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Introduction:
The 2026 DEF CON Bug Bounty Village agenda reveals a cybersecurity landscape fundamentally reshaped by artificial intelligence. As LLM-generated reports flood triage queues and AI agents begin executing autonomous penetration tests, the gap between vulnerability discovery and remediation is widening at an unprecedented rate. This article distills the critical technical knowledge from the village’s talks and workshops—from exploiting custom URI handlers for RCE to building production-ready hackbots—providing a comprehensive guide to the offensive and defensive paradigms defining modern bug bounty.
Learning Objectives:
- Master the exploitation of arbitrary file write vulnerabilities across multiple programming languages and frameworks.
- Understand the IDE extension attack killchain and how to chain primitives from workspace access to full code execution.
- Learn to design, build, and integrate AI-powered penetration testing bots for reconnaissance and vulnerability discovery.
- Comprehend the operational challenges AI introduces to vulnerability disclosure programs (VDPs) and how to scale remediation.
- Acquire practical techniques for hacking AI systems and submitting high-impact, actionable reports.
You Should Know:
- Exploiting the Unprotected: From URI Handlers to Remote Code Execution
Bug bounty hunters frequently encounter unprotected URL inputs and dismiss them as low-impact. However, this presentation reveals how custom URI handlers in Microsoft enterprise applications can be pivoted from web applications into desktop software, leading to remote code execution (RCE). The core concept is that URI schemes (e.g., ms-appx://, custom-scheme://) can invoke local applications with arguments, creating an attack surface where a web-based vector transitions to a native, privileged execution context.
Step-by-Step Guide:
- Identify Custom URI Handlers: On Windows, enumerate registered URI schemes using the registry. Open `regedit` and navigate to `HKEY_CLASSES_ROOT` to find schemes like
ms-settings,outlook, or custom application handlers. Alternatively, use the command:Get-ChildItem -Path "HKCR:\" -Recurse | Where-Object { $_.PSChildName -match "://" } - Analyze Handler Arguments: Examine the `shell/open/command` key for each handler to determine the executable and the argument format. Look for parameters passed via the URI.
- Craft a Malicious URI: Construct a URI that injects additional arguments or commands into the handler. For example, if an application opens a file based on a URI parameter, attempt path traversal:
custom-scheme://open?file=../../../../windows/system32/calc.exe
- Deliver the Payload: Host the malicious URI in an iframe or as a link on a web page. When a victim clicks the link, the local application executes with the injected arguments.
- Achieve RCE: If the application does not sanitize arguments, you can chain this with DLL hijacking or other techniques to achieve full remote code execution.
2. Hacking IDE Extensions: The VSCode Workshop Killchain
IDE extensions represent a lucrative and often overlooked attack surface. They run with privileged capabilities, parse arbitrary workspace files, and increasingly ship LLM-backed agentic features that execute commands and edit files. The “Nopilot” workshop simulates a deliberately vulnerable AI assistant VS Code extension, teaching a generalized exploitation killchain drawn from a multi-six-figure bug bounty practice.
Step-by-Step Guide:
- Install the Vulnerable Extension: Download and install “Nopilot” (or a similar intentionally vulnerable extension) in VS Code.
- Debug the Extension: Open the extension’s source code. Set breakpoints in the `activate` function and any command handlers. Use the VS Code debugger to attach to the extension host process.
- Review Code for Bugs: Look for common vulnerabilities:
Command Injection: Functions that execute shell commands using user-controlled input (e.g.,child_process.exec).
Path Traversal: File operations that do not validate workspace paths.
Insecure Deserialization: Parsing untrusted data from workspace files. - Chain Primitives: The killchain often starts with “user opened a folder.” The extension reads a malicious `.vscode/settings.json` or a specific file that triggers a vulnerability.
- Bypass Workspace Trust: Craft a payload that, when parsed, leads to code execution without requiring the user to explicitly trust the workspace. This is achieved by exploiting the extension’s privileged nature and lack of sandboxing.
-
Cloud-Scale Vulnerabilities and the 3 Principles of Modern VDPs
Bug bounty researchers rarely see the true impact of their work. A single vulnerability finding can have a blast radius spanning services, regions, and third-party dependencies. AI vulnerability discovery tools are now uncovering valid vulnerabilities faster than traditional VDP models were designed to process. This session introduces the “3 Principles for modern VDPs,” forged from operating vulnerability disclosure at the world’s largest cloud infrastructure.
Step-by-Step Guide for Defenders:
- Scale Triage with AI: Implement AI-assisted triage to filter LLM-generated noise and prioritize genuine findings. Train models on historical report data to identify patterns of high-impact vulnerabilities.
- Adopt a Risk-Based Prioritization: Move beyond CVSS scores. Assess the true blast radius of a vulnerability within your cloud environment. Use asset inventory and dependency mapping to understand reachability.
- Automate Remediation Workflows: Integrate VDP with your CI/CD pipeline. For certain classes of vulnerabilities (e.g., misconfigurations), automate the creation of pull requests or infrastructure-as-code updates.
- Close the Feedback Loop: Provide researchers with detailed information on how their findings were used. This encourages higher-quality reporting and builds a stronger community.
Linux/Windows Commands for Cloud Hardening:
- Linux (Auditing Dependencies):
List all installed packages and their dependencies apt-cache rdepends --installed <package_name> Check for open ports and listening services ss -tulpn
- Windows (Auditing Services):
Get all services and their associated paths Get-Service | Select-Object Name, DisplayName, Status, ServiceType Check for insecure service permissions sc sdshow <ServiceName>
4. Building AI-Powered Penetration Testing Bots (Hackbots)
This hands-on talk builds a functional hackbot for common offensive tasks, demonstrating asset discovery, endpoint analysis, and mutation-focused testing (e.g., XSS/SSRF). The core architecture involves single-purpose vs. multi-stage bots, context engineering for targeted prompting, and tool integration with output parsing workflows.
Step-by-Step Guide to Building a Hackbot:
- Define the Objective: Choose a single, well-defined task, such as “discover all subdomains for a target domain.”
- Design the Context: Create a system prompt that defines the bot’s role, the tools available, and the expected output format. For example:
You are a subdomain enumeration assistant. Your task is to use the provided tools to find subdomains for a given domain. Output the results as a JSON array.
- Integrate Tools: Connect the LLM to tools like
subfinder,amass, or `dnsx` via a tool-calling interface. Parse the output of these tools and feed it back to the LLM. - Implement Hallucination Mitigation: Instruct the LLM to only report findings from tool outputs, not to generate its own. Implement a validation step that checks the existence of discovered subdomains using DNS resolution.
- Optimize Cost and Auditability: Log all prompts, tool calls, and outputs. Use a cheaper, faster model for intermediate steps and a more capable model for final analysis.
Example Python Snippet for Tool Integration:
import subprocess
import json
def run_subfinder(domain):
result = subprocess.run(['subfinder', '-d', domain, '-silent'], capture_output=True, text=True)
return result.stdout.splitlines()
LLM would call this function based on the prompt
subdomains = run_subfinder('example.com')
print(json.dumps(subdomains))
- Write Once, Shell Everywhere: Universal Arbitrary File Write to RCE
Arbitrary file write bugs are often treated as “almost critical.” This talk closes the gap by presenting novel primitives that enable shell access across the most popular programming languages and frameworks, all with minimal information about the target application.
Step-by-Step Guide:
- Identify a File Write Primitive: Locate a vulnerability that allows writing a file to an arbitrary location on the server. This could be a path traversal in a file upload, a template injection that writes logs, or a configuration write.
- Determine the Target Framework: Identify the programming language and framework (e.g., PHP, Python/Flask, Node.js/Express, Java/Spring).
- Craft a Framework-Specific Payload: Write a file that will be executed or included by the framework.
PHP: Write a web shell to a directory accessible via the web root: ``
Python (Flask): Overwrite a template file to include a malicious payload that executes on render.
Node.js: Write to `package.json` to inject a preinstall script that runs on the next deployment. - Leverage Log Poisoning: If you can write to log files, inject PHP or other code that gets interpreted when the log is viewed.
- Achieve Code Execution: Trigger the execution of your payload by accessing the written file (e.g., visiting the web shell URL) or waiting for the application to automatically include it.
What Undercode Say:
- The convergence of AI and bug bounty is creating both unprecedented opportunities and critical bottlenecks. AI agents accelerate reconnaissance, but also generate noise that threatens to overwhelm traditional triage pipelines.
- The IDE extension attack surface is a goldmine for researchers. The trust model of “workspace trust” is fundamentally broken when extensions can execute arbitrary commands with minimal sandboxing.
- Modern vulnerability disclosure must evolve to handle the velocity of AI-discovered vulnerabilities. The future lies in automated triage, risk-based prioritization, and closing the feedback loop with researchers.
- Building hackbots is not a novelty but a necessity. Integrating AI into offensive workflows requires a disciplined approach to context engineering, tool integration, and hallucination mitigation.
Prediction:
- -1 The widening gap between discovery and remediation will lead to a significant increase in successful exploits of known vulnerabilities, as attackers leverage AI to automate exploitation at scale.
- +1 AI-powered triage and automated remediation workflows will mature, enabling large-scale VDPs to process report volumes that are 10x higher than current capacities.
- -1 IDE extensions will become a primary vector for supply chain attacks, with malicious or compromised extensions gaining access to developer credentials and source code.
- +1 The democratization of AI hackbots will lower the barrier to entry for bug bounty hunting, expanding the researcher pool and uncovering vulnerabilities in previously overlooked systems.
- -1 The quality of bug reports will decline as LLM-generated submissions flood programs, requiring significant investment in AI-based filtering and validation mechanisms.
- +1 The Bug Bounty Village at DEF CON will serve as a critical catalyst for defining new industry standards and best practices for AI-integrated security research and vulnerability disclosure.
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