reNgine 2 could produce a PDF. This produces a deliverable, and it is built
the way scan engines are built: sections are plug-and-play directories under
reports/, each with a pydantic config model that IS the source of truth. That
model feeds GET /reports/catalog, the builder renders its own form from it, and
the API validates against it. Adding a section is a directory. Scan Engine is to
stages what Report Template is to sections.
WeasyPrint 69 renders it, chosen over Chromium and Typst after measuring what
each can actually do. Only real CSS Paged Media gives a table of contents with
true page numbers, running headers, a PDF outline and footnotes; SysReptor
reaches the same verdict and pipes Chromium's HTML into WeasyPrint for exactly
that reason. reNgine drops the Chromium hop because it templates in Jinja and
draws its charts as SVG in Python, so a browser buys nothing and costs 400 MB
per report. Typst was rejected because a user-uploadable theme would then have
to be Typst code rather than CSS.
A theme is data. shared/definitions/report_theme.py is the schema, six themes
ship as YAML, and a user uploads their own; there is no markup in a theme, so
there is no injection surface. Fonts are vendored so an offline instance still
renders in Latin, Greek and Cyrillic.
Everything the document claims is computed once, in reports/analysis/engine.py:
a posture score whose every deduction is named, weaknesses ranked by the signals
behind the rank rather than severity alone, attack paths that chain observations
into a route, a remediation plan ordered by risk removed, per-dimension change
against the previous run, and caveats that state what was never assessed. Two
narrators read that one brief. Without AI the report still argues its case; with
AI the prose is better and the model never sees a row, only the brief, capped and
cached per check. An AI failure falls back and the report still completes.
Generation is a job. POST /reports queues, the worker walks progress over SSE,
and four formats come out of one render: PDF, self-contained HTML, Markdown and
JSON. The estimate reads each section's own limits, so the page count it promises
is close to the one it delivers.
AI moves out of Settings into its own top-level page: connection, a model per
tier, what it may be used for, what it has cost, and the narrative cache.
Verified on a 1,379-host gov.cy scan: 124 pages in 19.5s across 19 sections,
all six themes, and a broken key still producing a complete report.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017f2Mf2HGwFaTRnui4UxpRd
A Celery task that probes the unchecked, non-static URLs under one folder of a scan's
endpoints with httpx, applies the results through endpoint_inventory.verify (never
upsert, so no source is invented) and records an EndpointCoverage row whose cap_reason
names the branch. Dispatched from the api through celery_dispatch.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015KEfo6ipoWMYeHxThmTLRy
- Add aggregate functions for scan status and counts in orchestrator.
- Create ScanEventPublisher for emitting scan-related events.
- Introduce ScanActivityService for managing scan activities.
- Implement ScanCommandRecorder for logging command executions.
- Develop canvas building and finalization logic for scan tasks.
- Refactor scan task to utilize orchestrator functions for running stages and finalizing scans.
- Enhance scan notification system to dispatch relevant events.
- Integrate command recording into CLI tool execution for better tracking.
- Updated the default route for automation to redirect to engines instead of scans.
- Removed the scans page and replaced it with a new scans overview page that lists scan history for projects.
- Introduced a detailed scan view page to display individual scan results and subdomain discoveries.
- Added new enums for scan statuses and subdomain sources to improve code clarity and maintainability.
- Implemented a new scan task in Celery to handle scan execution and logging.
- Enhanced the database models for scans and subdomains to support new features and improve data integrity.
- Updated the worker Dockerfile to include necessary scanning tools and dependencies.
- Added utility functions for domain matching and pattern compilation to streamline scan operations.
- Added `clean_name` function to `validation.py` for name validation with max length.
- Simplified docstrings in `dnsx` client, parser, and service files for clarity.
- Enhanced error handling in `ViewDNSService` to prevent disabling keys on rate limits.
- Improved WHOIS service to handle concurrent inserts gracefully with integrity checks.
- Updated `Settings` class in worker config to inherit from `BaseAppSettings`.
- Added new dependencies in `pyproject.toml` for `apprise` and `cryptography`.
