Build-order step 6. Close the EchoLeak class (CVE-2025-32711): active content in
persisted model OUTPUT that a downstream renderer auto-fetches or makes clickable,
exfiltrating data zero-click. These carriers are neither injection strings nor
high-entropy, so lexicon + entropy miss them — a distinct control (OWASP LLM05,
Improper Output Handling).
Defang, don't delete. URLs in active-content position are rewritten to a
non-resolvable but auditable form (https://evil.com -> hxxps://evil[.]com;
data:/javascript: colon neutralized to [:]); raw active HTML is escaped so a
renderer shows inert literal text. Visible information survives review; only the
machine-actionable affordance dies. Dot-defang is idempotent (never [[.]]).
Six classes, each a Finding: markdown-image (HIGH, the zero-click primitive),
inline-link (MEDIUM), reference-link definition (MEDIUM, the documented
image-filter bypass), angle-bracket autolink (MEDIUM), raw active HTML (HIGH,
inherently-active tag OR event/URL attribute — benign <b>/<em> left untouched),
standalone data: URI (HIGH). Processing order prevents double-counting.
Opt-in and separate: calling neutralize() IS the opt-in to mutate; the report-only
gate stays pure (design principles 3 & 4). Byte-identical on clean output, mirror
of the sanitizer invariant. Scope conceded in the docstring: a targeted defanger,
not a full HTML sanitizer.
17 tests: byte-identity + FP guards (lone <>[], metadata:, benign HTML), image
defang + non-resolvability, secret-exfil URL, inline/reference/autolink, raw-html
escape + script neutralization, data: URI, no double-count, counts, source.
[skip-docs]: README positioning + honest-limitations remains the deliberate
build-order step-11 deliverable (steps 1-5 likewise left README frozen).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HyRCQMocjZ6SmSQ6JidJ2k
Build-order step 5. Wrap untrusted content in a quarantine fence a downstream
trusted prompt can rely on: everything between the delimiters is data, never
instructions.
Two load-bearing properties:
- Per-call cryptographic nonce (secrets.token_hex, 128-bit) in the delimiter, so
an attacker embedded in the payload cannot forge the matching closing marker to
break out — the nonce is unpredictable and fresh every call.
- Marker-strip FIRST: fabricated fence markers already in the payload are removed
(any/no nonce, case-insensitive, ReDoS-safe negated-class regex) and flagged as
fence:marker-injection (HIGH, LLM01) before wrapping — defense in depth against
a lucky guess of the static skeleton, and it surfaces the attempt.
Pure text -> (fenced_text, report, nonce); only mutation is the marker-strip.
Nonce exposed so the caller can reference the fence in the trusted prompt.
10 tests: wrap/preserve, per-call randomness, nonce length, breakout containment,
marker-strip (+ case-insensitive), prose-word FP guard, source, empty input.
[skip-docs]: README positioning + honest-limitations is a deliberate build-order
step-11 deliverable (steps 1-4 likewise left README frozen). README status line
("pre-implementation") is stale and flagged for the step-11 refresh.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01K8GmKRCdsPjWYAKWsNgeQS
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
Build order step 4 — the load-bearing port from the llm-security seed
(injection-patterns.mjs + string-utils.mjs), stdlib-only.
- injection_lexicon.json: 83 patterns (CRITICAL/HIGH/HYBRID/MEDIUM) as the
single source of truth (regex + id + severity + owasp + desc), compiled once
by a thin loader. Decoupled from the engine for a future TS port.
- scan_lexicon(text, source, max_scan_chars) -> Report: matches every pattern
against a deduped variant set (raw / normalized / homoglyph-folded / rot13),
plus unicode-tag presence signal and the cognitive-load trap.
- normalize_for_scan chain ported: unicode-tags -> bidi -> HTML-entities ->
unicode/hex/URL escapes -> whole-string base64 (reuses entropy.try_decode_base64)
-> collapse letter-spacing; plus fold_homoglyphs / rot13.
- Self-safety (OWASP LLM10): input-size cap (scan prefix + flag oversize) and
ReDoS-safe port — the two nested-.*? sub-agent patterns bounded to
(?:\S+\s+){0,N}?; verified true positives still fire.
- Non-Latin data (homoglyph map, BIDI block) built from explicit code points;
JSON non-ASCII kept as \uXXXX escapes.
24 tests; 55 green total.
[skip-docs]: README positioning + honest-limitations is a deliberate build-order
step-11 deliverable (steps 1-3 likewise left README frozen). README status line
("pre-implementation") is stale and flagged for the step-11 refresh.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01K8GmKRCdsPjWYAKWsNgeQS
Build order step 3. Pure text -> findings detector ported from the
llm-security entropy-scanner seed:
- Length-calibrated Shannon-entropy classification (CRITICAL 5.4/128,
HIGH 5.1/64, MEDIUM 4.7/40).
- Shape floor: base64-like (len>100) / hex (len>64) reach at least MEDIUM
even when entropy alone does not trigger — the only path that catches hex
(16-symbol alphabet caps H at 4.0 < 4.7).
- Decode-and-rescan (must-have): base64 blobs that decode to printable text
are exposed on EntropyResult.decoded for a later lexicon rescan.
- FP suppression scoped to the text-relevant subset (base64 media data-URI
prefixes + SRI sha*- prefix); source-code-specific seed rules omitted.
Exposes ported primitives shannon_entropy / is_base64_like / is_hex_blob /
try_decode_base64. 16 new tests; full suite 31 green. Stdlib-only.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01K8GmKRCdsPjWYAKWsNgeQS
Add .gitignore (Python stack + obligatory lines; /STATE.md kept local-only
until a private remote exists), CHANGELOG.md, and the Communication patterns
section in CLAUDE.md. STATE.md is gitignored — must never reach the public
open/ mirror.
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
