Improve scan engine error classification to reduce false positives and ambiguous results

During recent testing, some scan results were initially flagged as potential vulnerabilities but were later confirmed to be false positives due to environmental factors (intentional Docker behavior, outdated images, or network/SSL edge cases).

While these cases were handled correctly after manual investigation, the current scan engine does not clearly distinguish between:
genuine vulnerabilities
environmental misconfigurations
intentional design behavior
transient network or SSL handshake failures

This can lead to ambiguous results and additional manual verification effort for users.

Proposed improvement:
Improve error classification and reporting in the scan engine
Introduce clearer result states (e.g., confirmed issue, possible misconfiguration, environmental limitation)
Reduce false positives by improving exception handling and context-aware validation

Benefits:
Higher scan accuracy
Reduced false positives
Improved user trust in scan results
Better suitability for automated pipelines

No files, tests, or entry points are identified. Start by locating the scan engine's error handling and result-reporting paths, then review how Docker behavior, outdated images, and network or SSL failures are represented. Done means distinct result states and context-aware handling that reduce false positives without hiding genuine vulnerabilities.