Windows Agentic VR
Platform-Specific Advanced Windows Vulnerability Research
Track: AI-Native — Bring your RE and vulnerability research experience. Learn to direct agents through Windows analysis, debugging, and validation. Recommended foundation: Agentic RE or equivalent experience.
Agentic RE teaches reverse engineering and vulnerability research workflows across platforms, along with the AI infrastructure, tool integration, and orchestration behind them. Windows Agentic VR is platform-specific advanced training. It combines the research craft from Patch Diffing in the Dark with an agentic loop for speed and efficiency on Windows targets.
AI finds the leads. We build the test that could prove it wrong. This is the new work.
John McIntosh, To the Frontier and Back Again, [un]prompted.au 2026
Learn how to build the loop that discovers real bugs, then validates each finding.
Use local models to find real bugs. Start with a coding agent and a simple setup, investigate real Windows CVEs, and build toward a research loop you can run yourself. Patch diffing, static analysis, live debugging, and careful validation give the agents useful feedback. The local-model research behind To the Frontier and Back Again is part of what this course brings into the classroom.
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Dates & Registration
CSL Virtual Cohort Details · 2027 Schedule and Planned Cohorts
Start Simple. Prove It Works. Make It Yours.
This course is not here to teach you the perfect harness. It teaches ideas that work, why they work, and how to adapt them to your own research problem. You bring the reversing experience. You do not need to arrive with an elaborate agent setup.
Begin with a coding agent, a shell, and files. Give it decompiled code and a useful question. Work through a known Windows CVE and check whether its explanation holds up. That first useful result gives you a baseline. As the work gets harder, add the tools, feedback, and structure that help you move forward.
You will see the building blocks that recur across agent harnesses: tool access, manageable context, persistent notes, feedback, and verification. A debugger, an MCP integration, a reusable Skill, or another agent should earn its place by improving the investigation.
Validation is the new work. An agent pointing at suspicious code is a lead. Establish reachability, test the behavior, use controls, and separate what you observed from what you inferred. Feed those results back into the analysis. Keep the finding, revise it, or reject it.
The guided labs use public, fixed CVEs. The capstone asks you to investigate a staged target without a step-by-step walkthrough, reproduce the finding, and package it for another researcher to check. The aim is to leave with experience running the loop, not just a harness you watched someone else operate.
What You’ll Practice
- Turn a coding agent into a reversing agent using files, shell access, and focused research questions.
- Investigate real Windows CVEs with Ghidra, ghidriff, and runtime evidence.
- Compare simple tool access with MCP integrations and measure what the added structure buys you.
- Use local models and compare frontier-model behavior under the same standard of evidence.
- Keep research records, explore variants, and assign bounded work to agents without losing track of the claims.
- Reproduce findings on clean Windows targets and capture the method as reusable Agent Skills and root-cause reports.
Course Outline
Part 1: Turn a Coding Agent Into a Reversing Agent
Start with one agent, a shell, and decompiled functions on disk. Establish what it can do before building more infrastructure around it. Supply enough Windows context to ask a useful question: binary versions, symbols, relevant functions, and the behavior you want to understand.
Use Ghidra and ghidriff to investigate public CVEs, narrow noisy patch diffs, and connect the changed code to root-cause explanations. You will be taught how to approach complex vulnerability research: break a large target into questions an agent can help answer, and decide what to examine next. If an area is new to you, learn how to verify the agent's explanations against other findings, such as related CVEs, prior research, and your own lab results, before you build on them. Record what was observed, what was inferred, and what remains untested.
Part 2: Build the Loop That Checks Your Work
Give the investigation a repeatable Windows lab and a way to test its claims. Connect static analysis to debugger observations, controlled inputs, and reproducible VM state. Learn to check the test itself: a failed instrument can look like a negative result, and a PoC can appear to prove more than it actually does.
The debugger path starts with Windows debugging tools such as cdb/WinDbg through the shell. Compare that baseline with tool integrations, including pyghidra-mcp for static analysis and a focused debugger interface. Keep the tools that help the agent gather better evidence, not simply more output.
Part 3: Make the Hunt More Effective
Turn the loop toward candidate findings and variants. Use local models, targeted context, and reusable Agent Skills to apply lessons from a validated bug to another investigation. Keep a record of findings, rejected leads, and open questions so the workflow does not keep rediscovering its own work.
Explore how bounded agent roles and shared research records help divide the work. Compare local and frontier behavior without lowering the evidence bar. You remain responsible for deciding what the results support.
Part 4: Take a Finding Through to a Report
Apply the workflow to a staged Windows target. Move from a research question through analysis, validation, and a reproducible trigger or proof of concept. Document the root cause, the tested environment, the controls, and the limits of the evidence.
Learn how to write and file a real MSRC report: a clear root cause, a reproducible proof of concept, and the evidence Microsoft needs to confirm the issue. Leave with the research record, reusable Skills, and the experience to dive deep into new topics on your own, with AI assistance. Handling untrusted target content and controlling agent access are part of that workflow throughout.
These parts describe the course arc, not a fixed day-by-day timetable. Each edition sets its lab selection, depth, and delivery schedule.
Who This Course Is For
This is an advanced specialization for reverse engineers, vulnerability researchers, and exploit developers who want to apply agentic methods to Windows research. Agentic RE graduates with the underlying Windows RE and VR experience are a natural fit.
You should be comfortable with:
- Reversing binaries in Ghidra, IDA, or similar tools.
- Common vulnerability classes and the process of investigating a bug.
- Command-line analysis and debugging tools.
- Reading debugger output and reasoning about program state.
Windows internals knowledge is helpful, especially services, drivers, IOCTLs, and user versus kernel mode. Prior MCP and agent workflow experience is recommended; Agentic RE provides that foundation.
How It Differs From the Other Courses
| Course | Primary Focus |
|---|---|
| Everyday Ghidra | Build practical Windows reverse engineering fundamentals. |
| Patch Diffing in the Dark | Learn the Windows CVE research craft by hand, adding AI after the fundamentals. |
| Agentic RE | Build private AI stacks, tool integrations, and agentic workflows across platforms. |
| Windows Agentic VR | Direct those workflows through advanced Windows vulnerability investigation and reproduction. |
You do not need to purchase every earlier course. Equivalent practical experience is welcome.
Lab Preparation
The research environment combines isolated Windows targets, static-analysis tools, Windows debugging tools, and access to model inference. Setup instructions belong to the edition you are attending, including whether the Windows labs are hosted or run locally.
See the course schedule for registration links. Each event supplies its own duration, daily schedule, pricing, and laptop requirements. The CSL virtual cohort page has dates, pricing, and the daily schedule.
The CSL virtual edition runs over four days, with live instruction 09:00–13:00 US Eastern (America/New_York) and the other half of each day for exercises. The first cohort is February 1–4, 2027. Conference editions have their own schedules.
Instructor
Research Behind the Course
At [un]prompted.au in Sydney on September 18, 2026, John presented To the Frontier and Back Again. The talk examines two Windows findings, the human direction behind a fast frontier-model result, and comparison with local open-weight models. Its focus on evidence, researcher judgment, and model limitations informs this course's direction. Read the conference writeup above or the original talk abstract.
About John
John McIntosh (@clearbluejar) is the researcher behind ghidriff and PyGhidra-MCP. He teaches reverse engineering, vulnerability research, and agentic workflows through Clearseclabs, drawing on the tools and methods he develops in his own research.
Discuss Windows Agentic VR
Interested in a public virtual cohort or this direction for your team? Tell us about your Windows RE/VR experience, preferred time zone, and the research problems you want to work on. We’ll follow up about fit and upcoming dates. This inquiry does not reserve a seat.
Let’s Talk Windows Agentic VR
Tell us what you want to work on. We’ll follow up to discuss fit and next steps.
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