intelligent-machine-learning / intelligent-machine-learning/dlrover

Would you take a pull-request security scan workflow?

Open
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Dominant language
Python
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Forks
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Avg merge
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Merged PRs (30d)
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Description

Would a pull-request security scan be useful here, or is this already covered?

One workflow. On each pull request it scans the checked-out repository for
hardcoded credentials, injection, SSRF and unsafe deserialisation, and writes
findings to the Security tab. Report-only — findings never fail the build.

Two files under `.github/`, a pinned `@profullstack/threatcrush@0.11.0` whose tarball is hashed before
install, and `pull_request` rather than `pull_request_target`.

A pull request is open alongside this with the diff, if reading it is easier
than discussing it.

Disclosure: I maintain [ThreatCrush](https://github.com/profullstack/threatcrush);
MIT and free. Written with AI assistance. Closing this is a fine answer and I
will not ask again.

Contributor guide

Open the contributing guide

Research direction

Start by reading the open pull request diff and the two files under `.github/`, then compare them with any existing workflow coverage. Done means confirming the report-only scan runs on `pull_request`, uses the pinned and hashed ThreatCrush dependency, and publishes findings to the Security tab without failing the build.

Written by the indexing model from the issue text.

Assessment

Tech stack
github-actions
Domain
ci-cd, security
Issue type
Feature
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
25/100

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