intelligent-machine-learning / intelligent-machine-learning/dlrover
Would you take a pull-request security scan workflow?
- Dominant language
- Python
- Stars
- 1.7k
- Forks
- 219
- Avg merge
- 10h 11m
- Merged PRs (30d)
- 11
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
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