documented as a complement to least privilege and approvals, not a replacement) and Enterprise network controls. This is a more plan-dependent set of controls, and a model-based reviewer shares its weakness with the model it reviews.
Both designs keep real secrets away from the model. The difference is where usable authority sits. In Muse's description, the agent holds only surrogates, and real credentials are substituted at the network boundary after an authorization decision. In Grok Bot, connector tokens stay on Cursor's backend, but a signed-in browser and CLI credentials sit on the shared computer. More on the distinction: AI Agent Credentials and API-Key Abuse.
Neither design claims to solve it. Meta's post says prompt injection remains an open problem and that Muse can and will still make mistakes. Its defenses include model training, untrusted-content labeling, a classifier ensemble and a browser sub-agent that reads an accessibility tree and cannot run JavaScript. Grok Bot's documentation marks outside content as untrusted and says its defenses reduce but do not eliminate risk. For an example of how an agent's own tools can be turned against it, see the Grok Cryptographic Context Injection case (which targeted Grok's web chat, not Grok Bot).
For any agent product, ask what happens after the agent is manipulated.
Under Grok Bot's documented design, a steered Bot reaches what the whole computer holds, constrained by approvals and, on Enterprise, a network policy. Under Muse's documented design, a steered agent is constrained by Sentinel's decisions, with credentials swapped in only after authorization. The latter looks narrower on paper. Paper is not testing, and we have tested neither. See also AI agent blast radius.
We will not name a winner, and you should be wary of anyone who does without testing. A fair reading of the documentation is that Muse's published design moves more of the decision-making outside the agent, while Grok Bot's documentation is clearer about its limits and offers strong controls mainly on higher plans. Which fits you depends on your threat model and plan.
When evaluating Grok Bot: which plan are you on, are high-consequence logins on the shared computer, is a network policy set, and do you need a separate Cursor user for a separate trust domain?
When evaluating Muse: what do the grants you approve actually cover, how long do they last (a "perpetual" grant is a standing permission), and what does the planned Confidential VM add?
These are HexTyx recommendations and apply to either product and to your own agents.
Agent security is an architecture problem: where authority lives, who decides, and whether the agent can change the rules. Secure the authority, not just the runtime.
Run the free playground scanner against your AI application, or read the other guides in this series.