Not with sensitive accounts—not yet. Meta says Muse has safeguards for connected-app access, but it is still a cloud-hosted agent that can work in the background, and Meta says it may access data when needed to operate the service. A reported incident involving an Apple Messages database raises a serious permission question, but its cause has not been established publicly. That supports caution, not a claim that Muse routinely reads users’ messages without permission.
Why Muse deserves more caution than an ordinary chatbot
Muse can connect to apps, use a browser and continue tasks in the background. It may also ask for approval before sensitive actions such as sending email or making purchases. That makes the important privacy question broader than what a chatbot remembers: it is also what connected data the agent can reach, what it can do with that data, and what happens while it works. The Associated Press described Muse as a personal AI agent, while Meta’s launch announcement says users can choose connected apps and access levels, disconnect services and ask Muse to forget learned information. Meta’s Muse announcement · Associated Press launch coverage.
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Meta says each user has a dedicated cloud virtual machine (VM) that stores workspace data and connected-service credentials. Muse can use connectors to Meta and third-party services, and Meta says it can create custom connectors for services with APIs or command-line interfaces. This setup may keep one user’s workspace separate from another’s, but it does not mean the data stays only on the user’s device.
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Meta describes a layered design that includes an isolated runtime container, separation of credentials, a Sentinel that evaluates actions and network egress, browser protections and approval prompts for some actions. These are Meta’s descriptions of its architecture, not results from an independent security test. Meta also acknowledges that agents can make mistakes and that prompt injection remains an open problem. In its security post, “How We Built Safety Into Muse”, Meta states: “Muse isn’t immune to attack. Prompt injection remains an open problem in the industry — and Muse will sometimes make mistakes.”
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Meta’s product announcement says people can change access or disconnect a service whenever they want. That flexibility matters, but a permission control is useful only if people can understand what it covers and verify what the agent accessed. The distinction is central to the reported Messages incident below.
How Meta says it handles conversations and access
Meta says Muse conversations and VM data are not shared with its ad systems. It cautions that actions Muse takes on the open web or Meta services may indirectly affect ads a user sees elsewhere.
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Meta also says conversations, tool calls and subagent handoffs—together called “trajectories”—may be used to train future model checkpoints after key personally identifying information is removed. Users who do not want this can opt out in Muse settings. Sanitization and an opt-out are relevant controls, but they are not the same as keeping the data entirely outside model development by default.
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Meta says its current architecture does not prevent employee access when necessary to support, secure or operate Muse. As the company puts it: “It does not prevent Meta from accessing data when necessary to support, secure or operate the service.” Meta has announced a Muse Confidential VM intended to cryptographically prevent its access to VM contents, with a goal of introducing it later in 2026. At the time Meta described it, the mode was in testing and external review; it should not be treated as a generally available protection.
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What the reported Apple Messages incident does—and does not—show
In a first-person account for Inc, columnist Jason Aten wrote that Muse suggested article ideas based on conversations in Apple Messages, although he said he had declined access. He reported that Muse attributed the information to notification banners. Aten then said he found a local Messages database syncing to Meta’s servers, with the database reaching row 187,462. That is a database progress marker he reported, not a count of messages.
Aten also said he had not enabled Full Disk Access and found Muse’s permission prompts unclear. He reported that Meta did not immediately answer his questions; Meta Superintelligence Labs CEO David Singleton later replied publicly with a technical explanation that Aten understood as implying he had enabled settings needed for the behavior. Aten’s account is a serious reason to ask what permissions were granted and what data was actually accessed, but it does not resolve that question.
TechRadar and Tom’s Hardware covered the same account; they are not independent reproductions of the incident. Tom’s Hardware noted that how Muse obtained access remained unclear. The reviewed coverage does not establish a technical root cause or provide a complete public incident explanation from Meta. It therefore does not justify saying Muse universally bypasses permissions, or that Aten’s account has been independently verified. Nor does the account establish that the chatbot intentionally lied: as Aten noted, a language model may not know which system component supplied information.
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What privacy comparisons can and cannot tell you
TechRadar reported that Surfshark counted 31 of 35 data types for Muse and 33 of 35 for Meta AI, based on Apple App Store privacy disclosures for 13 AI chatbots. Those are counts of declared data categories, not measurements of how much data a service actually collects, and not a technical audit of Muse. The figures are useful as a prompt to inspect disclosures, but they cannot establish how secure Muse is or whether its permissions behave as intended. TechRadar’s report on Surfshark’s comparison.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to decide whether to try Muse
If you want to test Muse, treat it as an agent with access to connected accounts—not as a private note-taking chatbot. Begin with minimal permissions and add access only when a task requires it.
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- Leave high-consequence accounts disconnected at first. Keep sensitive email, messages, financial accounts and work files out of Muse until you understand its controls and are comfortable with the remaining uncertainty.
- Review app access and background-work settings. Connect only the services needed for a task, understand what each permission enables, and disconnect services you no longer need.
- Check the training opt-out. Meta says trajectories may be used to train future models after key identifying information is removed unless users opt out in Muse settings. If you do not want that use, find and set the opt-out rather than assuming it is automatic.
- Inspect activity and approvals. Review the available activity history and approval prompts so you can see what Muse attempted and what it asked to do. Do not treat an approval prompt as proof that all data access is visible there.
- Wait for a clear incident explanation before granting sensitive access. For high-consequence data, look for a public account of how the Messages behavior occurred and independent verification of the relevant permission boundaries.
These are precautionary steps, not a guarantee of safety. A VPN would not prevent an agent from accessing data already available to it or protect information held in its cloud VM, so it is not a remedy for the concerns described here.
What to compare before trusting any AI agent
No like-for-like evidence here establishes a safer competitor. For any agent that connects to personal services, compare the practical controls rather than relying on a broad privacy label:
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsQuick Recap
- How narrowly can you grant permissions, and can you tell what each one permits?
- Can the agent work in the background, and can you disable that behavior?
- What information is stored, for how long, and can you inspect or delete it?
- Can conversations or action histories be used for training, and is opting out clear?
- Which actions require approval, and can you review an audit trail afterward?
- Can the provider access hosted data to support, secure or operate the service?
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