PentesterFlow Launches Open-Source AI CLI Tool for Penetration Testers and Bug Bounty Hunters

PentesterFlow is a new open-source, human-in-the-loop AI command-line tool designed specifically for penetration testers and bug bounty hunters. It automates the full workflow from reconnaissance to report generation while requiring explicit analyst approval before executing sensitive commands. The tool addresses common issues in agentic AI security tools such as hallucinations, weak context retention, and poor tool integration by incorporating built-in pentesting skills and evidence-based vulnerability confirmation. It supports connections to local or hosted LLMs including Ollama, Gemini, Groq, and others, and features continuous local learning that stores user preferences and lessons without retraining models. A key differentiator is its integration with Burp Suite and a permission-based execution model that includes a YOLO mode for isolated environments. The project positions itself as a transparent alternative to fully autonomous tools like PentAGI and PentestGPT.

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PentesterFlow is a new open-source, human-in-the-loop AI command-line tool built for penetration testers and bug bounty hunters. It automates the entire workflow from information gathering to report generation without removing analyst control.

Most agentic AI security tools suffer from hallucinated findings, poor context retention, and weak tool integration. PentesterFlow tackles these problems with built-in pentesting skills, evidence-based vulnerability confirmation, and continuous local learning capabilities.

The tool connects to local or hosted large language models such as Ollama, LM Studio, Kimi, Groq, Gemini, DeepSeek, and OpenRouter. It plans actions against defined targets, runs real security testing tools, and always requests explicit analyst approval before executing sensitive commands.

Core Capabilities

PentesterFlow covers the full penetration testing lifecycle: scoping, reconnaissance, enumeration, vulnerability validation, coverage tracking, reporting, and ongoing learning.

  • Model backends: Ollama, LM Studio, Kimi, Groq, Gemini, DeepSeek, OpenRouter and OpenAI-compatible APIs
  • Built-in skills: Recon, Web vulnerabilities, SSRF, SSTI, JWT, GraphQL, race conditions, subdomain takeover, Supabase, deserialization
  • Toolset: Shell/Bash, HTTP, Burp Suite bridge, browser capture, MCP support, file operations, grep/glob
  • Report output: Confirmed findings with PoCs, impact analysis, remediation advice, and ready-to-use curl commands

In a live demonstration, the tool loaded a web vulnerability skill module, sent HTTP requests against an order API, and automatically confirmed a high-severity IDOR vulnerability, writing the evidence-backed finding directly to a Markdown file.

Security and Integration

PentesterFlow enforces permission-based tool calls, blocks dangerous shell command patterns, and redacts credentials during compression and snapshot operations. It also offers a YOLO mode that auto-approves all actions inside isolated lab environments.

The tool integrates directly with Burp Suite through a companion bridge, allowing testers to import captured traffic into the CLI and export confirmed findings back as Burp issues.

Installation and Usage

Installation uses a simple shell script on macOS/Linux or PowerShell on Windows to fetch the latest standalone binary and verify its SHA-256 checksum. Users can pin specific versions, select local models such as Ollama’s qwen2.5-coder, or connect to hosted providers, then set targets with the /target command and issue natural-language instructions.

Analysts working on sensitive targets are reminded that PentesterFlow is intended solely for authorized security testing, as approved commands can execute real shell operations and send live HTTP requests.

Industry Positioning

Alongside projects such as PentAGI and PentestGPT, PentesterFlow distinguishes itself through transparent, reproducible evidence chains and mandatory analyst approval, making it suitable for security teams cautious about fully autonomous penetration testing agents.

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