General fix strategy: sanitize or normalize any user-controlled string before including it in log messages, at least by removing or replacing carriage return and newline characters, and ideally by clearly marking or restricting user input. Here, the minimal, non-breaking fix is to sanitize type / event_type prior to logging without changing the parameters passed to the rest of the system.

Best concrete fix in this codebase:

• In CloudEventsPublisher.publish, derive a sanitized version of the type argument (e.g., by replacing \r and \n with empty strings) and use that in the log message instead of the raw type. This keeps the actual CloudEvent unchanged (it still uses the original type argument) and only affects the log output.
• Implement the sanitization inline in the logging call (or just above it), avoiding changes to method signatures or additional imports. The standard str.replace method is sufficient and does not require new dependencies.

Specific change:

• File: src/integration/cloudevents_publisher.py
  • In the publish method of CloudEventsPublisher, change line 171 from:
    • logger.info(f"Publishing CloudEvent: type={type}, id={event.id}")
  • To use a sanitized local variable, for example:
    • safe_type = str(type).replace("\r", "").replace("\n", "")
    • logger.info(f"Publishing CloudEvent: type={safe_type}, id={event.id}")
• No changes are required in advanced_video_routes.py for this particular log injection issue, since the problematic sink is in the publisher.

This keeps functionality intact (the event published is identical), and only the logged representation of type is sanitized to prevent log injection.

In general, to fix log injection when logging user-controlled data, either (a) validate the value and restrict it to a safe, known set of options before logging, or (b) sanitize the value to remove or neutralize characters that can break log structure (such as \r and \n) or other control sequences. It is preferable to validate to a whitelist of allowed values when the domain is small and known—as is the case for backend.

For this specific case, the simplest way to fix the problem without changing existing functionality is to avoid logging the raw self.backend value when it is not one of the known backends, and instead log a sanitized or redacted representation. Since the set of valid backends is already encoded in the Literal type and in the subsequent if/elif chain, we can compute a “safe” version of the backend string that strips newline characters before logging. This keeps the informational content (“what backend was requested”) without allowing injection of extra log lines. Concretely, inside the publish method of CloudEventsPublisher, we will introduce a local variable safe_backend in the unsupported-backend branch, computed from self.backend using .replace('\r', '').replace('\n', ''), and use that in the log message instead of self.backend.

We only need to modify src/integration/cloudevents_publisher.py. No changes are required in advanced_video_routes.py because the fix is at the sink (the logging call). The change is localized to the else branch at lines 182–184 (around the logger.error(f"Unsupported backend: {self.backend}") call). No new imports or helper methods are required.

In general, to fix log injection, any user-provided value written to logs should be normalized so that it cannot inject new log entries or otherwise alter the log format. For plain-text logs, this typically means stripping carriage returns and newlines, and optionally other non-printable control characters, from user input before including it in log messages. The underlying functionality (publishing events) should remain unchanged; only the logging message needs to use a sanitized representation of the user input.

For this specific issue, we should ensure that trigger_name used in the log message in _publish_openwhisk is sanitized. The best minimal-change approach is:

• Keep using the original trigger_name value for constructing the OpenWhisk URL and making the HTTP request (so runtime behavior is unchanged).
• Introduce a sanitized version of the string that removes \r and \n (and optionally other control characters) right before logging.
• Log the sanitized value instead of the raw trigger_name.

Concretely, in src/integration/cloudevents_publisher.py:

• Inside _publish_openwhisk, after trigger_name is computed and the HTTP call succeeds, create a safe_trigger_name variable derived from trigger_name with line breaks removed (e.g., trigger_name.replace("\r", "").replace("\n", "")).
• Change the logger.info call on line 284–286 to interpolate safe_trigger_name instead of trigger_name.

No additional imports or global helpers are strictly necessary for this small scope; we can do the sanitization inline.

To fix an unused import, remove the import statement for the module that is never referenced in the file. This simplifies dependencies, speeds up startup slightly, and removes the static analysis warning.

In this case, the best minimal fix is to delete the line import json from examples/complete_workflow_example.py. No other code changes are needed because nothing in the file references json. Specifically, remove line 15 while keeping the surrounding imports (asyncio, os, sys, Path) unchanged.

In general, unused import issues are fixed by either removing the import or using the imported symbol if it was unintentionally omitted from the code. Here, the best fix that does not change existing behavior is to delete the unused import os line.

Specifically, in examples/complete_workflow_example.py, remove the line import os at line 16 and leave the remaining imports unchanged. No new methods, imports, or definitions are needed; this is a simple cleanup and does not affect runtime behavior of the script.

In general, empty except blocks should either handle the error meaningfully (e.g., logging, metrics, recovery) or narrow their scope. Here, we can keep the existing functional behavior—falling back to a simple dictionary with the raw summary—while adding logging to record that JSON parsing failed.

The best minimal fix is to replace the pass in the except json.JSONDecodeError: block around line 347 with a logging statement, similar in style to the one already used in extract_tutorial_steps. We can log a warning that includes a short message and optionally a truncated version of result.summary for context. No new imports are needed because logger is already defined at the top of the file and json is already imported.

Concretely:

• In src/integration/temporal_video_analysis.py, in the method that returns the comparison analysis (around lines 337–350), update the except json.JSONDecodeError: block so that it logs a warning instead of silently passing.
• Keep the existing fallback return {"comparison": result.summary} unchanged so that behavior seen by callers does not change.

To fix an unused import, remove the unused symbol from the import statement (or the entire import if nothing from it is used). Here, datetime is used and timezone is not, so we should keep importing datetime while dropping timezone.

Concretely, in tests/unit/test_cloudevents_publisher.py, locate the line from datetime import datetime, timezone (line 9) and modify it to import only datetime: from datetime import datetime. No other code changes are required, since there are no references to timezone in the provided code. This removes the unnecessary dependency and resolves the CodeQL warning without affecting existing functionality.