Zen-AI-Pentest: Open-source AI-powered penetration testing framework

Zen-AI-Pentest provides an open-source framework for scanning and exercising systems using a combination of autonomous agents and standard security utilities.

The project aims to let users run an orchestrated sequence of reconnaissance, vulnerability scanning, exploitation, and reporting using AI guidance and industry tools like Nmap and Metasploit. It is written to support command line, API, and web interfaces.

Multi-agent structure and integrated tools

Zen-AI-Pentest organizes its functionality around a set of agents that handle discrete phases of a security assessment. A reconnaissance agent performs initial information gathering. A vulnerability agent executes scanning tools. An exploit agent attempts to validate findings. A report agent compiles results. Each agent forms part of a broader state machine that controls a sequence of actions.

The framework incorporates a range of established security tools. Nmap is included for network discovery. SQLMap handles database-related vulnerability checks. Metasploit is available for exploit execution. The system also integrates external threat intelligence and LLMs through vendor APIs.

Zen-AI-Pentest provides interfaces for users to interact with the system. A REST API can be called by other applications. A web UI presents results in a visual format. A command line option lets practitioners invoke functions directly.

AI involvement and risk handling

The repository’s design uses LLMs to influence decision making during a penetration test. The AI interacts with the state machine to guide which tools and scanning strategies to use. It can suggest follow-up actions based on the outputs of earlier steps.

A risk engine attempts to quantify the impact and likelihood of findings generated by the system. It applies standard scoring metrics such as CVSS and EPSS to assess vulnerabilities. The framework also includes a voting mechanism that compares outputs from multiple models to reduce uncertain or erroneous results.

The exploit validation phase uses sandbox environments created with containerization. This setup captures evidence like screenshots, HTTP captures, and packet traces while keeping the execution isolated from production systems. A record of actions and findings is maintained for audit purposes.

Benchmarks and performance

Zen-AI-Pentest includes a benchmarking section for users to compare results with other frameworks and manual work. Scenarios cover common test targets such as intentionally vulnerable applications from learning platforms. Metrics collected include how long vulnerabilities were found, how many were discovered, and rate of false positives.

These comparisons are intended to give security teams a basis for evaluating where automated workflows produce acceptable results relative to manual approaches or other tools. The repository’s benchmark subsystem also reports in visual formats for easier interpretation.

DevOps pipelines and reporting options

Zen-AI-Pentest works with continuous integration systems. GitHub Actions, GitLab CI, and Jenkins are all supported through direct integration files. Results can be output as JSON, XML, or SARIF formats, which are useful for automated tracking in development and security pipelines.

The reporting agent can send alerts through channels such as Slack and email. It records results in a way that fits existing ticketing systems. This allows security teams to make findings actionable within broader workflow tools.

Zen-AI-Pentest is available for free on GitHub.

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