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Scan your code to see where user data is going

Hacker News

Scan your code to see where user data is going

Hey everyone! We’ve been working on a static code analysis tool to map out where user data is flowing at the code level and catch potential privacy violations; you can check it out here: https://github.com/monoid-privacy/monoid/tree/master/monoid-... To run it via CLI, use the Docker command in the README with a local directory, and the tool will scan the directory and print detected user data sources, sinks, and paths. In short, the tool converts code to a code property graph (CPG), extracts the sources and sinks from the CPG, and uses the variable/function names to determine whether the sources could contain user data & the sinks could be sensitive outputs (e.g. logs, DB, analytics/marketing tools, etc.). The output is a list of potential user data variables (the scanning is fairly robust, so it detects everything from standalone variables to class attributes) and the outputs they eventually flow to (e.g. a "first_name" variable that makes its way to Segment). The goal here is to “shift privacy left” and make it easier to find potential privacy headaches, like user data leaking into logs, earlier in the software lifecycle. The tool slots easily into CI/CD for privacy checks on every commit, and can also be run ad-hoc via the CLI. This was also a pretty exciting build from a technical perspective; OSS tooling around code graph generation and static analysis is pretty sparse (though https://github.com/Fraunhofer-AISEC/cpg offers a great foundation), so we built out a lot of code property graph generation + manipulation logic from the ground up. Feedback would be much appreciated!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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 HuntOn track for Day 1 leaderboard · Strong signals: user, dock, code · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
35%35% 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
32%32% 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
11%11% 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.

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