Vulnetic's multi-purpose hacking agent

Hacker News

What it is: A hacking agent you run from a web UI. It enumerates, exploits, validates, and drafts reports. Why we built it: manual pentests are slow and scarce. We want repeatable coverage between human-led tests. What is different: a validator agent replays PoCs to cut false positives. You can lock the agent to your own methodology to control scope and steps. You can also give it tasks to complete. How to try: sign up, download the docker container, add a target, set rules, then run. Free credits on signup and no card required. Usage based pricing after that. What it can do today: Pretty much any asset type including AD, web applications, and cloud. Limits today: noisy targets and complex auth flows may need manual setup. It does not have the tooling for Wi-Fi pentesting or mobile apps. What we want feedback on: where it breaks, missing integrations, and what would make this useful for your team. For more information the website is: https://www.vulnetic.ai

Visit product View on Hacker NewsSep 24, 2025

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

88%88% 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: including · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

29%29% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

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