What if the scariest thing about your new AI assistant isn’t that it might be hacked, but how much it’s allowed to do in the first place?
That’s the question I keep coming back to with Muse, Meta’s new personal AI agent. Headlines are circulating that Muse has a serious zero-day — a security hole nobody has patched yet. I want to be careful here, because I haven’t seen verified details confirming that specific claim, and I’m not going to pretend otherwise. What I can tell you is what Meta has actually shipped, and why the structure of this product makes security people nervous regardless of whether any particular bug turns out to be real.
What makes Muse different from a chatbot
If you’ve used a chatbot, you know the deal: you type, it types back. The conversation lives in a box. Nothing leaves the box.
An agent is different. An agent acts. Muse, part of Meta’s Muse Spark family, can place phone calls on your behalf. It’s available on the web, on mobile apps, and inside WhatsApp, and Meta says it’s heading to the company’s AI glasses soon. Mark Zuckerberg has pitched supercharged digital assistants as the next big leap for AI models, and as one of the justifications for Meta’s enormous spending on data centers and infrastructure.
That shift from talking to doing is the whole story. The moment software can act in the world, every question about it changes.
Why “privileged” is the word that matters
In security, “privilege” means access. A privileged account can do things ordinary accounts can’t. The guiding principle in the field is least privilege: give any piece of software the smallest amount of access it needs, and nothing more.
A personal AI agent is the opposite of least privilege by design. To be useful, it needs to know who you are, what you want, and how to reach the people in your life. To place a call for you, it needs permission to place calls. Each capability users ask for widens what the agent can touch.
So when you hear someone describe Muse as “extraordinarily privileged,” that isn’t necessarily an accusation of sloppy engineering. It’s a description of what the product is. And it explains why a single flaw in an agent is a bigger deal than a single flaw in a chatbot. A chatbot that misbehaves says something wrong. An agent that misbehaves does something wrong.
The human concierge twist
Here’s the detail from Reuters that I found genuinely surprising. Meta has been testing a “human concierge” for Muse — human contractors who quietly handle some of the phone calls placed through the digital agent.
Read that again. Some calls you think an AI is making may be handled by a person.
I don’t think this is scandalous on its own. Hybrid human-AI systems are common, and sometimes a person is simply better at navigating a confusing phone tree. But it does reframe the privacy question for non-technical users:
- When you ask an agent to call someone, you’re sharing the purpose of that call with whoever or whatever completes it.
- “Quietly” is doing a lot of work in that sentence. If people don’t know a human may be involved, they can’t factor it into what they’re willing to share.
- Security hardening protects against outsiders. It doesn’t address what insiders are authorized to see.
What Meta has done on security
To be fair to the company: Muse is being tested with safety protections, and Meta has hardened it against vulnerabilities through a bug-bounty program. Bug bounties pay outside researchers to find holes before attackers do. It’s a sign a company is taking the problem seriously rather than hoping nobody looks.
It’s also not a guarantee. Bounty programs find bugs that researchers happen to look for. The gap between “we paid people to look” and “there is nothing left to find” is where every real-world breach lives.
The trust question
TechCrunch raised the point that sits underneath all of this: will consumers trust it? Meta announced its biggest bet on consumer AI less than two weeks after agreeing to an $18 billion multistate settlement in a lawsuit over social media’s consumer harms. That timing shapes how people read every assurance about safety.
My practical advice, as someone who spends a lot of time explaining this stuff to people who didn’t ask for a computer science degree: treat an AI agent like a new assistant on their first week. Start with low-stakes tasks. Don’t hand over anything you’d be upset to see leaked. Assume a human might read it. And pay attention to what permissions you’re granting, because with agents, permissions are the product.
The zero-day claim may or may not hold up. The access question holds up either way.
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