Add aicaudit (Python SAST: taint paths + evidence-grounded AI verdicts) - #1894
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Contributing criteria check[FAIL]
|
| Criterion | Result |
|---|---|
| Stars (min 20) | fail 0 stars (minimum is 20) |
| Contributors (min 2) | fail 1 contributor(s) (minimum is 2) |
| Age (min 6 months) | fail created 33 days ago, needs 150 more days to meet the 6-month minimum |
One or more tools do not meet the contributing criteria yet, or a criterion could not be verified. We will keep this PR open. Feel free to update it once the thresholds are met.
Contributing criteria check[FAIL]
|
| Criterion | Result |
|---|---|
| Stars (min 20) | fail 0 stars (minimum is 20) |
| Contributors (min 2) | fail 1 contributor(s) (minimum is 2) |
| Age (min 6 months) | fail created 35 days ago, needs 148 more days to meet the 6-month minimum |
Thank you for sharing your tool! One or more tools do not yet meet the contribution criteria, as shown above, so we are closing this pull request.
You are welcome to submit a new pull request once all criteria are met. Thank you for your contribution!
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Adds aicaudit (MIT,
pip install aicaudit).input()/os.environ) to sinks across function boundaries and proves constants safe, eliminating classic false-positive classes. Optional AI verification is evidence-grounded (the LLM sees the full function + traced path) and fail-safe (three-state verdicts, never auto-confirms).data/tools/*.ymlschema (checked tags againstdata/tags.yml: python, security, uses-llm).Happy to adjust tags/description per maintainer preferences.