Proof / Browser-Safe AI Security

The browser is part of the AI trust boundary.

Controlled local research makes prompt injection, source confusion, hidden browser state, frame boundaries, visual deception, and tool behavior observable without relying on real customer data or third-party production systems.

See AI Security →Back to Proof →

Observe the whole browser-AI path

PageHTML · text · hidden content

◎

BrowserDOM · frames · navigation

◉

AIVisible context · interpretation

⚙

ToolBrowser · API · action

⌁

NetworkRequests · responses · pivots

□

EvidenceState · manifest · hash

The security problem

The browser changes what AI can see, trust, and act on.

A browser-integrated AI system inherits trust decisions from HTML, rendered content, frames, navigation, authenticated state, tools, screenshots, hidden DOM, and delayed transitions. The model is only one component in that larger attack surface.

Emerging trust failures

Indirect injection

Untrusted page content can become instruction without being an explicit user command.

⌁

Source confusion

What the machine can read may differ from what the user believes they are viewing.

↻

State transitions

The attack path can span navigation, delayed content, frames, and off-browser handoffs.

Evidence-first research

Make dynamic browser state reviewable after the test.

AI and browser behavior can change between runs. The research captures multiple views of the same event so a reviewer can understand what content existed, what the browser exposed, what network activity occurred, and what result followed.

Independent evidence views

◎

Browser

DOM, frame trees, rendered text, screenshots, navigation, and timing.

⌁

Network

HTTP requests, responses, proxy captures, and relevant navigation metadata.

◉

Model & target

Model-visible context, local target state, response, and resulting action.

□

Integrity

Structured records, manifests, deterministic hashes, reports, and archive sidecars.

Evidence is designed for reproduction, not demonstration alone.

What this proves

Emerging AI risk can become a controlled test system.

The project combines browser automation, local AI behavior, network observation, synthetic data, structured evidence, and safe experimentation into one repeatable research workflow.

Capability demonstrated

▰

Synthetic sensitive data

Study leakage and handling behavior without introducing real client secrets.

◎

Visual deception

Compare what the human sees with what the browser and model can read.

⚙

Controlled tooling

Browser automation, direct HTTP, proxy observation, and local targets provide independent evidence.

◇

Safe research boundary

Explore the mechanics in local synthetic environments rather than attacking production SaaS by default.

Why a client should care

AI findings should be grounded in observable attack paths, not speculative prompt examples.

Black Bag Security can instrument the new trust boundaries created when AI meets browsers, tools, authenticated state, and untrusted content.

Discuss an AI system →

Browser-Safe AI Security

Influence

Untrusted content is observable.

◎

State

Browser context is captured.

⌁

Consequence

Actions and network effects are recorded.

□

Proof

Evidence can be replayed and reviewed.