bug-bounty-hunting-prompts: Open-Source Prompt Pack for Bug Bounty Hunters

Security researcher Marius du Preez (GitHub: mdpsec, X: @mdp_sec) has released bug-bounty-hunting-prompts, an open-source collection of structured, reusable prompts designed specifically for bug bounty hunters and penetration testers. The repository maps out disciplined workflows across the critical phases of offensive security engagements, spanning initial asset reconnaissance, authenticated access testing, and vulnerability triage.

Image source: GitHub @mdpsec / X @NitinGavhane_

The toolkit gained traction across the ethical hacking and vulnerability research community after security researcher Nitin Gavhane (@NitinGavhane_) highlighted the repository on X (formerly Twitter). Designed to replace ad-hoc, free-form queries with structured analytical frameworks, the prompts help practitioners systematically explore attack surfaces using modern LLM interfaces without relying on heavy agent frameworks.

Structured Methodology: From Reconnaissance to Vulnerability Triage

While generative AI models are increasingly utilized in offensive security research, unguided queries often yield unfocused, superficial, or hallucinated findings. bug-bounty-hunting-prompts decomposes the penetration testing lifecycle into distinct operational phases, guiding models to maintain analytical consistency.

• Reconnaissance (Recon) Prompts: Guides researchers in systematically structuring initial asset discovery and attack surface mapping into a coherent workflow.
• Authenticated Access Testing: Focuses beyond public endpoints on analyzing access control boundaries and privilege states within authenticated environments.
• Vulnerability Triage Prompts: Assists researchers in reviewing, categorizing, and summarizing potential security findings for reproducible technical reporting.

The prompts require no specialized dependencies or proprietary frameworks. Researchers can clone the GitHub repository and copy the modular templates directly into their preferred LLM interfaces and offensive testing workflows.

Community Reception and the Practical AI Security Tooling Landscape

The release gained visibility when Nitin Gavhane shared the repository with the wider bug bounty community, drawing positive engagement from peer researchers alongside a supportive response from creator Marius du Preez ('Enjoy').

As automated hacking bots raise concerns over steep API token expenditures—an issue highlighted by industry veterans like Jason Haddix regarding heavy token burn across asset sets—and uncontrolled agent loops prove inefficient, disciplined prompt packs provide a controllable alternative that keeps the human researcher firmly in command.

AI Hallucination Backlash, Scope Boundaries, and Responsible Disclosure

Alongside its operational utility, applying bug-bounty-hunting-prompts in live testing environments requires strict adherence to security ethics and legal boundaries.

• Mandatory Manual Verification: The influx of unchecked, AI-hallucinated vulnerability reports has placed immense strain on triage teams. Recently, major initiatives including the Google Open Source Software Vulnerability Reward Program (Google OSS VRP) moved to suspend product vulnerability submissions following a flood of invalid automated AI submissions. Security researchers must never forward raw LLM outputs to bounty programs; every potential finding requires independent manual verification and concrete Proof of Concept (PoC) validation on the target.
• Strict In-Scope Adherence: Autonomous or AI-guided tooling risks drifting outside program boundaries if left unmonitored. All prompt-assisted research must be strictly confined to authorized in-scope targets defined by legal bug bounty policies, with no unauthorized exploration of out-of-scope assets or private pipelines.

By pairing structured prompting for reconnaissance and triage with rigorous human validation, bug-bounty-hunting-prompts offers security researchers a disciplined way to accelerate analysis without contributing to automated report noise.

Sources

• GitHub Repository: mdpsec/bug-bounty-hunting-prompts
• Original Signal: Nitin Gavhane on X (@NitinGavhane_)