[CI] Escape the backport-pr GHA body template to avoid template injection

[amenasria]

What does this PR do?

This PR fixes a security vulnerability in the "Backport PR" GitHub Actions workflow by properly escaping the pull request body in the EJS template used by the tibdex/backport action (using <%- instead of <%=). This prevents the execution of malicious code that could be injected through untrusted PR bodies.

Motivation

The use of unsanitized, user-controlled inputs in EJS templates within the tibdex/backport action allows for arbitrary code execution, leading to potential leakage of secrets and other malicious activities.

Describe how to test/QA your changes

Possible Drawbacks / Trade-offs

Additional Notes

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 48476662 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

• tests_deb-arm64-py3
• tests_deb-x64-py3
• tests_flavor_dogstatsd_deb-x64
• tests_flavor_heroku_deb-x64
• tests_flavor_iot_deb-x64
• tests_rpm-arm64-py3
• tests_rpm-x64-py3
• tests_windows-x64

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

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 00ec4c7f-5aaf-4c8a-a55f-40172361047f

Baseline: 80ece75
Comparison: fce9df7
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.81	[+0.10, +1.53]	1	Logs
➖	basic_py_check	% cpu utilization	+0.42	[-3.32, +4.16]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.02	[-0.23, +0.27]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.02	[-0.37, +0.41]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.09, +0.08]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.07	[-0.26, +0.13]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.08	[-0.33, +0.16]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.13	[-0.61, +0.36]	1	Logs
➖	quality_gate_idle	memory utilization	-0.48	[-0.54, -0.42]	1	Logs bounds checks dashboard
➖	idle	memory utilization	-0.76	[-0.82, -0.70]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	-0.93	[-0.99, -0.88]	1	Logs
➖	file_tree	memory utilization	-1.11	[-1.27, -0.96]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-1.69	[-5.02, +1.64]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-2.38	[-2.53, -2.24]	1	Logs bounds checks dashboard
➖	idle_all_features	memory utilization	-3.44	[-3.56, -3.32]	1	Logs bounds checks dashboard

Bounds Checks: ❌ Failed

perf	experiment	bounds_check_name	replicates_passed	links
❌	quality_gate_idle	memory_usage	6/10	bounds checks dashboard
❌	idle	memory_usage	7/10	bounds checks dashboard
❌	quality_gate_idle_all_features	memory_usage	7/10	bounds checks dashboard
❌	idle_all_features	memory_usage	8/10	bounds checks dashboard
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	lost_bytes	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

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

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:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. 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.

3. Its configuration does not mark it "erratic".

[amenasria]

/merge

[dd-devflow]

Devflow running: /merge

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2024-11-08 09:37:56 UTC ℹ️ MergeQueue: pull request added to the queue

The median merge time in main is 24m.