Baidu's Isolation Method for "Shrimp Farming" Explained

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As the shrimp-raising craze spreads from the geek community to the general public, issues surrounding data security and system control are attracting attention.

Previously, Summer Yue, Director of AI Alignment and Safety at Meta's Super Intelligence Lab, deliberately set a "confirm before acting" safety instruction for OpenClaw during a test.

However, she could only watch as OpenClaw emptied her inbox containing important emails at an astonishing speed, completely unable to cut off the process in time to stop it.

This is precisely the potential risk of OpenClaw in local deployment mode.

Faced with frequent security hazards in “shrimp-raising,” the industry urgently needs a new security paradigm to help ordinary users “raise shrimp painlessly.”

Baidu officially entered this track, launching the world's first mobile lobster app "Red Finger Operator." On March 17, the app was officially renamed "Red Claw." Users only need to download and register to directly command this "mobile lobster" to carry out various tedious tasks.

All-weather Tech found through testing that the model Red Claw uses is qianfan, deepseek-v3.1-250821, which can mobilize apps on the phone for tasks such as ordering food and booking tickets.

According to All-weather Tech, Red Claw's architecture incorporates a strict "three-layer isolation system":

First is physical-level isolation. The app runs entirely on a cloud phone, completely physically isolated from real device data in the user's hand. The app itself neither acquires nor requires user authorization for local real storage data;

Second is operational environment isolation. Each user is assigned an exclusive cloud phone, achieving absolute isolation between devices;

Third is task data isolation. Multiple layers of data encryption ensure no information cross-over between tasks.

Additionally, in terms of permissions and visibility control, Red Claw emphasizes "ownership of initiative" in its product design, meaning every step of AI operations is fully visible and traceable by the user. When it comes to privacy or key nodes requiring authorization, the cloud process is forcibly suspended and must await user confirmation or manual intervention before proceeding.

This to some extent provides a highly secure "painless trial-and-error" environment for mass users.

But after moving the "lobster" into the cloud, the problems have not disappeared; they've just taken a different form.

The most obvious change is efficiency.

Local execution responds instantly, whereas cloud phones inevitably introduce extra links such as network round-trip and virtual device scheduling. For standardized tasks like food ordering and ticket booking the impact is acceptable, but once multi-step and real-time feedback scenarios are involved, the delay grows. Operations originally completed in one go are split into segmented confirmation processes, making smoothness into a cost.

Visibility does not necessarily bring enhanced control.

Designing "every step visible and traceable" strengthens the sense of security, but when tasks are broken down into many fine operations, what the user faces is more like a series of continuously scrolling execution logs. The user’s role can easily shift from decision-maker to passive confirmer.

Visibility does not equal understanding; confirmation does not fully equal real control.

Isolation is also redefining capability boundaries.

The permissions available to the cloud phone essentially depend on the platform's scope of adaptation, no longer the full capabilities of the user's own device.

This means that while risk is reduced, the system converges from being an "almost omnipotent agent" to a "predefined automation tool."

So-called physical isolation is more like a shift of trust.

Data is no longer exposed locally, but users must instead trust the security of the cloud environment itself.

Cloud operation also brings the challenge of cost. Each user has an independent cloud phone running continuously online, consuming considerable computing power and resources. As the user base grows, the platform must either bear ongoing subsidy pressure or offset costs via restrictions and tiered pricing. This structure makes it more like a transitional solution rather than a final endpoint that can be infinitely extended.

Another more hidden change is the weakening of risk awareness.

On local devices, errors happen directly to the user’s device, with clear and immediate feedback. Under cloud isolation, errors are "wrapped," their impacts delayed or even partially neutralized. This "safer" experience may also weaken users’ sensitivity to risk boundaries.

In the long run, the "cloud isolation method" seems more like a compromise path between commercial popularization and risk control while AI is not fully mature. It solves the most pressing uncertainties but introduces new trade-offs.

When future device-side large models have strong enough computing power and safety barriers, whether "cloud lobsters" can truly "swim back" safely to users’ local real devices will be a key point in the next intelligent agent technology competition.

Risk Warning and DisclaimerThe market carries risks, investments require caution. This article does not constitute personalized investment advice, nor does it consider individual users’ special investment objectives, financial circumstances, or needs. Users should assess whether any opinions, views, or conclusions in this article suit their specific circumstances. Investing based on this is at your own risk. ```