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Scan GitHub Actions for Malicious Code

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Scan GitHub Actions for Malicious Code

Hello HN! Creator and maintainer of vet here. We have been working on malicious OSS code identification through static code analysis for a while. After the recent tj-actions/changed-files hack, we wanted the ability scan GitHub Action code as well since most of them are Javascript anyway. One of the challenges with scanning GHA compared to other package registries such as pypi, npm is immutability. While npm, pypi etc. guarantees immutability for a published version of artifact, GHA tags are mutable. This means we cannot trust the results of a tag (e.g. v1) scanned in the past. To workaround this problem for GitHub Actions scanning, we choose to always resolve a given tag/branch to a commit SHA before actually performing code analysis.

Visit productView on Hacker NewsMar 20, 2025
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
77%77% 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 · Missing: supports, reddit linkedin, podcasting
52%52% 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
37%37% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
28%28% 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
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
6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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