Web page
The user opens a real page with its content and interface.
MaskAgent


SIH 2026 / Problem ID SIH26171
On-device visual perception for lightweight browser agents. MaskAgent understands a web page, masks sensitive context, and acts locally through Ollama.

Traditional browser agents pass raw page context to an AI service. MaskAgent inserts a deliberate boundary: understand the interface, remove the secrets, then let a local model decide.
The user opens a real page with its content and interface.
DOM structure and visible elements are mapped for context.
Names, email, phone, address and password fields are identified.
Private values become safe placeholders before inference.
Ollama chooses a browser action from the sanitized context.
Built for useful browser automation without unrestricted access to personal information. Every action stays legible to the person using it.
Redacts sensitive UI values before they enter an AI prompt.
Combines page structure with visible UI signals to find the right target.
Supports CLICK, TYPE, SCROLL, SELECT and DONE through a local model.
An activity stream shows scans, redaction, decisions and execution.
Checks whether a target exists and an intended action is relevant.
Designed to grow toward OCR, prompt-injection defense and cross-browser support.
A working prototype is easiest to understand when the privacy boundary is visible. Watch both videos, then explore the interface captures below.



The people behind the prototype
MaskAgent is a Smart India Hackathon 2026 project exploring how browser agents can be capable and privacy-conscious at the same time.