AIPentestKit is an open-source toolkit that applies artificial intelligence and large language models to everyday red team tasks. The project is primarily documented in Chinese and aggregates modules for browser analysis, Burp Suite fuzzing plugins, webshell bypass helpers, and a completed tasklist analysis plugin called GodzillaPlugin-tasklist. The repository aims to accelerate repetitive workflows such as payload generation, context-aware fuzz dictionary creation, and automated triage, while leaving critical decisions to an operator. The README lists implemented modules, progress status, and references to complementary projects.

Features

• GodzillaPlugin-tasklist — AI-powered analysis of Windows tasklist output to prioritise high-value processes for follow-up investigation.
• FuzzMind — Burp Suite plugin that generates adaptive fuzzing dictionaries using an AI model and target context. The README states it integrates with Burp Intruder.
• AI-assisted SQLMap enhancements — planned modules to analyse database schemas and produce targeted payloads, marked as in progress.
• Webshell bypass helpers — modules listed for intelligent webshell evasion techniques; status is work in progress.
• Project roadmap and references to related AI tooling for code analysis, payload generation, and terminal assistants.

Installation

The repository is a multi-module collection rather than a single packaged binary. The recommended first step is to clone the repository and follow module README files for individual components. Use the following as a starting point:

Each submodule (for example GodzillaPlugin-tasklist or FuzzMind) contains its own documentation and integration notes. The main README does not provide a single install script or a CLI binary. Follow the per-module README for requirements such as Java and Burp extension installation steps, Python dependencies or model API keys.

Usage

The repository does not expose a unified command line interface with a documented --help output. Usage is module-specific and typically involves installing a Burp extension, dropping a plugin jar into Burp’s extension folder, or running a local script that communicates with an AI backend. For example, the README describes FuzzMind as a Burp Intruder plugin that supplies adaptive dictionaries; installing it requires using Burp Suite’s Extensions manager and following the plugin README. GodzillaPlugin-tasklist is documented as a completed plugin that analyses tasklist output and returns ranked process suggestions. For exact per-module commands, consult the module README files in the cloned repository.

Offensive scenario

An operator is performing web application testing against an asset with complex input parsing and partial WAF rules. The operator installs the FuzzMind Burp plugin and configures it to use a contextual prompt describing the application’s behavior and observed response patterns. FuzzMind generates a focused fuzz dictionary that combines traditional payloads with AI-synthesised variants tuned to the target’s input sanitisation quirks. The operator runs Burp Intruder with the generated dictionary and uncovers a subtle SSRF that standard dictionaries missed. The operator then uses the GodzillaPlugin to prioritise local processes on a compromised host to identify a likely persistence daemon for lateral movement planning.

Red team relevance and recommended use

AIPentestKit is valuable for reducing repetitive manual work and for creating better starting points for manual analysis. It is not a replacement for operator judgment. Use cases that benefit most include adaptive fuzzing, targeted payload generation where contextual nuance matters, and rapid triage of noisy outputs such as tasklist dumps. Operators should validate AI outputs before using them in active exploitation to avoid false positives or unintended destructive actions.

Detection and defensive notes

AI-augmented tooling introduces distinct detection opportunities for defenders. Monitor for:

• New or unusual Burp Suite extension installations on analyst or build hosts.
• Automated generation of large or templated payload sets that include consistent markers or LLM artifacts.
• Bursty, context-driven fuzzing patterns that diverge from traditional dictionary distributions.

Defenders can also deploy canary inputs or known prompts to detect AI-assisted reconnaissance and instrument CI and endpoint controls to detect unusual toolchain installs.

Comparison and related reading

AIPentestKit falls into the same emerging category as other AI-assisted penetration testing projects covered on Darknet. For context and detection guidance, see related articles on Darknet such as PentestGPT, Nebula — Autonomous AI Pentesting Tool, and the recent LLAMATOR — Red Team Framework for Testing LLM Security. These pieces help frame detection and OPSEC tradeoffs when adopting AI toolchains.

Limitations and ethical considerations

The repository contains modules at varying levels of completion. Several features are listed as planned or in progress. The README is in Chinese; non-Chinese readers should carefully translate the module READMEs before running. Always obtain explicit written permission before running AI-augmented offensive tooling against third-party systems. AI-generated payloads can include unexpected behavior; validate outputs in isolated lab environments.

Conclusion

AIPentestKit demonstrates the practical value and current limitations of AI-augmented red team tooling. It provides useful plugins and modules for Burp Suite integration and task list analysis while remaining a work in progress. For teams adopting AI-assisted methods, combine tool outputs with operator verification, closely monitor toolchain installations, and instrument telemetry to detect templated AI outputs.

You can read more or download AIPentestKit here: https://github.com/Conan924/AIPentestKit