Transilience Report Style

Turns a structured findings JSON into a branded A4 PDF (light theme by default — modern/minimal; --theme dark fallback; brand gradient rule, boxed code/samples, framed screenshots, colour-coded severity + CVSS-score, findings cards, severity metrics) — the standard Transilience deliverable. The generator is data-driven: you assemble one JSON, run one command. Design system: formats/transilience-report-style/SKILL.md §0 (theme/palette).

When to use

• A pentest / vuln-assessment / network-scan / compliance engagement is finished and validated and needs its PDF deliverable.
• You already have findings (ideally validated) with severity + CVSS + evidence + remediation.

How to use (3 steps)

1. Write report_data.json matching reference/report-data-schema.json. Only engagement and findings are required; every other key (executive_summary, metrics, sections, cve_register, coverage_table, attack_pattern_coverage, ruled_out, tools_used, roadmap, disclaimer) is an optional section that is skipped if absent. See reference/example-report-data.json for a minimal working file.

   • Internal-only sections: a sections[] entry with "internal": true is retained in report_data.json (kept as the internal record) but is never rendered into the client PDF. Use it for internal QA logs such as blind-validation / independent-reproduction verdict tables — these must not appear in any client-facing PDF.

2. Run the generator:

   python3 reference/generate_report.py <report_data.json> -o reports/My-Report.pdf
   

   Copy

   Fonts + logo are auto-discovered from formats/transilience-report-style/. Override with --assets <dir> if running outside the repo. Requires reportlab (pip install reportlab).

3. Verify by rendering a page (pdftoppm -png -r 100 -f 1 -l 1 out.pdf /tmp/p) and reading it before delivery.

Other editions of the same data

• Executive edition — generate_report.py <data.json> --exec-only renders cover + KPI boxes + narrative + roadmap and no finding detail or technical registers, for circulation beyond the security team. The KPI counts still describe the whole engagement.
• Machine-readable exports — python3 ../../tools/report_export.py <data.json> --format csv|xml -o findings.csv emits the findings register for a vulnerability-management import. Same rows as the PDF register and the xlsx: one projection, three renderings.

What it renders

Cover (logo, title lines, subtitle, metadata) → Executive Summary (auto KPI metric boxes from severity counts + narrative + key risks + positives) → free-form sections (Scope/Methodology) → finding cards grouped Critical→Info (severity bar, CVSS+vector, CWE/OWASP, status, affected, description, impact, optional PoC block — ordered steps each with prose + code-styled command + embedded screenshot, optional severity-calibration, optional per-finding CVE table, remediation) → optional CVE register, coverage table, Attack Pattern Coverage (deterministic surface-unit × attack-class matrix with colour-coded status), ruled-out appendix, Tools & Techniques Used, remediation roadmap, disclaimer. Section numbers are assigned automatically.

Finding object (the important fields)

id, title, severity (Critical|High|Medium|Low|Info), cvss_score, cvss_vector, cwe, owasp, affected[], description, impact, recommendation + optional poc[], calibration, needs_live_confirmation, cves[], attack[]. attack[] carries MITRE technique ids — ATT&CK (T1190, T1059.001) or, for AI/LLM findings that have no ATT&CK technique, ATLAS (AML.T0051); it renders beside CWE/OWASP and appears in every export. poc is an ordered list of steps {description, command, image_url} (it merges the former evidence / poc_request / screenshot fields): each renders as a numbered prose description, an optional code-styled command, and an optional embedded image. PAN/Aadhaar/card-like values are defensively masked at render time — but redact real secrets/PII in your source text anyway.

Conventions

• Severity is set by the CVSS band (≥9 Critical, ≥7 High, ≥4 Medium, >0 Low, 0 Info) unless deliberately env-adjusted — state the calibration in the calibration field (see formats/transilience-report-style/pentest-report.md §7).
• No emoji; text severity labels only. Finding metadata as fields, not prose.
• One finding = one validated issue. Group systemic instances rather than repeating near-duplicates.

References

• Full visual design system (palette, typography, components): formats/transilience-report-style/SKILL.md
• Pentest report structure + finding-quality standard + severity calibration + compliance mapping: formats/transilience-report-style/pentest-report.md
• Compliance variant: formats/transilience-report-style/compliance-report.md
• Generator: reference/generate_report.py · Schema: reference/report-data-schema.json · Example: reference/example-report-data.json
• Preflight lint (hard-fail schema + RAW-field escaping/tag-allowlist gate before rendering): ../../tools/report_data_lint.py — run before generate_report.py; it (and the generator's own report_data_shape guard) block a string-narrative, an invalid severity, or an unsafe <img>/<a href> from reaching a client PDF.
• In-place revision (append / supersede / --rescore <disposition.csv> / --scope <allowlist> restrict+renumber / cross-feed; always re-derives KPIs + scrubs narrative finding-counts): ../../tools/report_data_revise.py — the incremental-revision entry point, distinct from merge-reports' N→1 consolidation.
• Ingest a finished/foreign report (PDF/xlsx/DOCX/markdown/native JSON) into the canonical finding schema: ../../tools/report_ingest.py — bootstraps merge / retest / CERT-In from an already-produced deliverable instead of hand re-keying.