Bulletproof sessions – secure, cookieless session handling

Hacker News

I had this idea for a few years already and now I also managed to implement a proof of concept for it: instead of relying on cookies or tokens to identify a user, have a service worker intercept and sign all the requests to the server (with a private key generated when the service worker is initially installed). The server identifies the user based on the public key corresponding to the signature. BAM! no more cookie sessions, so no more sessions hijacking and session replay attacks. I also wrote a blog post [0] detailing some advantages over the traditional session handling mechanisms, but I feel this enables endless possibilities. Appreciate your thoughts&feedback! [0] https://programming.tudorconstantin.com/2025/03/bulletproof-...

Visit product View on Hacker NewsMar 24, 2025

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2points

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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos

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

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

34%34% 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

31%31% 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

19%19% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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