docs(readme): lead with write-time trust-boundary; feature shipped OKF adapter
Rewrite the README value proposition to make the necessity land on mechanism, not adjectives: write-time ingestion is the trust boundary query-time guardrails structurally cannot see; an OKF/LLM-wiki has no format-level authenticity, so the ingestion pipeline IS the trust boundary. Add a first-class 'OKF / LLM-wiki support (shipped)' section for import_bundle mode-b (per-concept gates). Fix stale test badge (357 -> 522) and drop the brittle module count. Tighten prose; retain all honest-limitations items (a shipped control). Every symbol/preset/command verified against v0.2 code.
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12
CHANGELOG.md
12
CHANGELOG.md
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@ -26,6 +26,18 @@ not. Two consumers of the same source of truth:
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This is the real-case validation gate ahead of a v1.0 freeze.
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### Documentation — consumer adoption + README value proposition
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- `docs/ADOPTION-BRIEF.md` — a self-contained brief a consumer repo (OKF
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second-brain / LLM wiki) can plan an inclusion from: the write-time trust-boundary
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argument, the two bookends + 8-step contract, the shipped OKF adapter
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(`import_bundle` mode-b), how to verify (coverage matrix), how to depend
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(stdlib-only core), and a checklist for *when/where* to wire it.
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- README rewritten to lead with the write-time trust-boundary framing, add a
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first-class **OKF / LLM-wiki support (shipped)** section for `import_bundle`, and
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correct the test badge (357 → 522). Every claim verified against the code; all
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honest-limitations items retained.
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## [0.2.0] — 2026-07-06
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### Added — OKF adapter (stream 1)
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259
README.md
259
README.md
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@ -3,34 +3,39 @@
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A reusable, minimal, dependency-light defensive layer for **LLM ingestion
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pipelines** — the write-time siblings of query-time chatbot guardrails.
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**Write-time ingestion is the trust boundary that query-time guardrails
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structurally cannot see.** When untrusted content passes through an LLM
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enrichment/summarization/extraction step into a *persisted* artifact — a RAG
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corpus, a knowledge base, an LLM wiki — the poisoned result is read *later* by a
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downstream agent as **trusted context**. That agent's guardrail never sees where
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the content came from. The only place the provenance still exists is the write.
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Where mature guardrails (LLM Guard, NeMo Guardrails, Rebuff, Vigil, …) sit
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between a user and a model at query time, this library hardens the other shape:
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untrusted content flowing through an LLM enrichment/summarization/extraction step
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into a **persisted, downstream-consumed artifact** (RAG corpus, knowledge base,
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wiki). It packages the architectural contract — sanitize → fence → tool-less
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quarantined transform → per-stage capability isolation → scan output before
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commit → fail-secure — as composable, stdlib-first, framework-agnostic code.
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This library packages that write-time contract — sanitize → fence → tool-less
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quarantined transform → per-stage capability isolation → scan-before-commit →
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fail-secure — as composable, stdlib-first, framework-agnostic code. It is the
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write-time **sibling** of query-time tools (LLM Guard, NeMo Guardrails, Rebuff,
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Vigil), not a competitor: those harden material as it enters the model; this
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hardens it as it is committed for a *later* reader. Where existing OSS tooling is
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mostly single-stage *detectors* — a risk verdict, with quarantine, capability
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isolation, scan-before-persist, and fail-secure left to the integrator — this
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packages the full contract as code. (Neighbours surveyed in
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[`docs/BRIEF.md`](docs/BRIEF.md) §11.)
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The gap it fills is **not** "no one detects injection." Existing OSS tools are
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either single-stage *detectors* — they emit a risk verdict and leave quarantine,
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capability isolation, scan-before-persist, and fail-secure disposition to the
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integrator — or runtime content-hardening that guards material as it *enters* the
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model. This library packages the full **write-time ingestion contract** as
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composable code: the part query-time tooling structurally cannot see, because a
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poisoned artifact committed at write time is read by a *downstream* agent whose
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guardrail never sees where it came from. (Nearest neighbours surveyed in
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`docs/BRIEF.md` §11.)
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**Why an LLM wiki (e.g. Google OKF) needs this specifically.** OKF and
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second-brain formats have no schema registry, no central authority, and no
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signing — a bundle's claimed origin is not verifiable at the format level. So
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*your ingestion pipeline is the trust boundary*: provenance must be stamped by you
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at write time, never assumed from the format. Any pipeline ingesting external data
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into an agent-read store has this shape; an OKF wiki is its canonical form — which
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is why the guard ships a first-class OKF adapter (below).
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**Status:** `v0.2`, alpha. The stdlib-only core is built and tested — ten
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detector/contract modules and the top-level wiring, exercised by an end-to-end
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showcase and adversarial + false-positive corpora. The public API may still
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change. There are real limitations, stated plainly below; read them.
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**Status:** `v0.2`, alpha. The stdlib-only core — its detector, contract, and
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OKF-adapter modules plus the top-level wiring — is built and tested, exercised by
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an end-to-end showcase and adversarial + false-positive corpora. The public API
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may still change. There are real limitations, stated plainly below; read them.
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## Install
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@ -51,13 +56,13 @@ from llm_ingestion_guard import (
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prepare_input, screen_output, Disposition, PRESET_USER_UPLOAD,
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)
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prepared = prepare_input(untrusted_content) # §6 1-2: sanitize + fence
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enriched = your_model(prepared.fenced) # §6 3: tool-less — YOUR call
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decision = screen_output(enriched, PRESET_USER_UPLOAD) # §6 6-7: scan + dispose
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prepared = prepare_input(untrusted_content) # sanitize + fence
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enriched = your_model(prepared.fenced) # tool-less — YOUR call
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decision = screen_output(enriched, PRESET_USER_UPLOAD) # scan + dispose
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if decision.disposition is Disposition.FAIL_SECURE:
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alert(gate_code=decision.reasons) # §6 8: minimal payload, no content
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raise SystemExit # §6 7: halt — never persist
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alert(gate_code=decision.reasons) # minimal payload, no content
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raise SystemExit # halt — never persist
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```
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`screen_output` fails **closed**: if the scanner itself errors on crafted input,
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@ -73,24 +78,44 @@ machinery, and the contract asserters `assert_tool_less` /
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`assert_credential_allowlist` / `scoped_env`. See
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[the end-to-end showcase](tests/test_showcase.py) for a full worked pipeline.
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## Verify what it stops (coverage matrix)
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## OKF / LLM-wiki support (shipped)
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Before wiring the guard into your pipeline, see — in one place — every
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vulnerability class it stops, and the ones it deliberately does not:
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For a bundle-shaped store, the `okf` submodule sits **on top of** the
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format-agnostic core: it knows OKF structure (frontmatter, paths, links,
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`resource`, bundles) and feeds scannable regions into the same `sanitize` /
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`scan_output` / disposition machinery — no YAML/format awareness leaks into the
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core. Two ingestion modes:
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```bash
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python -m llm_ingestion_guard.coverage # prints the matrix; exit 0 = all as documented
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- **(a) Own enrichment output** — your agent writes concepts. Run the two bookends
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above per concept before commit.
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- **(b) Received external bundle** — you merge a whole third-party OKF bundle.
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`import_bundle` iterates concept-by-concept and runs the full per-concept gate:
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```python
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from llm_ingestion_guard.okf import import_bundle, Origin, Channel
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# bundle: {concept_path -> raw document text}, e.g. {"tables/users.md": "---\n..."}
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result = import_bundle(bundle, origin=Origin.EXTERNAL, channel=Channel.AUTOMATIC)
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for c in result.concepts:
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if c.error: # hard reject: bad path, unsafe frontmatter, non-https resource
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skip(c.path) # disposition is FAIL_SECURE; do not merge this concept
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# result.disposition = most-severe across concepts
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# result.links = the cross-link graph (dangling/rejected/resolved edges)
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# result.log() = the log.md body (one provenance-stamped line per concept)
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```
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Each row drives the real guard with a live payload and prints
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`class -> OWASP anchor -> expected -> observed -> verdict`. The manifest
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([`coverage.py`](src/llm_ingestion_guard/coverage.py)) is the single source of
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truth for [`tests/test_coverage_matrix.py`](tests/test_coverage_matrix.py), which
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asserts **total recall** across every defended class, that **every documented gap
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still holds** (a closed gap fails the test, forcing a doc update), and — the
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honesty guard — that **every lexicon pattern has a case** so the matrix cannot
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fall behind the lexicon. The full LLM02 secret-egress set and the container-layer
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front-end (CSV formula-injection, zip-slip/bomb, symlink) are asserted there too.
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Per-concept gates: **path / reserved-name** (rejects `..` traversal and reserved
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`index.md`/`log.md` shadowing); **frontmatter parse-safety** — a strict,
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reject-by-default loader that refuses anchors, aliases, and explicit tags *by
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construction*, so a billion-laughs alias expansion or a `!!python/object` coercion
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cannot occur (it is deliberately **not** a general YAML engine, whose own features
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are the attack surface); **`resource` https-allowlist** (hard-rejects
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`data:`/`javascript:`/`file:` before commit — a reject-gate, not defang);
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**whole-concept scan** (frontmatter *values* + body through `scan_output`);
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**cross-link graph** (surfaces dangling targets, the dormant-injection signal, and
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rejects bundle-escaping links); **provenance stamping** (`Origin` × `Channel` →
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tier + disposition, emitted to `log.md`).
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## The reusable contract (adopt-this checklist)
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@ -118,97 +143,101 @@ The actual product is this checklist, encoded as code you wire in order:
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Steps 1-2 are `prepare_input`; steps 6-7 are `screen_output`; steps 3-5 are
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yours; the contract asserters harden step 3-4.
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## Verify what it stops (coverage matrix)
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Before wiring the guard into your pipeline, see — in one place — every
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vulnerability class it stops, and the ones it deliberately does not:
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```bash
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python -m llm_ingestion_guard.coverage # prints the matrix; exit 0 = all as documented
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```
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Each row drives the real guard with a live payload and prints
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`class -> OWASP anchor -> expected -> observed -> verdict`. As of `v0.2`: **126 of
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126 defended classes demonstrated (recall 100%)** and **4 of 4 documented gaps
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still hold**. The manifest ([`coverage.py`](src/llm_ingestion_guard/coverage.py))
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is the single source of truth for
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[`tests/test_coverage_matrix.py`](tests/test_coverage_matrix.py), which asserts
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**total recall** across every defended class, that **every documented gap still
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holds** (a closed gap fails the test, forcing a doc update), and — the honesty
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guard — that **every lexicon pattern has a case** so the matrix cannot fall behind
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the lexicon. The full LLM02 secret-egress set and the container-layer front-end
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(CSV formula-injection, zip-slip/bomb, symlink) are asserted there too.
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## Honest limitations (shipped as a control)
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Conceding these plainly is itself a control — it prevents the false assurance
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that a green scan means safe content:
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Conceding these plainly is itself a control — it prevents the false assurance that
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a green scan means safe content:
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- **Structural unsolvability at the text layer.** Pattern/lexicon detection is
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bypassable in isolation; character-injection and novel phrasings evade it. The
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*contract* (tool-less transform, capability isolation, fail-secure) is what
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carries the security — the lexicon is defense-in-depth, not a wall.
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*contract* (tool-less transform, capability isolation, fail-secure) carries the
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security — the lexicon is defense-in-depth, not a wall.
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- **A lone HIGH finding in trusted prose disposes to WARN, not quarantine.**
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Under `PRESET_TRUSTED_SOURCE`, §4.7 trust-scaling downgrades a single HIGH to
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WARN (a reputable single-author source is expected to carry security
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vocabulary), and one HIGH is not "compound" — compound escalation needs ≥2
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findings at MEDIUM+. So a HIGH-severity injection reproduced verbatim in output
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under a *trusted* policy persists with only a WARN. This is the §4.7 design, not
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a miss: if your "trusted" sources can carry attacker-influenced text, run them as
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untrusted (or add a quarantine floor) instead.
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Under `PRESET_TRUSTED_SOURCE`, trust-scaling downgrades a single HIGH to WARN,
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and one HIGH is not "compound" (escalation needs ≥2 findings at MEDIUM+). So a
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HIGH injection reproduced verbatim under a *trusted* policy persists with a WARN.
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This is by design: if your "trusted" sources can carry attacker-influenced text,
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run them as untrusted (or add a quarantine floor).
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- **The upload preset's quarantine floor is currently vacuous.**
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`PRESET_USER_UPLOAD` sets `quarantine_default` ("any finding → at least
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QUARANTINE_REVIEW"), but every detector emits CRITICAL/HIGH/MEDIUM only (no
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LOW/INFO), and under untrusted trust a MEDIUM already escalates to
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QUARANTINE_REVIEW. The floor therefore never changes an outcome in today's
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severity set — it is defensive headroom for a future LOW/INFO finding (e.g. a
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grounding "unchecked" marker), not an active control. Documented so the preset's
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guarantee is not over-read.
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- **Semantic / factual poisoning is invisible** to lexicon + entropy: a
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factually false claim in clean prose carries no suspicious token. The
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`PRESET_USER_UPLOAD` sets `quarantine_default`, but detectors emit
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CRITICAL/HIGH/MEDIUM only, and under untrusted trust a MEDIUM already escalates to
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QUARANTINE_REVIEW — so the floor changes no outcome today. It is headroom for a
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future LOW/INFO finding, documented so the preset is not over-read.
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- **Semantic / factual poisoning is invisible** to lexicon + entropy: a false claim
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in clean prose carries no suspicious token. **Highest impact for a wiki.** The
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`grounding` module ships only a `SourceGroundingCheck` *seam* — the deterministic
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core does not judge semantics; a `[judge]` implementation must be plugged in.
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- **Adversarial-ML evasion** can survive normalization; **tokenizer mismatch**
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between scanner and model leaves gaps.
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- **Latent / dormant memory poisoning** is not judgeable at write time.
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between scanner and model leaves gaps. **Latent / dormant memory poisoning** is
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not judgeable at write time.
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- **Dormant / broken-link injection** in a linked corpus (e.g. an OKF bundle): a
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link to a not-yet-existing target passes a per-concept write-time scan clean —
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the payload is planted later, when that target is written. A scanner that sees
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one document at a time cannot catch it; it needs cross-write graph re-scan
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(tracked for the OKF adapter).
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link to a not-yet-existing target passes a per-concept write-time scan clean — the
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payload is planted later, when that target is written. `link_graph` surfaces the
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*dangling* edge as the signal, but catching the payload needs cross-write re-scan
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over time (the caller's disposition call).
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- **OKF reserved files (`index.md` / `log.md`).** In a *received* bundle these are
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legitimate structure (directory listing, update log), so mode-b `import_bundle`
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scans their body and frontmatter — an injection planted in a directory listing
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is caught — rather than path-rejecting the whole conformant bundle. The upload
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front-end keeps the opposite rule: an individual upload landing on a reserved
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basename is a shadow of the listing and is refused (`allow_reserved=False`).
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- **A document that *describes* attacks is a false positive.** Content whose
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legitimate purpose is to document prompt-injection payloads (security notes,
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this project's own corpus) trips carrier-strip / fail-secure. At the text layer
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there is no way to distinguish "*about* an attack" from "*carrying* an attack";
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such content needs a deliberate, explicitly-marked escaped path, never a silent
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allow.
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legitimate structure, so mode-b `import_bundle` scans their body and frontmatter
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(an injection in a directory listing is caught) rather than path-rejecting the
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conformant bundle. A front-end materialising individual uploads keeps the opposite
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rule (`allow_reserved=False`): a reserved basename is a listing-shadow and refused.
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- **A document that *describes* attacks is a false positive.** Content documenting
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prompt-injection payloads (security notes, this project's own corpus) trips
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carrier-strip / fail-secure. At the text layer "*about* an attack" and "*carrying*
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an attack" are indistinguishable; such content needs a deliberate, explicitly
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escaped path, never a silent allow.
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- **Bilingual text trips the Cyrillic/Latin homoglyph rule.**
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`homoglyph:cyrillic-latin-mix` (MEDIUM) flags any Latin letter adjacent to a
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Cyrillic look-alike, so genuine bilingual prose (e.g. Russian, or mixed
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Norwegian/Cyrillic) trips MEDIUM → under untrusted → QUARANTINE_REVIEW — a real
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false positive for an inbox that *expects* multilingual content. A calibration
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fix is pending (require ≥N mixed pairs, or only flag when a folded variant also
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hits another pattern); until then, multilingual corpora over-quarantine on this
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rule.
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`homoglyph:cyrillic-latin-mix` (MEDIUM) flags a Latin letter adjacent to a
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Cyrillic look-alike, so genuine bilingual prose → MEDIUM → under untrusted →
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QUARANTINE_REVIEW — a real false positive for an inbox that expects multilingual
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content. A calibration fix is pending.
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- **Insider in-place edits** by a trusted author are out of the untrusted-content
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threat model.
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- **Text-only.** The core is `text -> findings`: it parses no files (no
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`pypdf`/`python-docx`/archive deps). Extract text first, then scan it with the
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high-untrust upload provenance. OCR-embedded instructions and multimodal stego
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in images/PDFs are out of scope beyond the sanitizer's character-layer stripping.
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- **Uploaded files: only the *extracted text* is scanned.** The two-stage OKF
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inbox showcase (`tests/test_okf_inbox_uploads.py` + `tests/inbox_frontend.py`, a
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dev-scoped demonstration whose `python-docx`/`python-pptx` parsers live in the
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`[dev]` extra, never core `dependencies`) reads `.txt`/`.md`/`.csv`/`.docx`/
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`.pptx`/`.xlsx`, folders and `.zip`, materializes them into an OKF bundle, then
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guards it. What survives text extraction is **out of scope**: VBA/macros
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(`.docm`/`.xlsm`/`.pptm`), OLE / embedded objects, image-embedded instructions
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needing OCR, font/render steganography, and encrypted / password-protected files
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— the binary layer needs a separate scanner. The front-end owns the container
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threats it *can* see (zip-slip → path gate, zip-bomb → size cap, symlink
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refusal, CSV/XLSX formula-lead cells). **`.pdf` is a deliberate concession, not a
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TODO:** a top-level `.pdf` drop is *refused as an unsupported format* rather than
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half-scanned, because a PDF parser (plus `reportlab` solely to author white-on-white
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test fixtures) is disproportionate for a dev-scoped showcase, and the OCR /
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font-render stego carriers a PDF would smuggle are already out of scope above. The
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one remaining known gap is the numeric `-`/`+` CSV false positive (an XLSX numeric
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cell is typed, so it does not trip the gate).
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high-untrust upload provenance. OCR-embedded instructions and multimodal stego are
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out of scope beyond the sanitizer's character-layer stripping.
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- **Uploaded files: only the *extracted text* is scanned.** The dev-scoped OKF
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inbox showcase (`tests/test_okf_inbox_uploads.py` + `tests/inbox_frontend.py`;
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parsers in the `[dev]` extra, never core `dependencies`) reads
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`.txt`/`.md`/`.csv`/`.docx`/`.pptx`/`.xlsx`, folders and `.zip`, materializes an
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OKF bundle, then guards it. What survives extraction is **out of scope**:
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macros, OLE/embedded objects, OCR-needing images, font/render stego, encrypted
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files — the binary layer needs a separate scanner. The front-end owns the
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container threats it *can* see (zip-slip → path gate, zip-bomb → size cap, symlink
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refusal, CSV/XLSX formula-lead cells). **`.pdf` is a deliberate concession:** a
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top-level `.pdf` is *refused as unsupported* rather than half-scanned (a PDF parser
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is disproportionate for a dev showcase, and the OCR/stego it would smuggle is
|
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already out of scope). One known gap: the numeric `-`/`+` CSV false positive (a
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typed XLSX numeric cell does not trip it).
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- **Lexicon findings are deduplicated by pattern id** — `count=1` and the first
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offset are reported, so the same class matched across several channels/variants
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collapses to one finding at its first location. This keeps reports readable, but
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a caller that counts occurrences or needs every offset of a repeated pattern sees
|
||||
only the first: a deliberate readability tradeoff, not full positional coverage.
|
||||
offset are reported, so a class matched across several channels collapses to one
|
||||
finding at its first location: a deliberate readability tradeoff.
|
||||
- **Secret egress: base64-wrapped is caught, hex-wrapped is not.** The output gate
|
||||
decodes base64 blobs and re-scans the plaintext through the credential/egress set,
|
||||
so a base64-*wrapped* secret surfaces as `decoded:egress:*` rather than vanishing.
|
||||
A *hex*-wrapped secret does not: `entropy` exposes decoded plaintext for base64
|
||||
only, so hex (and other encodings, or nested wraps) is a deliberate boundary, not
|
||||
a silent miss — decode the transport layer first if you need it scanned.
|
||||
decodes base64 blobs and re-scans the plaintext, so a base64-*wrapped* secret
|
||||
surfaces as `decoded:egress:*`. `entropy` exposes decoded plaintext for base64
|
||||
only, so hex (and other encodings, or nested wraps) is a deliberate boundary —
|
||||
decode the transport layer first if you need it scanned.
|
||||
|
||||
## Out-of-scope (documented boundary)
|
||||
|
||||
|
|
@ -219,6 +248,8 @@ steganography; query-time / runtime guardrails; semantic factuality verification
|
|||
|
||||
- [Design brief](docs/BRIEF.md) — what this repo contains and why.
|
||||
- [Build plan](docs/PLAN.md) — module build order and the reuse map.
|
||||
- [Adoption brief](docs/ADOPTION-BRIEF.md) — wiring the guard into an OKF
|
||||
second-brain / LLM wiki, and a checklist for *when* to include it.
|
||||
|
||||
The contract is extracted from a working reference implementation (the
|
||||
`claude-code-llm-wiki` Stage B enrichment pipeline). Threat-model anchors: OWASP
|
||||
|
|
|
|||
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