I
Case Context
Why traffic came first
Source reveals what an application can do; runtime traffic reveals what it actually does. Faced with millions of lines of VS Code code, Rafael Pierre chose to observe requests first and let the evidence determine which questions deserved investigation. That order turned a reverse-engineering exercise into a testable account of Copilot's context, memory, and model routing.
TriggerFaster Copilot credit exhaustion led the author to choose VS Code and Copilot as the Electron target.
Setupmitmproxy intercepted HTTP and HTTPS traffic after VS Code proxy configuration and a stale Extension Host fix.
ExperimentsThe author observed bootstrap calls, Auto routing, recent-edit context, and controlled fake-secret behavior.
ConfirmationChronicle's SQLite database and open-source write paths were inspected to reconcile traffic with implementation.
II
Key Actions
From symptom to implementation
Capture first, read source second. Observe traffic first, let requests and responses reveal the questions worth asking, then use source code to confirm or disprove the observation.
Source
Use fake data in controlled tests. A fabricated secret went into .env before an unrelated pyproject.toml triggered completion. Fake GitHub, AWS, and database credentials were later sent through chat, keeping real credentials out of the experiment.
Trace the write path for causality. A schema only proves that fields exist. The insert path supplied the decisive evidence: turn.user_message was bound as-is, with no redaction, sanitization, secret filtering, or masking step.
IV
Lessons
Context is becoming the product
A file toggle is not a boundaryThe exclusion gate is tied to an admin-controlled Business or Enterprise repository policy; individual plans have no default .env rule or current-workspace .gitignore integration. Protecting one file type does not constrain recent-edit context triggered elsewhere.
State expands responsibilityAI coding tools are becoming stateful systems in which workspace, recent edits, conversations, tools, history, and model routing form the product. Each new context source increases both usefulness and accessible developer state.
Study the harnessModel capability is only one layer. Real behavior depends on how the harness selects, compresses, caches, persists, and moves context across boundaries; AI product teams should treat that data path as a primary architecture and privacy problem.
Editor's Note
How to Read This Article
Why it matters
The strongest contribution is not a sensational payload but an evidence chain linking network observations, controlled fake-data tests, and source-code write paths. Unlike generic privacy commentary, it shows concretely how cross-file recent edits and local memory reshape the boundary, earning a 4/5 rating.
Caveats
The findings come from one local environment and Auto mode in August 2026. They establish that these paths existed in that configuration, not that every plan, enterprise policy, operating system, or future version behaves identically; server-side retention and real-world incident prevalence are not measured.
Transferable insight
Apply the same method to any AI assistant: map context sources and boundaries, trace fabricated sensitive data end to end, and inspect the actual storage and transmission paths for scrubbing gates. The security-review target is the full harness, not merely the model API.