PentesterFlow: An AI‑Driven Automated Workflow for Penetration Testers and Bug Bounty Hunters

PentesterFlow is a new open‑source, human‑AI collaborative command‑line tool designed for penetration testers and bug bounty hunters. It aims to automate the entire workflow—from scope definition, information gathering, enumeration, vulnerability verification, coverage tracking, report generation, to continuous learning—without sacrificing analyst control.

Many existing AI security agents suffer from hallucinations, weak context retention, and poor tool integration. PentesterFlow addresses these issues by embedding penetration‑testing skills, an evidence‑based vulnerability confirmation mechanism, and a persistent local learning system.

The tool can connect to local or hosted large language models (LLM) such as Ollama, LM Studio, Kimi, Groq, Gemini, DeepSeek, OpenRouter, or any OpenAI‑compatible API. It plans actions within a defined target range, runs real security tools, and requires explicit analyst approval before executing sensitive commands, thereby closing the trust gap in autonomous testing agents.

In a live demo, PentesterFlow loaded a "webvuln" skill module, sent HTTP requests to an orders API, automatically confirmed a high‑severity IDOR vulnerability, and recorded the evidence‑backed finding in a Markdown file.

Key capabilities include:

Model backends: Ollama, LM Studio, Kimi, Groq, Gemini, DeepSeek, OpenRouter, OpenAI‑compatible APIs.

Built‑in skills: Recon, Web vulnerabilities, SSRF, SSTI, JWT, GraphQL, race conditions, sub‑domain takeover, Supabase, deserialization.

Toolset integration: Shell/Bash, HTTP, Burp Suite bridge, browser capture, MCP support, file I/O, grep/glob.

Report output: Confirmed findings with PoC, impact analysis, remediation advice, and copy‑ready curl commands.

Memory ability: Session restoration, context snapshots, continuous local intelligent system.

The local continuous‑learning system silently stores user preferences, successful workflows, coverage gaps, and lessons from failed hypotheses in project‑specific and personal smart files, improving future sessions without retraining the underlying model.

Sensitive data such as keys are automatically redacted before disk writes, and duplicate memory entries are deduplicated before being sent to the model, keeping the workflow both intelligent and concise.

PentesterFlow enforces permission‑based tool invocation, blocking dangerous shell command patterns and sanitizing credentials during compression and snapshotting. A "YOLO mode" is also available for fully automated operation in isolated test environments.

Integration with Burp Suite is provided via a bridging tool, allowing captured traffic to be imported into the CLI and confirmed findings to be pushed back as Burp issues, streamlining the handoff between manual and AI‑assisted testing.

Installation is straightforward: a shell script for macOS/Linux or a PowerShell command for Windows fetches the latest standalone binary and verifies its SHA‑256 checksum. Users can lock a specific version, select a local LLM (e.g., Ollama’s qwen2.5‑coder) or a hosted provider (e.g., Gemini, Groq), set the target URL with a simple /target command, and issue natural‑language instructions such as “test the orders API for access‑control flaws.”

It is emphasized that PentesterFlow is intended only for authorized security testing, as approved runs can execute shell commands and send live HTTP requests.

With the emergence of projects like PentAGI and PentestGPT, PentesterFlow distinguishes itself through transparent, reproducible evidence chains and analyst approval mechanisms, making it suitable for security teams cautious about fully autonomous penetration‑testing agents.