The Google Fonts css2 API answers a modern user agent with one @font-face per
script, all sharing a family and weight and separated only by unicode-range.
WeasyPrint then shapes a run against one subset file and rasterises it from
another: the PDF's text layer read back correctly while the page showed
"ΨΗΦΙΑΚΗ ΥΠΗΡΕΣΙΑ" as "N ĂN ĐỀ ẤU NẤGĂNG". Every non-Latin page title in a
report was gibberish.
Ask the API with a user agent that predates unicode-range and it answers with
one whole file per family, weight and style; subset that here with fontTools
and save it as woff2. The vendoring script now refuses to write a face whose
block still carries a unicode-range, so the split cannot come back unnoticed.
131 files and 3.3 MB become 23 files and 1.2 MB — smaller as well as correct.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AW5pitnosX8LEFEYxH8gFS
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
Discovery was passive-only — subfinder, ctfr, crtname, assetfinder and certificate
SANs — so anything absent from a public source was absent from the scan, and every
later stage inherited the miss. It now runs two more sources after the passive merge,
carrying SubdomainSource.BRUTEFORCE and PERMUTATION through the same scoping and
merge every other provider uses.
Bruteforce is `dnsx -d <apex> -w <list>` over a wordlist shipped in the repo:
4,860 labels ranked by real-world frequency, regenerated by
scripts/fetch_subdomain_wordlist.py and committed like the port registry, so an
install with no egress still has one. The word budget reads from the top of that
ranking, which is why a smaller budget is simply the first N lines. Permutation is
alterx over the names already found, seeded apex-first because a name one label from
the apex has more siblings worth guessing than a five-label one.
Two things this needed to be worth running at all:
- **A budget, not an idle watchdog.** A guessed name that does not exist produces
no output, so the stall detector that protects the resolver killed both runs at
exactly 90s the first time. Guessing is bounded by total time instead, floored at
5 names/s against a measured 9, and says so when it stops early.
- **Wildcard rejection.** The wildcard probe moved ahead of expansion: on a wildcard
zone every guess answers, and storing 5,000 fake names would be worse than not
guessing. Answers that resolve only to the wildcard address are dropped and
counted in the source's note.
Both are skipped at passive intensity and say why — the stage still touches no
target, but a wordlist run does query the target's own nameservers. Bruteforce
defaults on at 1,000 words (110s measured, 2 hits on vulnweb.com, both attributed
correctly alongside their passive sources); permutations default off, because every
variant is another DNS query.
ProviderResult gained `note` so a source can come up short without being reported as
failed. alterx reads seeds from stdin — `-l <file>` answers "no input found".
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01DPueZXQMbjLXMokXeA8LFP
An open port with no name is a dead end. The IANA Service Name and Transport
Protocol Port Number Registry (public domain, no key) fills the ~5,800 TCP
ports our curated map does not cover, committed as a generated data file
because it is small and slow-moving; scripts/fetch_port_registry.py rebuilds it.
IANA is the fallback vocabulary, never the authority. It still calls 9200
wap-wsp and 5601 esmagent while the internet runs Elasticsearch and Kibana
there, so WELL_KNOWN wins and gained ASM-framed descriptions of its own —
"Docker API without TLS, equivalent to root on the host" beats "Docker REST
API (plain text)". 80, 443, kibana, nessus, consul, kubelet and rabbitmq are
now named rather than falling through to a generic "http".
Provenance is carried, not hidden: describe() returns (text, from_registry)
and a registered name is presented as "IANA registration for port N. This scan
did not confirm it." — because IANA says port 12345 is a chat system. A name
the UI shows is always a name `service:<name>` can find, so the registry
writes through service_for_port into the stored column and a migration
backfills rows written before it existed.
Services also get an identity mark: the vendored Wappalyzer logo when the
service is a product, the class glyph when it is a protocol.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BnC2xMMzV6WQnpDp5zcXoU
TLDR; Script updated to abort uninstall process if anything other than the specified "y/Y/Yes/yes/YES" is entered. Fixes uninstall on user inputs like "no" or blank input.
---
The uninstall.sh script does not abort if any user input is provided.
The following user-input scenarios were tested, confirmed and fixed:
- no input
- "n"
- "N"
- "No"
- "1"
- "q"
- "
- (
- '
- <
- [
- {
- /
Because the `make` commands need to be executed in the same directory as where the Makefile is located, putting `update.sh` is not going to work. This commit moves the `update.sh` file back to the root directory.
