Malinois – Open-Source iOS Unattended-Device Tamper Detection

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Built for evil-maid/insider-access scenarios where your device passcode may be known. Malinois’ singular objective is to guarantee, to the greatest extent possible, that you either return to an unattended device in the same state you left it, or know that unauthorized access was attempted (or succeeded). With Guided Access on, Side Button option off, and iOS set to ask before new wired accessories connect, there is no viable scenario (short of an already compromised device) where a tamper can occur without an indication. Designed around the iOS Guided Access feature, which is novel in the space as far as I can tell. This solves the iOS backgrounding issue that relegated the Guardian Project’s now-defunct Haven app to Android, and though the app’s goal is different, a lot of the same functionality exists. My goal with the post is feedback and hopefully some small, interested user base covering scenarios different from mine: This is a personal project, I am not an iOS developer, and the code is 100% Claude. My workflow is basically build with Claude, review and red-team with GPT/Deepseek/etc., then device testing with personal devices, and that can only take me so far. I think the concept and GA differentiating it from existing apps is real, but I also think the models are a bit sycophantic when it comes to discussing that, and would love to have people smarter than me poking holes in my premises and assumptions, not to mention the code. Guided Access: https://support.apple.com/en-us/111795 Haven: https://guardianproject.info/apps/org.havenapp.main/ App Store: https://apps.apple.com/us/app/malinois-device-anti-tampering...

Visit product View on Hacker NewsSep 8, 2026

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

91%91% 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: ios · Missing: supports, reddit linkedin, podcasting

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

TrustMRRFits verified-revenue profile · Strong signals: ios, personal, apps · Missing: mobile apps, entrepreneurs, video

65%65% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

50%50% 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 · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

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