The public Snowflake incident is a useful test case for one question I keep coming back to: in a mixed workflow with both deterministic static analyzers and AI review, which one actually catches the injection?
I decided to find out empirically instead of arguing from vibes. I pulled the exact vulnerable workflow pattern and ran zizmor 1.29.0 against it in a sandbox.
Result: it flags the injection line. Rule template-injection, description “code injection via template expansion”, High confidence / High severity, pointing at jira_issue.yml:24:29 and naming github.event.issue.title as attacker-controllable input that can expand into a command. It reproduces with a plain zizmor --quiet run; the JSON output carries the same finding.
Here is the boring mechanics of the bug, because that is the part worth understanding and it is checkable by eye:
The workflow interpolates the issue title directly into a shell script:
run: |
TITLE=$(echo '${{ github.event.issue.title }}' | sed 's/"/\\"/g' | sed "s/'/\\'/g")The sed escaping runs after GitHub’s ${{ }} template expansion, not before. A single quote in the issue title breaks out of echo '...' and reaches the shell. That is the escape-ordering bug: you cannot escape a value that has already been interpolated into an execution context.
Two structural bugs are worth separating because they fail at different layers:
- Escape-ordering. The sanitizer runs after the expansion that made the value dangerous, so it is pure theater. This is structural, not a model failure, and you do not need AI to explain it. A linter rule can and does catch it.
- The protective
if:guard does not match the actual invocation path. A guard that only makes sense for a handler it never fires for is dead weight. Again fully statically checkable.
The part I found notable: this is the same workflow that GH Advanced Security scanned and did not flag, and an AI autofix was associated with the same PR in a related file. So you get a clean A/B in the wild: a deterministic rule with an autofix caught the exact dangerous line, while the AI-assisted layer shipped a fix elsewhere in the same change.
That is not a claim that static analysis replaces code review. It is a claim about layering and about test selection. If you have a static analyzer that flags ${{ }} interpolation into run: with High confidence, that is reproducible evidence a given PR needs a human or an AI read on that specific line. The value of the deterministic layer is that it nominates the exact places where judgement is required.
The reproducibility point is the one I want to keep: every claim here was derived by running a pinned tool version against a known input, not by reading a vendor’s marketing page. That is the standard I would like the rest of the field held to. If a review tool claims it catches script injection, ask for the flag it produces on this workflow. It either names the line or it does not.