PenPeeper–An Open-Source Pentesting Engagement Manager (Optional AI)

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PenPeeper – An Open-Source Pentesting Engagement Manager (with Optional AI) Most pentesting tools I’ve used fall into one of two buckets: absurdly expensive enterprise SaaS open-source tools that don’t help once scanning is done PenPeeper is my attempt to fix that. What it is A free, open-source, self-hosted pentesting engagement manager that focuses on the boring but critical parts: scoping & engagement tracking vulnerability management reporting tying everything together in one workflow The AI part (optional, not magic) PenPeeper can integrate with local or external LLMs (Ollama, LM Studio, ChatGPT, Claude, Gemini, OpenRouter). Runs on Windows (via WSL integration), MacOS, Linux The goal isn’t “AI replaces pentesters.” It’s: faster vuln analysis better first-draft reports less copy-pasting between tools You can run it fully local. You can turn AI off entirely. Why I built it Commercial tools are overpriced and locked down. Most open-source tools stop at scanning. Reporting is still manual, repetitive, and error-prone. That gap is what PenPeeper is trying to cover. Status Early but stable Actively developed Looking for real pentester feedback (not hype) Links Site: https://penpeeper.com GitHub: https://github.com/chetstriker/PenPeeper Feedback I want What part of your pentest workflow is still the most painful? Where does AI actually help vs get in the way? What would make this worth using on a real engagement? Happy to answer technical questions or take criticism.

Visit product View on Hacker NewsJan 27, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, claude · Missing: agents, agent, cursor

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: llama, io · Missing: https docs, excited, just released

30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas, active · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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