Baidu's "Shrimp Farming" Quarantine Method Explained

As the shrimp farming craze spreads from the tech community to the general public, concerns about data security and system control are emerging.

Previously, Summer Yue, Director of AI Alignment and Safety at Meta’s Superintelligence Lab, set a “confirm before acting” safety command for OpenClaw during a test.

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

This highlights the potential risks of OpenClaw in a local deployment mode.

In response to frequent security issues in “shrimp farming,” the industry urgently needs a new safety paradigm to help ordinary users “raise shrimp painlessly.”

Baidu has officially entered this field, launching the world’s first mobile lobster app, “Red Hand Operator.” On March 17, the app was renamed Red Claw, and users can simply download and register to directly command this “mobile lobster” to perform various tedious tasks.

A test by AllWeather Tech found that Red Claw uses the qianfan and deepseek-v3.1-250821 models, which can activate apps on the phone for tasks like ordering food and booking tickets.

According to AllWeather Tech, Red Claw’s architecture introduces a strict “three-layer isolation system”:

1. Physical-level isolation at the bottom. The app runs entirely in the cloud on the phone, physically isolated from the user’s real device data. The app itself does not access or require user authorization for local storage data.

2. Runtime environment isolation. Each user is assigned a dedicated cloud phone, ensuring absolute separation between devices.

3. Task data isolation. Multiple layers of encryption prevent information crossover between tasks.

Additionally, in terms of permissions and visibility control, Red Claw emphasizes “ownership of control,” meaning every AI operation is fully visible and traceable to the user. When privacy or authorization is involved, the cloud process is forcibly suspended, requiring user confirmation or manual intervention to proceed.

This provides a relatively safe “painless trial and error” environment for mainstream users.

However, moving the “lobster” to the cloud does not eliminate problems—only changes their form.

The most obvious change is in efficiency.

Local execution responds instantly, while cloud-based phones inevitably introduce network latency and virtual device scheduling. For standardized tasks like ordering food or booking tickets, the impact is manageable. But in multi-step, real-time feedback scenarios, delays can accumulate. Operations that were once seamless become fragmented, with waiting for confirmations, turning smoothness into a cost.

Visibility does not necessarily equate to increased control.

“Every step is visible and traceable” enhances security in design, but when tasks are broken into many small operations, users face a continuous stream of execution logs. Their role shifts from decision-maker to passive confirmer.

Visibility does not equal understanding; confirmation does not mean true control.

Isolation also redefines capability boundaries.

The permissions a cloud phone can invoke depend on platform compatibility, not the full capabilities of the user’s device.

This means that while risks are reduced, the system converges from an “almost omnipotent agent” to a “defined automation tool.”

Physical isolation is more like a shift in trust.

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

Running in the cloud also incurs costs. Each user has an independent cloud phone running continuously, consuming significant computing power and resources. As the user base grows, platforms will either need to sustain ongoing subsidies or offset costs through restrictions and tiered pricing. This structure suggests it is a temporary solution rather than a scalable, ultimate approach.

A more subtle change is the diminished perception of risk.

In local environments, errors often occur directly on the user’s device, with clear and immediate feedback. In cloud isolation, errors are “wrapped,” with impacts delayed or partially mitigated. This “safer” experience may also weaken users’ sensitivity to risk boundaries.

In the long run, the “cloud isolation method” is more like a compromise—balancing commercial adoption and risk control during AI’s immature stage. It addresses urgent uncertainties but introduces new trade-offs.

When future edge devices have sufficiently powerful models and robust safety measures, whether “cloud lobsters” can safely “return” to users’ local devices will be a key focus in the next wave of intelligent agent technology.

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