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Completely Automate Penetration Testing with AI

#ai #pinggy #webdev #devops

Software development has entered a phase where shipping fast is the norm. Teams push updates frequently, AI assistants generate code quickly, and CI/CD pipelines keep releases moving. While this pace helps innovation, security testing often struggles to keep up. Many organizations still depend on occasional manual penetration testing, which means large portions of newly deployed code remain unchecked for long periods.

This growing gap between development speed and security validation increases risk. As applications evolve, vulnerabilities can quietly accumulate without detection.

The idea behind penetration testing AI security, Shannon cybersecurity Pinggy, automated pentesting web security is to bring automation and intelligence into this process so that security testing can match the pace of development.

The Problem With Traditional Pentesting

Conventional penetration testing is not only costly but also slow to organize. Scheduling a full engagement can take weeks. By the time a detailed report is delivered, the application has often changed significantly. New endpoints may have been added, authentication logic updated, and integrations introduced.

Automated scanners attempt to fill this gap, but they frequently generate excessive warnings. Many flagged issues cannot actually be exploited. Developers then spend time investigating alerts that may not matter, while real threats sometimes remain unnoticed.

What teams truly need is confirmation of real vulnerabilities, not just theoretical ones.

Understanding Shannon

Shannon is an open source AI-powered penetration testing system designed to operate like a human security analyst. Instead of merely highlighting possible weaknesses, it investigates whether those weaknesses can be exploited.

Its workflow includes:

  • Examining application source code
  • Interacting with the running application using automated browsing
  • Creating real attack payloads
  • Reporting only validated vulnerabilities

If a working exploit cannot be created, the issue does not appear in the final report. This approach helps reduce noise and eliminates false positives.

Shannon has demonstrated strong performance in standardized evaluations such as the XBOW benchmark, achieving a success rate of over 96 percent in identifying real vulnerabilities without hints. Beyond benchmarks, it has uncovered numerous critical issues in intentionally vulnerable applications used for testing security tools.

Currently, Shannon focuses on major vulnerability categories defined by OWASP:

  • Injection vulnerabilities, including SQL and command injection
  • Cross-Site Scripting
  • Server Side Request Forgery
  • Weak or broken authentication and authorization

The system runs entirely in Docker containers and relies on Anthropic’s Claude models as its reasoning engine. It also incorporates tools like Nmap, Subfinder, WhatWeb, and Schemathesis to gather intelligence about the target application.

The Four-Stage Workflow

Shannon follows a structured process that mirrors the workflow of experienced penetration testers.

Reconnaissance

The first step involves mapping the application’s attack surface. Shannon analyzes source code and interacts with the live application through automated browsing. It identifies endpoints, API routes, login flows, and all possible input points.

This phase is similar to a human tester exploring an unfamiliar system to understand its structure and behavior.

Vulnerability Analysis

Specialized AI agents then examine how user input flows through the application. They track data from entry points to sensitive operations such as database queries, system commands, and rendered output.

From this analysis, Shannon builds a set of potential vulnerability paths that could lead to exploitation.

Exploitation

This phase distinguishes Shannon from traditional scanners. Instead of stopping at potential risks, it attempts real attacks. Payloads are generated and submitted through forms or APIs. The system checks whether the attack succeeds.

If an exploit fails, it is ignored. Only confirmed vulnerabilities move forward.

Reporting

After testing is complete, Shannon generates a detailed report in Markdown format. Each entry includes a description of the issue, severity level, affected endpoints, and a ready to use proof of concept exploit.

All outputs are stored within the audit logs directory for review.

Requirements Before Setup

Before running Shannon, prepare the following:

  • Docker installed on your system
  • An Anthropic API key
  • Access to your application’s source code
  • A running version of the application to be tested

Because Shannon performs white box analysis, it requires access to the repository.

Setting Up Shannon

Clone the Repository 

git clone https://github.com/KeygraphHQ/shannon.git
cd shannon
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Add Your API Key

You can export the key directly:

export ANTHROPIC_API_KEY="your-api-key-here"
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Or store it in a .env file:

ANTHROPIC_API_KEY=your-api-key-here
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Place Your Application Repository

Clone your application into the ./repos/ directory:

git clone https://github.com/your-org/your-app.git ./repos/your-app
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Multiple repositories or monorepos can also be organized inside this folder.

Optional Authentication Configuration

If your application requires login, create a YAML configuration file inside ./configs/. This file describes how Shannon should authenticate and which routes to prioritize or ignore.

Instructions for login can be written in simple language. Shannon’s browser automation follows them step by step.

Start the Pentest

Run the command:

./shannon start URL=https://your-app.com REPO=your-app
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If you created a configuration file:

./shannon start URL=https://your-app.com REPO=your-app CONFIG=./configs/my-config.yaml
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During the first run, the required Docker images will be downloaded automatically.

Track Progress

View logs in real time:

./shannon logs
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Check a specific workflow:

./shannon query ID=shannon-1234567890
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You can also open the Temporal interface at http://localhost:8233/namespaces/default/workflows to monitor agents and workflow status.

Access Results

Reports are stored in:

./audit-logs/{hostname}_{sessionId}/
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This directory includes session data, agent logs, prompt snapshots, and the final comprehensive report.

You may also choose a custom output directory:

./shannon start URL=https://your-app.com REPO=your-app OUTPUT=./my-reports
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Testing Local Applications Using Pinggy

Many developers want to test applications running on their local systems. Since Shannon operates within Docker containers, direct access to localhost is not always straightforward.

Pinggy offers a simple solution by creating a secure public tunnel to your local application.

Start the Local Application

If your app runs on port 3000:

npm install
npm start
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It will be accessible at http://localhost:3000.

Create the Tunnel

Run the following command:

ssh -p 443 -R0:localhost:3000 free.pinggy.io
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Pinggy will generate a public HTTPS URL that forwards traffic to your local server.

Run Shannon Against the Tunnel

Use the generated URL as the target:

./shannon start URL=https://your-generated-link.a.free.pinggy.link REPO=your-app
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Shannon will now test your local application through the secure tunnel and complete its full workflow, including reconnaissance, analysis, exploitation, and reporting.

This setup allows security testing before deployment to staging or production and enables teams to share a consistent testing endpoint.

Managing Sessions

Stop containers while keeping data:

./shannon stop
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Remove everything completely:

./shannon stop CLEAN=true
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Rethinking Security in Fast-Moving Teams

Automated penetration testing powered by AI changes how security fits into modern development. Instead of waiting months for manual reviews, teams can run comprehensive tests whenever needed. Shannon reads code, interacts with applications, generates real exploits, and produces clear reports without overwhelming developers with unnecessary warnings.

When combined with Pinggy for secure tunneling, even locally running applications can be tested thoroughly. This approach allows security validation to happen continuously alongside development rather than after deployment.

As development cycles continue to accelerate, integrating intelligent automated testing into the workflow is becoming less of an option and more of a necessity.

Reference

Completely Automate Penetration Testing with AI

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