Ground-truth pass over the named flagship consumer (portfolio-optimiser's
'OKF-upload-inbox') found it does not exist as a seam: both optimiser siblings
are frozen at release, carry their own OKF layer, receive no external bundles,
and take no dependency on this guard. Defer consumer integration (stream 2)
until the guard is mature; mature it here first, Node-port-friendly. Next
concrete build is the in-repo OKF inbox showcase (mode-b import_bundle as a
receive/quarantine gate), spec'd under 'The OKF inbox showcase'.
Google's Open Knowledge Format (OKF v0.1) is the LLM-wiki pattern this
library guards. Capture a security brief for OKF ingestion as tracked
future work -- NOT shipped scope. The v0.1 core is format-agnostic text;
OKF surfaces beyond the body (YAML frontmatter, resource URLs, cross-link
graph, file path / reserved names, format-level authenticity) are uncovered.
- Coverage claims verified against the code at 5397ba1 (brief section 9): no
YAML parse in src, neutralize defangs but has no reject-gate, no path/graph
logic, disposition machinery exists.
- OKF confirmed real at the format level (Google Cloud, 2026-06-12; spec
GoogleCloudPlatform/knowledge-catalog).
- Design move: an OKF adapter ON TOP of the format-agnostic core; the core
stays text->findings.
- Section 8 lists 8 v0.2 tasks (T1-T8); T8 (surface two OKF residual risks in
README honest-limits) can land independently.
One realistic content sample carrying many vulnerabilities at once, run through
a mock ingestion pipeline (sanitize -> lexicon+entropy+decode-rescan -> output
gate -> disposition); assert every planted vuln is caught and disposition fails
secure. Doubles as the README worked example. Added to build-order step 11 and
the test strategy; tracked as task #11. Inspiration: llm-security/examples/*.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01K8GmKRCdsPjWYAKWsNgeQS
Based on the working, tested Layer B ingestion-gate in ms-ai-architect (2026-07-04):
- A: correct consumer 2 — it fetches authored Learn docs + code samples via the
microsoft-learn MCP, NOT the open Q&A forum / MSDN / Stack Overflow (that claim
was unverified/overstated); low-trust surface is intra-document. Repo/name
reconciled: the consumer is ms-ai-architect, no separate "MS AI Security plugin".
- B: §4.7 — trust tiers WITHIN a document (code sample / localized string vs
authored prose), not only across sources; carriers + critical block in any tier.
- C: §12 — consume the lexicon as pure imported functions, not the llm-security
scan CLI (which under-covers Markdown prose + base64-in-code-block, verified).
- D: §4.6 — fail-secure extends to scanner-unavailable: un-scannable ⇒ BLOCK.
Adds ms-ai-architect as the second reference implementation (output side).
Second target consumer named: MS AI Security plugin, an ingestion
pipeline over Microsoft Learn content that includes user-generated Q&A
(learn.microsoft.com/answers, plus ingested MSDN/Stack Overflow). This
is the high-untrust case where the contract is load-bearing, not
hygiene. New design principle 4.7: disposition scales with source trust
(pinned changelog = WARN; open UGC = quarantine/hard-fail on high
severity). Day-1 rationale: architectural controls are cheap to design
in, expensive to retrofit (consumer 1 is proving that at its A13).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HPAmFyEVWbwvmSNVdXTu4d
