[CI] Remove template injection vulnerable bits from Serverless GHA Workflows #30631
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Serverless Benchmark Results
tl;drUse these benchmarks as an insight tool during development.
What is this benchmarking?The The benchmark is run using a large variety of lambda request payloads. In the charts below, there is one row for each event payload type. How do I interpret these charts?The charts below comes from The benchstat docs explain how to interpret these charts.
I need more helpFirst off, do not worry if the benchmarks are failing. They are not tests. The intention is for them to be a tool for you to use during development. If you would like a hand interpreting the results come chat with us in Benchmark stats |
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[Fast Unit Tests Report] On pipeline 47884641 (CI Visibility). The following jobs did not run any unit tests: Jobs:
If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help |
Regression DetectorRegression Detector ResultsRun ID: 316d203e-0d12-4232-b983-6a41ab074c08 Metrics dashboard Target profiles Baseline: a0e73e3 Performance changes are noted in the perf column of each table:
No significant changes in experiment optimization goalsConfidence level: 90.00% There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.
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| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | otel_to_otel_logs | ingress throughput | +1.17 | [+0.36, +1.98] | 1 | Logs |
| ➖ | basic_py_check | % cpu utilization | +0.98 | [-1.76, +3.73] | 1 | Logs |
| ➖ | quality_gate_idle | memory utilization | +0.36 | [+0.32, +0.41] | 1 | Logs bounds checks dashboard |
| ➖ | pycheck_lots_of_tags | % cpu utilization | +0.29 | [-2.24, +2.81] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.27 | [-0.45, +1.00] | 1 | Logs |
| ➖ | idle | memory utilization | +0.20 | [+0.15, +0.24] | 1 | Logs bounds checks dashboard |
| ➖ | tcp_syslog_to_blackhole | ingress throughput | +0.10 | [+0.05, +0.15] | 1 | Logs |
| ➖ | file_to_blackhole_300ms_latency | egress throughput | +0.06 | [-0.12, +0.24] | 1 | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.01, +0.01] | 1 | Logs |
| ➖ | file_to_blackhole_100ms_latency | egress throughput | -0.00 | [-0.23, +0.22] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.10, +0.09] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency | egress throughput | -0.01 | [-0.35, +0.32] | 1 | Logs |
| ➖ | file_to_blackhole_500ms_latency | egress throughput | -0.02 | [-0.26, +0.23] | 1 | Logs |
| ➖ | quality_gate_idle_all_features | memory utilization | -0.31 | [-0.42, -0.20] | 1 | Logs bounds checks dashboard |
| ➖ | file_to_blackhole_1000ms_latency | egress throughput | -0.41 | [-0.89, +0.08] | 1 | Logs |
| ➖ | idle_all_features | memory utilization | -0.68 | [-0.82, -0.54] | 1 | Logs bounds checks dashboard |
| ➖ | file_tree | memory utilization | -1.18 | [-1.31, -1.04] | 1 | Logs |
Bounds Checks
| perf | experiment | bounds_check_name | replicates_passed |
|---|---|---|---|
| ✅ | file_to_blackhole_0ms_latency | memory_usage | 10/10 |
| ✅ | file_to_blackhole_1000ms_latency | memory_usage | 10/10 |
| ✅ | file_to_blackhole_100ms_latency | memory_usage | 10/10 |
| ✅ | file_to_blackhole_300ms_latency | memory_usage | 10/10 |
| ✅ | file_to_blackhole_500ms_latency | memory_usage | 10/10 |
| ✅ | idle | memory_usage | 10/10 |
| ✅ | idle_all_features | memory_usage | 10/10 |
| ✅ | quality_gate_idle | memory_usage | 10/10 |
| ✅ | quality_gate_idle_all_features | memory_usage | 10/10 |
Explanation
A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".
For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:
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Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.
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Its configuration does not mark it "erratic".
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/merge |
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🚂 MergeQueue: pull request added to the queue The median merge time in Use |
What does this PR do?
This PR removes the template injection vulnerable bits from Serverless GHA Workflows.
Motivation
TL; DR: From this remediation security post from Gitlab:
The best practice to avoid code and command injection vulnerabilities in GitHub workflows is to set the untrusted input value of the expression to an intermediate environment variable:
This way, the value of the ${{ github.event.issue.title }} expression is stored in memory and used as variable instead of influencing the generation of script. As a side note, it is a good idea to double quote shell variables to avoid word splitting, but this is one of many general recommendations for writing shell scripts, not specific to GitHub Actions.
Describe how to test/QA your changes
Possible Drawbacks / Trade-offs
Additional Notes