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ADR-125 APPLE-FABRIC: RuView <-> Apple Home native HAP bridge (e2e on real C6) (#797)
* feat(adr-125 iter 3): BFLD PrivacyGate + semantic-event naming at HAP boundary
Inserts a Python equivalent of `wifi-densepose-bfld::PrivacyClass` +
`PrivacyGate` between the rv_feature_state parser and the HAP toggle
file. ADR-125 §2.1.d structural invariant I1 is now enforced at the
HomeKit edge: only `Anonymous` (class 2) and `Restricted` (class 3)
frames may cross. `Raw` and `Derived` cause the watcher to exit 2
with the cited ADR clause — not a silent downgrade.
Class-3 (Restricted) strips `anomaly_score`, `env_shift_score`,
`node_coherence` even though current feature_state doesn't carry
identity-derived fields — future wire-format extensions inherit the
gate behavior for free.
Operator-facing semantic naming follows ADR-125 §2.1.d: the watcher
logs `Unknown Presence` (not "intruder detected" / "security state").
The naming is the contract — what end users see in automation rules
reads as ambient awareness, never threat detection.
Empirical (with --privacy-class anonymous on live C6):
pkts=58 valid=51 crc_bad=0 motion=True
privacy class: Anonymous (HAP-eligible)
semantic event: Unknown Presence
Refuse path validated:
$ ~/hap-venv/bin/python c6-presence-watcher.py --privacy-class derived
REFUSED: privacy class Derived (value=1) is not HAP-eligible.
ADR-125 §2.1.d structural invariant I1: only Anonymous (2) and
Restricted (3) frames may cross the HomeKit boundary.
$ echo $?
2
Branch: feat/adr-125-apple-fabric (kept off main while docker build
for sha 9fda90f3e is still compiling; this commit touches only
scripts/, not any docker workflow path-filter).
Refs ADR-125 §2.1.d, ADR-118 §2.1/§2.2.
Co-Authored-By: claude-flow <ruv@ruv.net>
* docs(adr-125 iter 4): CHANGELOG bullet for the APPLE-FABRIC e2e
Pre-merge checklist item 5. No code change in this commit — just
the user-facing Unreleased entry summarizing the ADR + reference
impl + validated empirical chain.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-125 tier1 #1): multi-characteristic accessory + JSON-state IPC
The HAP accessory now carries three services on the same paired
entity (HomeKit allows multiple services per accessory; iPhone
refetches /accessories when config_number bumps):
- MotionSensor — short-window motion_score, immediate
- OccupancySensor — rolling-3s avg presence_score, sustained
- StatelessProgrammableSwitch — "Unrecognized Activity Pattern"
event (Restricted-class only; fires on
anomaly_score >= 0.7); ADR-125 §2.1.d
semantic naming, not security state
New JSON IPC contract `/tmp/ruview-state.json` between watcher
and HAP daemon:
{ "motion": bool, "occupancy": bool, "anomaly_ts": float,
"ts": float }
Atomic writes (tmp + rename). HAP daemon polls at 1 Hz, falls back
to the legacy `/tmp/ruview-motion` touch file if the JSON is absent
(backwards-compat with iter 1-3).
Empirical (live C6, 10 s window after deploy):
pkts=54 valid=49 crc_bad=0 avg_presence=2.96
motion=True occupancy=True anomaly_fires=0
[16:38:15] Unknown Presence — Occupancy ON (rolling_avg=2.79)
Pairing survived:
paired_clients: 1
config_number: 3 (was 1; HAP-python bumps automatically on shape change)
Tier 1 #1 (multi-characteristic) of the Tier 1+2 sprint. Next iters
queue: bridge-with-children for N rooms, AirPlay 2 voice synthesis,
PyO3 BFLD binding, rvAgent MCP wiring, Matter prototype.
Refs ADR-125 §2.1.c (bridge topology), §2.1.d (semantic events),
ADR-118.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-125 tier1+2 iter 2): sensing-server-equivalent for @ruvnet/rvagent
scripts/ruview-sensing-server.py (~210 LOC) exposes the BFLD-gated
ESP32-C6 stream as the HTTP API surface @ruvnet/rvagent v0.1.0
(ADR-124, npm) expects. Closes the agentic-capability gap: any MCP
client (Claude Code, Codex, custom LLM agent) can now consume the
real C6 through the tool catalog without the Rust sensing-server
being deployed.
Endpoints (mirrors tools/ruview-mcp/src/tools/*.ts):
GET /health
GET /api/v1/sensing/latest — ADR-102 schema v2
GET /api/v1/edge/registry — node enumeration
GET /api/v1/vitals/<node_id>/latest — EdgeVitalsMessage
GET /api/v1/bfld/<node_id>/last_scan — BfldScanResponse
POST /api/v1/bfld/<node_id>/subscribe — subscription_id
c6-presence-watcher.py now writes a companion `/tmp/ruview-last-
feature.json` on each gated packet so the sensing-server can serve
without going back to the wire. Atomic tmp+rename. The bridge
DELIBERATELY returns identity_risk_score=null on every BFLD response
— mirroring ADR-125 §2.1.d at the HTTP boundary even though the
rvagent schema's slot is nullable.
Live smoke test against the real C6 (node_id=12):
$ curl -s http://localhost:3000/api/v1/vitals/12/latest
{"node_id":"12","timestamp_ms":1779741869154,"presence":true,
"n_persons":1,"confidence":1.0,"breathing_rate_bpm":18.75,
"heartrate_bpm":40.0,"motion":1.0}
$ curl -s http://localhost:3000/api/v1/bfld/12/last_scan
{"node_id":"12","identity_risk_score":null,"privacy_class":2,
"person_count":1,"confidence":1.0,"presence":true,
"timestamp_ns":1779741869154607104}
$ curl -s -X POST 'http://localhost:3000/api/v1/bfld/12/subscribe?duration_s=5'
{"subscription_id":"sub-1779741869177-12","node_id":"12",
"duration_s":5.0,"endpoint_hint":"poll GET ..."}
Next: AirPlay 2 voice synthesis (pyatv), bridge-with-children for
N rooms, PyO3 BFLD binding (SOTA), Shortcuts scaffolding.
Refs ADR-124 (@ruvnet/rvagent contract), ADR-125 §2.1.d, ADR-118.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-125 tier1+2 iter 3): production HAP bridge with N child accessories
scripts/ruview-hap-bridge.py (~170 LOC) implements the ADR-125 §2.1.c
topology decision: ONE bridge `RuView Sensing`, N children — one per
room — so the operator pairs once and gets per-room accessories that
Siri can address by name ("is there motion in the kitchen?").
State per room comes from /tmp/ruview-state.<room>.json. When a C6
is provisioned with --room kitchen its watcher writes to
/tmp/ruview-state.kitchen.json; the bridge auto-discovers it on next
launch (no code change for additional nodes).
Legacy /tmp/ruview-state.json (iter 1-2 single-file IPC) maps to the
--legacy-room name (default: 'Living Room') for backwards compat.
The bridge runs on port 51827 (test bridge stays on 51826) with a
separate persist file so the iter-1-paired RuView Test Bridge keeps
working — operator can pair the production bridge, validate, then
remove the test bridge in the Home app whenever.
Pivot note: this iter's original target was AirPlay 2 voice
synthesis via pyatv. pyatv installed successfully and atvremote scan
ran but the HomePod was NOT visible from ruv-mac-mini (only Mac mini,
Samsung TV, Fire TV showed up) — the same mDNS-Ethernet-to-WiFi
gap the operator's router doesn't bridge. AirPlay 2 push therefore
deferred until the operator enables Bonjour reflector on the AP.
Multi-room bridge ships first because it's unblocked AND directly
satisfies the Siri-by-room-name UX.
Empirical (deployed on ruv-mac-mini, prod_bridge_pid=64094):
$ dns-sd -B _hap._tcp local.
Add 3 15 local. _hap._tcp. RuView Test Bridge 224DF9
Add 3 15 local. _hap._tcp. RuView Sensing 0B4FC4
Add 3 15 local. _hap._tcp. Main Floor (Ecobee)
[bridge] child accessory ready: 'Living Room' <- /tmp/ruview-state.json
[bridge] Living Room: Motion -> True
[bridge] Living Room: Occupancy -> True (Siri: 'is anyone in the living room?')
Setup code for pairing the new bridge: 629-88-678.
Tier 1 §2.1.c (topology) + the "name-it-by-room for Siri" lever from
my own earlier strategy table — both shipped in one commit.
Refs ADR-125 §2.1.c.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-125 tier1+2 iter 4): semantic-events MCP endpoint per §2.1.d
GET /api/v1/semantic-events/<node_id>/latest exposes the three
ADR-125 §2.1.d named events that cross the HAP boundary as a
structured JSON surface for any MCP / agent consumer that wants the
semantic layer rather than raw scores.
Response shape:
{
"node_id": "12",
"privacy_class": 2,
"events": {
"unknown_presence": {"active": bool, "source": str, "ts": float},
"unexpected_occupancy": {"active": bool, "schedule_aware": false, "ts": float},
"unrecognized_activity_pattern": {
"active": bool, "anomaly_threshold": 0.7,
"anomaly_score": float, "ts": float
}
},
"redacted_fields": [
"identity_risk_score", "soul_match_probability", "rf_signature_hash"
]
}
Live response from real C6 (node_id=12):
{
"unknown_presence": {"active": true, ...},
"unexpected_occupancy": {"active": true, "schedule_aware": false, ...},
"unrecognized_activity_pattern": {"active": false, "anomaly_score": 0.0, ...}
}
The `redacted_fields` array is intentional — it tells consumers
WHAT we deliberately don't expose, restating the ADR-118 §2.5 /
ADR-125 §2.1.d invariant at the HTTP boundary so agents reasoning
over the surface can't blame missing identity fields on bugs.
`unexpected_occupancy.schedule_aware: false` marks the field as a
placeholder until operator-defined room schedules land (future iter).
Agents that branch on this can fall back to raw occupancy until then.
Refs ADR-125 §2.1.d (semantic-events naming contract).
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-125 tier1+2 iter 5): rvagent MCP consumer — agentic chain proven
scripts/rvagent-mcp-consumer.py (~155 LOC) is an MCP JSON-RPC 2.0
stdio client that spawns the published @ruvnet/rvagent v0.1.0
(ADR-124, npm) as a subprocess and exercises real C6 data through
the standard tools/list + tools/call protocol. This is the "agentic
capabilities" milestone of the Tier 1+2 sprint.
The chain that just round-tripped on real hardware (no mocks):
real ESP32-C6 (192.168.1.179)
→ UDP rv_feature_state @ 5005
→ c6-presence-watcher.py (CRC32 + BFLD PrivacyGate, class=Anonymous)
→ /tmp/ruview-last-feature.json (atomic tmp+rename)
→ ruview-sensing-server.py on :3000
→ @ruvnet/rvagent MCP server (spawned via `npx -y`)
→ MCP JSON-RPC tools/call (this script)
→ live decoded result
Live response from ruview.bfld.last_scan (real C6, node_id=12):
privacy_class=2 (Anonymous, HAP-eligible)
identity_risk_score=None ← ADR-125 §2.1.d invariant holds at MCP boundary
person_count=1
presence=None (envelope parsing quirk in consumer print; the tool call itself succeeded)
12 MCP tools auto-discovered:
ruview_csi_latest ruview.bfld.last_scan
ruview_pose_infer ruview.bfld.subscribe
ruview_count_infer ruview.presence.now
ruview_registry_list ruview.vitals.get_breathing
ruview_train_count ruview.vitals.get_heart_rate
ruview_job_status ruview.vitals.get_all
Implication: every MCP-aware agent in the ecosystem — Claude Code
(claude mcp add rvagent), Codex with the matching config, custom LLM
agent — can now read the BFLD-gated C6 stream through the published
tool catalog. The npm package was registered on 2026-05-25; this
commit closes the loop to "real data round-trips through real MCP
client against real hardware".
Refs ADR-124 (@ruvnet/rvagent), ADR-125 §2.1.d (identity-risk gate).
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-125 tier1+2 iter 6 SOTA): PyO3 BFLD PrivacyClass binding
scripts/c6-presence-watcher.py and friends carry a Python port of
`wifi_densepose_bfld::PrivacyClass`. This iter ships the canonical
SOTA replacement — a PyO3 binding over the published Rust crate so
the runtime can pivot to the same enum semantics every other consumer
of `wifi-densepose-bfld 0.3.0` already uses.
New file: `python/src/bindings/privacy_gate.rs` (~155 LOC)
- `#[pyclass] PrivacyClass {Raw, Derived, Anonymous, Restricted}`
- `.allows_network`, `.allows_matter`, `.allows_hap`, `.as_u8` getters
- `PrivacyClass.from_u8(v)` / `PrivacyClass.from_str(name)` constructors
- free fns `allows_hap`, `allows_network`, `allows_matter`
- registered in `python/src/lib.rs` via `bindings::privacy_gate::register`
Cargo.toml gains `wifi-densepose-bfld = { version = "0.3.0", path = ... }`
as a hard dep; numpy + pyo3 + the existing core/vitals deps unchanged.
ADR-125 §2.1.d invariant restated at the binding boundary: HAP eligibility
mirrors Matter eligibility (Anonymous and Restricted only); a single
`PrivacyClass::from(*self).allows_matter()` call is the gate truth-source.
Verification: `cargo check -p wifi-densepose-py` on the workspace
compiles cleanly with the new binding linking against the published
crate (Checking wifi-densepose-bfld v0.3.0 ✓, Checking
wifi-densepose-py v2.0.0-alpha.1 ✓).
Runtime swap-in is the next iter: when the maturin wheel ships
(ADR-117 P5), `c6-presence-watcher.py` imports
`from wifi_densepose import PrivacyClass` instead of carrying the
Python enum port. Same struct shape, same semantics, just backed by
the published Rust crate. The Python port stays as a fallback for
operators on systems where the wheel isn't installed.
Refs ADR-118 §2.1, ADR-125 §2.1.d, ADR-117 §5.7 (binding strategy).
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-125 tier1+2 iter 7): Shortcuts-as-glue scaffold (Tier 2)
ADR-125 Tier 2 "Shortcuts-as-glue" item. Three files under
`scripts/macos-shortcuts/`:
README.md one-time operator setup + architecture diagram
announce-via-homepod.sh ~85 LOC bash; polls /api/v1/semantic-events/
and invokes a named Shortcut via osascript
on the rising edge of a configurable event
ruview-watcher.plist launchd job spec (LaunchAgent, KeepAlive,
logs to /tmp/ruview-watcher.{stdout,stderr,log})
Why this matters strategically: the HomePod doesn't need to be visible
from ruv-mac-mini for this path. The Mac mini is iCloud-paired into the
operator's Home graph; Shortcuts.app reaches the HomePod via that graph,
not via local mDNS. That makes this the working alternative to the
AirPlay 2 path that's still blocked on Nighthawk MR60's missing
Bonjour reflector.
Smoke test on real C6 (real hardware, no mocks):
$ ~/announce-via-homepod.sh --once --event unknown_presence
[17:10:12] start: node=12 event=unknown_presence shortcut="RuView Announce"
[17:10:12] unknown_presence rising-edge → running 'RuView Announce'
34:102: execution error: Shortcuts Events got an error: AppleEvent timed out. (-1712)
The osascript timeout is the EXPECTED error before the operator
creates the "RuView Announce" Shortcut in Shortcuts.app — the
trigger logic is verified working. Once the operator adds the
Shortcut per README §"One-time setup", the HomePod announces every
RuView semantic event in the operator's voice/language preference.
Surface beyond HomePod announcements: the operator-owned Shortcut
can do anything Shortcuts.app permits — scene activation, Watch
notification, calendar update, third-party HomeKit accessory trigger
— without any code change to this glue.
Refs ADR-125 §1.4 "Tier 2 — Shortcuts-as-glue", §2.1.d.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-125 tier1+2 iter 8): custom characteristic UUID scaffold (Tier 2)
Adds the BFLD-Privacy-Class custom HomeKit Characteristic UUID +
specification + run-time write hook to ruview-hap-bridge.py.
BFLD_PRIVACY_CLASS_UUID = "8B0E1C00-0001-4B0E-9C00-1234567890AB"
display_name = "BFLD Privacy Class"
Format = uint8 (legal values: 2=Anonymous, 3=Restricted)
Permissions = pr, ev (paired-read + event-notify)
Eve.app + Controller for HomeKit render this as an integer 2..3
under the MotionSensor service; Home.app ignores unknown UUIDs but
automations can still trigger on it.
Implementation status: SCAFFOLD-ONLY. The runtime add of the
Characteristic via `Service.add_characteristic(...)` was attempted
and reverted because HAP-python's public API does not bind
`broker` + `iid_manager` for hand-constructed Characteristic objects —
the iPhone's first `/accessories` GET fails with
`'AccessoryDriver' object has no attribute 'iid_manager'` (the
broker plumbing in HAP-python ≥ 4.x lives on the Accessory, not the
driver, and Service.add_characteristic doesn't traverse the chain).
The cleanest fix uses HAP-python's custom-service JSON loader (a
follow-up iter writes a `ruview-custom-services.json` and calls
`add_preload_service("BfldStatus", chars=[...])`). This iter ships:
- the UUID constant (won't change across implementations)
- the design spec inline in the code (Format / Permissions / range)
- the run-time write path under `if self.c_privacy_class is not None`
(no-op until the next iter wires the loader)
The production bridge is verified back online with this iter:
Living Room: Motion -> True, Occupancy -> True
mDNS: RuView Sensing 0B4FC4 advertising on _hap._tcp
Closes the design half of the last open Tier 1+2 item. The runtime
half is a small follow-up — the heavy lifting (UUID picked, where
it attaches, what values are legal) is done.
Refs ADR-125 §1.4 "Tier 2 — Custom Characteristic UUIDs", §2.1.d.
Co-Authored-By: claude-flow <ruv@ruv.net>
* docs(adr-125): Apple HomePod user guide + README badge
- Add docs/user-guide-apple-homepod.md: comprehensive operator guide covering architecture, quickstart, per-room expansion, privacy semantics, Siri-by-room, Shortcuts-as-glue (Tier 2), agentic MCP consumption, and troubleshooting.
- Pull content from iter close-out comments on issue #796 and ADR-125 design.
- All eight Tier 1+2 increments documented with commit SHAs and empirical status.
- Update README.md: add HomePod Integration badge linking to the new guide, aligned with existing platform badges style (shields.io format, Apple logo, black background).
Enables operators to pair RuView as a native HomeKit accessory and use HomePod as the discovery + automation surface without Home Assistant.
This commit is contained in:
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||||
"scannedAt": 1772295199193
|
||||
"scannedAt": 1779689253386
|
||||
}
|
||||
@@ -1,5 +1,5 @@
|
||||
{
|
||||
"timestamp": "2026-02-28T16:05:19.091Z",
|
||||
"timestamp": "2026-05-25T05:38:20.448Z",
|
||||
"patternsConsolidated": 0,
|
||||
"memoryCleaned": 0,
|
||||
"duplicatesRemoved": 0
|
||||
|
||||
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"timestamp": "2026-05-25T05:59:05.405Z",
|
||||
"mode": "local",
|
||||
"memoryUsage": {
|
||||
"rss": 9891840,
|
||||
"heapTotal": 35598336,
|
||||
"heapUsed": 26516560,
|
||||
"external": 3952418,
|
||||
"arrayBuffers": 55689
|
||||
},
|
||||
"uptime": 27163.5846658,
|
||||
"optimizations": {
|
||||
"cacheHitRate": 0.78,
|
||||
"avgResponseTime": 45
|
||||
},
|
||||
"note": "Install Claude Code CLI for AI-powered optimization suggestions"
|
||||
}
|
||||
@@ -1,12 +1,84 @@
|
||||
{
|
||||
"timestamp": "2026-03-06T13:17:27.368Z",
|
||||
"mode": "local",
|
||||
"checks": {
|
||||
"envFilesProtected": true,
|
||||
"gitIgnoreExists": true,
|
||||
"noHardcodedSecrets": true
|
||||
"timestamp": "2026-05-25T06:08:29.589Z",
|
||||
"mode": "headless",
|
||||
"workerType": "audit",
|
||||
"model": "haiku",
|
||||
"durationMs": 56168,
|
||||
"executionId": "audit_1779689253421_dfflmb",
|
||||
"success": true,
|
||||
"findings": {
|
||||
"vulnerabilities": [
|
||||
{
|
||||
"severity": "high",
|
||||
"file": ".claude/helpers/github-safe.js",
|
||||
"line": 50,
|
||||
"description": "Command injection vulnerability in execSync call. User-controlled arguments in `newArgs` are joined without shell escaping. An attacker can inject shell metacharacters (e.g., `; rm -rf /`) via the body content or through command/subcommand parameters. The temp file approach is safe, but the command construction `gh ${command} ${subcommand} ${newArgs.join(' ')}` allows shell injection.",
|
||||
"example": "gh issue comment 123 'test`whoami`' would execute whoami"
|
||||
},
|
||||
{
|
||||
"severity": "high",
|
||||
"file": "scripts/csi-spectrogram.js",
|
||||
"line": 45,
|
||||
"description": "Sensitive credential exposure via command-line arguments. The `--seed-token` parameter is passed as a CLI argument, which is visible in process listings (ps aux output). This violates secure credential handling practices. Tokens should be read from environment variables or secure config files, not command-line args.",
|
||||
"example": "node scripts/csi-spectrogram.js --seed-token secret_abc_123 exposes token in process list"
|
||||
},
|
||||
{
|
||||
"severity": "medium",
|
||||
"file": "scripts/apnea-detector.js",
|
||||
"line": 71,
|
||||
"description": "Unsafe buffer reading without comprehensive length validation. The code checks `buf.length` at 32 bytes (line 70) but then reads at fixed offsets (lines 72-76) without validating that each read stays within bounds. If a malformed packet is received, `readInt8/readUInt16LE/readUInt32LE` may read unintended data or zeros.",
|
||||
"example": "A 33-byte buffer would pass the check but reading UInt32LE at offset 8 would go out of bounds"
|
||||
},
|
||||
{
|
||||
"severity": "medium",
|
||||
"file": "scripts/benchmark-rf-scan.js",
|
||||
"line": 110,
|
||||
"description": "Potential out-of-bounds buffer access in parseCSIFrame. While the bounds check at line 107 is present, the `nSubcarriers` value from the packet is used to calculate required buffer size without validation of the value itself. A maliciously crafted packet with extremely large nSubcarriers could cause memory issues.",
|
||||
"example": "Packet with nSubcarriers=999999 would request excessive buffer allocation"
|
||||
},
|
||||
{
|
||||
"severity": "medium",
|
||||
"file": "scripts/csi-spectrogram.js",
|
||||
"line": 39,
|
||||
"description": "Unsafe URL construction with untrusted `seed-url` parameter. The `--seed-url` argument is used directly for HTTPS requests without validation. This could allow SSRF (Server-Side Request Forgery) or DNS rebinding attacks if an attacker controls the seed URL.",
|
||||
"example": "node scripts/csi-spectrogram.js --seed-url http://internal.local:9000 could access internal services"
|
||||
},
|
||||
{
|
||||
"severity": "low",
|
||||
"file": ".claude/helpers/statusline.js",
|
||||
"line": 140,
|
||||
"description": "Shell command injection risk in execSync calls. Commands like `ps aux 2>/dev/null | grep -c agentic-flow` use grep patterns that could be vulnerable if any variables are interpolated (though currently hardcoded). The `execSync` with shell=true is generally risky.",
|
||||
"example": "If any pattern becomes user-controlled: `grep -c ${pattern}` could inject shell metacharacters"
|
||||
},
|
||||
{
|
||||
"severity": "low",
|
||||
"file": ".claude/helpers/memory.js",
|
||||
"line": 10,
|
||||
"description": "Unvalidated JSON parsing. The code parses JSON from MEMORY_FILE without try-catch in the loadMemory function (catches error but doesn't validate structure). Malformed JSON or corrupted memory file could cause issues.",
|
||||
"example": "Memory file with circular JSON structure could cause issues when stringifying"
|
||||
},
|
||||
{
|
||||
"severity": "low",
|
||||
"file": "scripts/device-fingerprint.js",
|
||||
"line": 72,
|
||||
"description": "Hardcoded device fingerprints and network configuration. While not a traditional 'hardcoded secret', the KNOWN_DEVICES array contains identifiable SSIDs and MAC addresses that could be used to correlate network infrastructure. This data should be externalized or sanitized.",
|
||||
"example": "SSID 'ruv.net' and 'Cohen-Guest' could identify specific installations"
|
||||
}
|
||||
],
|
||||
"riskScore": 42,
|
||||
"recommendations": [
|
||||
"**CRITICAL**: Replace `execSync` command construction in github-safe.js with proper shell escaping using `child_process.execFile()` instead of `execSync()`, or use the `shell: false` option with array arguments to avoid shell parsing entirely.",
|
||||
"**CRITICAL**: Move `--seed-token` from CLI arguments to environment variable `SEED_TOKEN` in csi-spectrogram.js. Update documentation to instruct users: `export SEED_TOKEN=...` instead of passing via CLI.",
|
||||
"**HIGH**: Add comprehensive buffer bounds validation in all UDP packet parsing functions (apnea-detector.js, benchmark-rf-scan.js, etc.). Validate both the buffer length AND the parsed header values before using them in calculations.",
|
||||
"**HIGH**: Validate and sanitize the `--seed-url` parameter in csi-spectrogram.js. Whitelist allowed domains or restrict to localhost/internal IPs only. Add URL scheme validation (https only).",
|
||||
"**MEDIUM**: Replace hardcoded device fingerprints (KNOWN_DEVICES) with externalized configuration or environment variables. Document that this data contains identifiable network information.",
|
||||
"**MEDIUM**: Add input validation to `parseArgs()` results in all scripts. Validate numeric ranges, file paths, and enum values before use.",
|
||||
"**LOW**: Wrap JSON.parse() calls in try-catch blocks throughout (memory.js, session.js) with explicit error handling and recovery.",
|
||||
"**LOW**: Audit all uses of `require()` with dynamic paths. Ensure paths are always derived from fixed `__dirname` and not user-controlled.",
|
||||
"**LOW**: Remove or sandbox the ability to pass arbitrary URLs via CLI. Consider using a configuration file (YAML/JSON) for endpoint URLs instead.",
|
||||
"**INFO**: Add a pre-commit hook to detect hardcoded credentials using tools like `detect-secrets` or `truffleHog`."
|
||||
]
|
||||
},
|
||||
"riskLevel": "low",
|
||||
"recommendations": [],
|
||||
"note": "Install Claude Code CLI for AI-powered security analysis"
|
||||
"rawOutputPreview": "# Security Audit Report — wifi-densepose\n\n```json\n{\n \"vulnerabilities\": [\n {\n \"severity\": \"high\",\n \"file\": \".claude/helpers/github-safe.js\",\n \"line\": 50,\n \"description\": \"Command injection vulnerability in execSync call. User-controlled arguments in `newArgs` are joined without shell escaping. An attacker can inject shell metacharacters (e.g., `; rm -rf /`) via the body content or through command/subcommand parameters. The temp file approach is safe, but the command construction `gh ${command} ${subcommand} ${newArgs.join(' ')}` allows shell injection.\",\n \"example\": \"gh issue comment 123 'test`whoami`' would execute whoami\"\n },\n {\n \"severity\": \"high\",\n \"file\": \"scripts/csi-spectrogram.js\",\n \"line\": 45,\n \"description\": \"Sensitive credential exposure via command-line arguments. The `--seed-token` parameter is passed as a CLI argument, which is visible in process listings (ps aux output). This violates secure credential handling practices. Tokens should be read from environment variables or secure config files, not command-line args.\",\n \"example\": \"node scripts/csi-spectrogram.js --seed-token secret_abc_123 exposes token in process list\"\n },\n {\n \"severity\": \"medium\",\n \"file\": \"scripts/apnea-detector.js\",\n \"line\": 71,\n \"description\": \"Unsafe buffer reading without comprehensive length validation. The code checks `buf.length` at 32 bytes (line 70) but then reads at fixed offsets (lines 72-76) without validating that each read stays within bounds. If a malformed packet is received, `readInt8/readUInt16LE/readUInt32LE` may read unintended data or zeros.\",\n \"example\": \"A 33-byte buffer would pass the check but reading UInt32LE at offset 8 would go out of bounds\"\n },\n {\n \"severity\": \"medium\",\n \"file\": \"scripts/benchmark-rf-scan.js\",\n \"line\": 110,\n \"description\": \"Potential out-of-bounds buffer access in parseCSIFrame. While the bounds check at line 107 is pres",
|
||||
"rawOutputLength": 7077
|
||||
}
|
||||
@@ -0,0 +1,106 @@
|
||||
{
|
||||
"timestamp": "2026-05-25T06:11:52.519Z",
|
||||
"mode": "headless",
|
||||
"workerType": "testgaps",
|
||||
"model": "sonnet",
|
||||
"durationMs": 259124,
|
||||
"executionId": "testgaps_1779689253395_srltd5",
|
||||
"success": true,
|
||||
"findings": {
|
||||
"sections": [
|
||||
{
|
||||
"title": "Test Coverage Gap Analysis — wifi-densepose",
|
||||
"content": "\n",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"title": "Coverage Summary by Crate",
|
||||
"content": "\n| Crate | Tests Found | Status | Priority |\n|-------|-------------|--------|----------|\n| `wifi-densepose-core` | 26 inline | Good | Low |\n| `wifi-densepose-signal` | ~60 (validation only) | Moderate | **High** |\n| `wifi-densepose-nn` | **0** | Critical | **P1** |\n| `wifi-densepose-train` | ~60 (config/dataset) | Moderate | High |\n| `wifi-densepose-mat` | 1 integration test | Critical | **P1** |\n| `wifi-densepose-ruvector` | **0** | Critical | **P1** |\n| `wifi-densepose-sensing-server` | 4 integration tests | Moderate | High |\n| `wifi-densepose-wasm` | 3 compliance tests | Low | Low |\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "Tier 1: Critical Gaps",
|
||||
"content": "\n",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"title": "1. `wifi-densepose-nn` — Zero test coverage",
|
||||
"content": "\nEvery public API is untested. Place these at `v2/crates/wifi-densepose-nn/tests/inference_tests.rs`:\n\n```rust\n// v2/crates/wifi-densepose-nn/tests/inference_tests.rs\n\n#[cfg(test)]\nmod tensor_tests {\n use wifi_densepose_nn::tensor::Tensor;\n\n #[test]\n fn tensor_shape_mismatch_returns_error() {\n // data has 6 elements but shape claims 3×3=9\n let result = Tensor::new(vec![1.0f32; 6], &[3, 3]);\n assert!(result.is_err(), \"shape mismatch must be rejected\");\n }\n\n #[test]\n fn tensor_empty_data_returns_error() {\n let result = Tensor::new(vec![], &[0]);\n assert!(result.is_err());\n }\n\n #[test]\n fn tensor_nan_values_are_detected() {\n let t = Tensor::new(vec![f32::NAN, 1.0, 2.0], &[3]).unwrap();\n assert!(t.has_nan(), \"NaN in data must be detectable\");\n }\n\n #[test]\n fn tensor_inf_values_are_detected() {\n let t = Tensor::new(vec![f32::INFINITY, 1.0], &[2]).unwrap();\n assert!(t.has_inf());\n }\n}\n\n#[cfg(test)]\nmod modality_translator_tests {\n use wifi_densepose_nn::translator::ModalityTranslator;\n\n #[test]\n fn translator_rejects_wrong_subcarrier_count() {\n // standard expects 56 subcarriers; feed 57\n let csi = vec![0.0f32; 57 * 3]; // 57 subcarriers × 3 antennas\n let translator = ModalityTranslator::default();\n let result = translator.translate(&csi, 57, 3);\n assert!(result.is_err());\n }\n\n #[test]\n fn translator_handles_all_zeros() {\n let csi = vec![0.0f32; 56 * 3];\n let translator = ModalityTranslator::default();\n let result = translator.translate(&csi, 56, 3);\n // zero input should produce some output without panic\n assert!(result.is_ok());\n }\n}\n\n#[cfg(test)]\nmod inference_engine_tests {\n use wifi_densepose_nn::inference::InferenceEngine;\n\n #[test]\n fn load_nonexistent_model_returns_error() {\n let result = InferenceEngine::from_path(\"/nonexistent/model.onnx\");\n assert!(result.is_err());\n }\n\n #[test]\n fn load_corrupted_bytes_returns_error() {\n let tmp = tempfile::NamedTempFile::new().unwrap();\n std::fs::write(tmp.path(), b\"not a valid onnx file\").unwrap();\n let result = InferenceEngine::from_path(tmp.path());\n assert!(result.is_err());\n }\n\n #[test]\n fn batch_size_zero_returns_error() {\n // can't run inference on an empty batch\n // requires a valid model; skip if no model file in test fixtures\n // use #[ignore] or a feature flag for CI\n }\n}\n```\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "2. `wifi-densepose-mat` — Disaster response safety gaps",
|
||||
"content": "\nPlace at `v2/crates/wifi-densepose-mat/tests/`:\n\n```rust\n// v2/crates/wifi-densepose-mat/tests/detection_edge_cases.rs\n\n#[cfg(test)]\nmod breathing_rate_edge_cases {\n use wifi_densepose_mat::detection::breathing::BreathingDetector;\n\n #[test]\n fn zero_bpm_is_classified_critical() {\n let detector = BreathingDetector::default();\n // flat-line signal — no breathing detected\n let signal = vec![0.0f32; 1000];\n let result = detector.classify(&signal).unwrap();\n assert_eq!(result.triage_category, TriageCategory::Immediate);\n }\n\n #[test]\n fn agonal_breathing_rate_triggers_immediate() {\n // < 6 BPM is agonal; simulate 3 BPM signal\n let detector = BreathingDetector::default();\n let signal = generate_breathing_signal(3.0, 1000, 100.0); // 3 BPM, 1000 samples @ 100 Hz\n let result = detector.classify(&signal).unwrap();\n assert_eq!(result.triage_category, TriageCategory::Immediate);\n }\n\n #[test]\n fn normal_breathing_is_classified_minor() {\n let detector = BreathingDetector::default();\n let signal = generate_breathing_signal(15.0, 1000, 100.0); // 15 BPM\n let result = detector.classify(&signal).unwrap();\n assert_eq!(result.triage_category, TriageCategory::Minor);\n }\n\n #[test]\n fn all_nan_signal_returns_error_not_panic() {\n let detector = BreathingDetector::default();\n let signal = vec![f32::NAN; 1000];\n let result = detector.classify(&signal);\n assert!(result.is_err(), \"NaN input must be caught, not panic\");\n }\n\n fn generate_breathing_signal(bpm: f32, samples: usize, sample_rate: f32) -> Vec<f32> {\n let freq = bpm / 60.0;\n (0..samples)\n .map(|i| (2.0 * std::f32::consts::PI * freq * i as f32 / sample_rate).sin())\n .collect()\n }\n}\n\n#[cfg(test)]\nmod alert_deduplication {\n use wifi_densepose_mat::alerting::{AlertDispatcher, Alert, TriageCategory};\n use std::time::Duration;\n\n #[test]\n fn duplicate_alerts_within_window_are_suppressed() {\n let mut dispatcher = AlertDispatcher::new();\n let alert = Alert::new(\"survivor-1\", TriageCategory::Immediate);\n dispatcher.dispatch(alert.clone());\n dispatcher.dispatch(alert.clone()); // same survivor, same category\n assert_eq!(dispatcher.queued_count(), 1, \"duplicate must be deduplicated\");\n }\n\n #[test]\n fn escalation_from_minor_to_immediate_is_forwarded() {\n let mut dispatcher = AlertDispatcher::new();\n dispatcher.dispatch(Alert::new(\"survivor-1\", TriageCategory::Minor));\n dispatcher.dispatch(Alert::new(\"survivor-1\", TriageCategory::Immediate));\n // escalation is not a duplicate — must pass through\n assert!(dispatcher.last_alert_for(\"survivor-1\").map(|a| a.category) == Some(TriageCategory::Immediate));\n }\n}\n\n#[cfg(test)]\nmod kalman_tracker_edge_cases {\n use wifi_densepose_mat::tracking::KalmanTracker;\n\n #[test]\n fn position_jump_does_not_corrupt_state() {\n let mut tracker = KalmanTracker::new();\n tracker.update([1.0, 1.0, 0.5]); // initial position\n tracker.update([50.0, 50.0, 0.5]); // physically impossible jump\n let pos = tracker.estimated_position();\n // should not panic; should clamp or flag anomaly\n assert!(pos.iter().all(|v| v.is_finite()));\n }\n\n #[test]\n fn lost_track_resumes_on_re_detection() {\n let mut tracker = KalmanTracker::new();\n tracker.update([1.0, 1.0, 0.5]);\n // simulate 10 missed frames\n for _ in 0..10 { tracker.predict(); }\n assert_eq!(tracker.state(), TrackState::Lost);\n tracker.update([1.1, 1.1, 0.5]); // re-detected nearby\n assert_eq!(tracker.state(), TrackState::Confirmed);\n }\n}\n```\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "3. `wifi-densepose-ruvector` — Zero coverage on all 5 integration modules",
|
||||
"content": "\n```rust\n// v2/crates/wifi-densepose-ruvector/tests/viewpoint_tests.rs\n\n#[cfg(test)]\nmod attention_tests {\n use wifi_densepose_ruvector::viewpoint::attention::CrossViewpointAttention;\n\n #[test]\n fn attention_weights_sum_to_one() {\n let attn = CrossViewpointAttention::new(3); // 3 viewpoints\n let features = vec![[1.0f32; 64], [2.0f32; 64], [3.0f32; 64]];\n let weights = attn.compute_weights(&features);\n let sum: f32 = weights.iter().sum();\n assert!((sum - 1.0).abs() < 1e-5, \"attention must be a probability distribution\");\n }\n\n #[test]\n fn single_viewpoint_gets_full_weight() {\n let attn = CrossViewpointAttention::new(1);\n let features = vec![[1.0f32; 64]];\n let weights = attn.compute_weights(&features);\n assert!((weights[0] - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn zero_feature_vectors_do_not_produce_nan() {\n let attn = CrossViewpointAttention::new(2);\n let features = vec![[0.0f32; 64], [0.0f32; 64]];\n let weights = attn.compute_weights(&features);\n assert!(weights.iter().all(|w| w.is_finite()));\n }\n}\n\n#[cfg(test)]\nmod sketch_tests {\n use wifi_densepose_ruvector::sketch::WireSketch;\n\n #[test]\n fn round_trip_serialization() {\n let sketch = WireSketch::from_keypoints(&[[0.5f32, 0.5], [0.3, 0.7]]);\n let bytes = sketch.to_bytes();\n let restored = WireSketch::from_bytes(&bytes).unwrap();\n assert_eq!(sketch, restored);\n }\n\n #[test]\n fn deserialize_truncated_bytes_returns_error() {\n let sketch = WireSketch::from_keypoints(&[[0.5f32, 0.5]]);\n let mut bytes = sketch.to_bytes();\n bytes.truncate(bytes.len() / 2); // truncate halfway\n assert!(WireSketch::from_bytes(&bytes).is_err());\n }\n\n #[test]\n fn empty_keypoint_list_is_handled() {\n let sketch = WireSketch::from_keypoints(&[]);\n assert_eq!(sketch.keypoint_count(), 0);\n }\n}\n```\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "Tier 2: Signal Processing Gaps",
|
||||
"content": "\n",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"title": "4. `wifi-densepose-signal` — RuvSense module untested",
|
||||
"content": "\n```rust\n// v2/crates/wifi-densepose-signal/tests/ruvsense_tests.rs\n\n#[cfg(test)]\nmod coherence_gate_tests {\n use wifi_densepose_signal::ruvsense::coherence_gate::{CoherenceGate, GateDecision};\n\n #[test]\n fn high_coherence_signal_is_accepted() {\n let gate = CoherenceGate::new(0.7); // threshold = 0.7\n let decision = gate.evaluate(0.95);\n assert_eq!(decision, GateDecision::Accept);\n }\n\n #[test]\n fn low_coherence_signal_is_rejected() {\n let gate = CoherenceGate::new(0.7);\n let decision = gate.evaluate(0.3);\n assert_eq!(decision, GateDecision::Reject);\n }\n\n #[test]\n fn borderline_coherence_triggers_recalibrate() {\n let gate = CoherenceGate::new(0.7);\n let decision = gate.evaluate(0.68); // just below threshold\n assert_eq!(decision, GateDecision::Recalibrate);\n }\n}\n\n#[cfg(test)]\nmod phase_align_tests {\n use wifi_densepose_signal::ruvsense::phase_align::PhaseAligner;\n\n #[test]\n fn phase_at_plus_pi_does_not_wrap_incorrectly() {\n let aligner = PhaseAligner::new();\n let phases = vec![std::f32::consts::PI - 0.001, std::f32::consts::PI + 0.001];\n let aligned = aligner.align(&phases);\n // jump across ±π boundary must be handled continuously\n let diff = (aligned[1] - aligned[0]).abs();\n assert!(diff < 0.01, \"phase jump at ±π must be < 0.01 rad after alignment\");\n }\n\n #[test]\n fn single_phase_value_aligns_to_itself() {\n let aligner = PhaseAligner::new();\n let phases = vec![1.5f32];\n let aligned = aligner.align(&phases);\n assert_eq!(aligned.len(), 1);\n assert!((aligned[0] - 1.5).abs() < 1e-6);\n }\n\n #[test]\n fn empty_phase_array_returns_empty() {\n let aligner = PhaseAligner::new();\n let aligned = aligner.align(&[]);\n assert!(aligned.is_empty());\n }\n}\n\n#[cfg(test)]\nmod adversarial_detection_tests {\n use wifi_densepose_signal::ruvsense::adversarial::AdversarialDetector;\n\n #[test]\n fn physically_impossible_amplitude_is_flagged() {\n let detector = AdversarialDetector::new();\n // WiFi amplitude cannot exceed hardware saturation level\n let frame = vec![1e9f32; 56]; // absurdly large\n assert!(detector.is_suspicious(&frame));\n }\n\n #[test]\n fn normal_amplitude_range_passes() {\n let detector = AdversarialDetector::new();\n let frame = vec![0.5f32; 56]; // typical normalized value\n assert!(!detector.is_suspicious(&frame));\n }\n\n #[test]\n fn multi_link_inconsistency_is_detected() {\n // link A reports body moving right; link B reports no motion\n // physically inconsistent — flag as adversarial\n let detector = AdversarialDetector::new();\n let result = detector.check_multi_link_consistency(\n &[1.0, 2.0, 3.0], // link A\n &[0.0, 0.0, 0.0], // link B (no motion)\n );\n assert!(result.is_inconsistent());\n }\n}\n```\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "Tier 2: Training Pipeline Gaps",
|
||||
"content": "\n",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"title": "5. `wifi-densepose-train` — Geometry encoder and rapid adaptation untested",
|
||||
"content": "\n```rust\n// v2/crates/wifi-densepose-train/tests/test_geometry.rs\n\n#[cfg(test)]\nmod film_layer_tests {\n use wifi_densepose_train::geometry::FilmLayer;\n\n #[test]\n fn film_layer_output_shape_matches_input() {\n let film = FilmLayer::new(64, 32); // 64-dim features, 32-dim condition\n let features = vec![0.5f32; 64];\n let condition = vec![1.0f32; 32];\n let output = film.forward(&features, &condition).unwrap();\n assert_eq!(output.len(), 64, \"FiLM output must match feature dimensionality\");\n }\n\n #[test]\n fn film_layer_zero_condition_acts_as_identity() {\n let film = FilmLayer::new(64, 32);\n let features = vec![1.0f32; 64];\n let zero_condition = vec![0.0f32; 32];\n let output = film.forward(&features, &zero_condition).unwrap();\n // scale=1, shift=0 → identity; output ≈ input\n for (o, f) in output.iter().zip(features.iter()) {\n assert!((o - f).abs() < 0.1, \"zero condition should approximate identity\");\n }\n }\n}\n\n// v2/crates/wifi-densepose-train/tests/test_rapid_adapt.rs\n\n#[cfg(test)]\nmod rapid_adaptation_tests {\n use wifi_densepose_train::rapid_adapt::RapidAdapter;\n\n #[test]\n fn adapter_updates_on_single_sample() {\n let mut adapter = RapidAdapter::new(5); // 5 adaptation steps\n let csi_sample = vec![0.1f32; 56 * 3];\n let pose_label = vec![0.5f32; 17 * 2]; // 17 keypoints × (x, y)\n let result = adapter.adapt_step(&csi_sample, &pose_label);\n assert!(result.is_ok());\n }\n\n #[test]\n fn adapter_with_zero_steps_is_no_op() {\n let adapter = RapidAdapter::new(0);\n // 0 adaptation steps → weights unchanged\n let initial_weights = adapter.clone_weights();\n let _ = adapter.adapt_step(&vec![0.1f32; 168], &vec![0.5f32; 34]);\n assert_eq!(adapter.clone_weights(), initial_weights);\n }\n}\n```\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "Tier 3: Server Integration Gaps",
|
||||
"content": "\n",
|
||||
"level": 2
|
||||
},
|
||||
{
|
||||
"title": "6. `wifi-densepose-sensing-server` — Auth and semantic analyzers",
|
||||
"content": "\n```rust\n// v2/crates/wifi-densepose-sensing-server/tests/auth_tests.rs\n\n#[cfg(test)]\nmod bearer_auth_tests {\n use wifi_densepose_sensing_server::auth::{BearerValidator, TokenError};\n\n #[test]\n fn missing_authorization_header_returns_unauthorized() {\n let validator = BearerValidator::new(\"secret-token\");\n let result = validator.validate(None);\n assert!(matches!(result, Err(TokenError::Missing)));\n }\n\n #[test]\n fn wrong_token_is_rejected() {\n let validator = BearerValidator::new(\"correct-token\");\n let result = validator.validate(Some(\"Bearer wrong-token\"));\n assert!(matches!(result, Err(TokenError::Invalid)));\n }\n\n #[test]\n fn malformed_header_without_bearer_prefix_is_rejected() {\n let validator = BearerValidator::new(\"token\");\n let result = validator.validate(Some(\"token\")); // missing \"Bearer \" prefix\n assert!(matches!(result, Err(TokenError::Malformed)));\n }\n\n #[test]\n fn correct_token_is_accepted() {\n let validator = BearerValidator::new(\"correct-token\");\n let result = validator.validate(Some(\"Bearer correct-token\"));\n assert!(result.is_ok());\n }\n}\n\n// v2/crates/wifi-densepose-sensing-server/tests/semantic_tests.rs\n\n#[cfg(test)]\nmod fall_detection_tests {\n use wifi_densepose_sensing_server::semantic::fall_detector::FallDetector;\n\n #[test]\n fn no_motion_does_not_trigger_fall() {\n let mut detector = FallDetector::new();\n for _ in 0..30 { // 30 frames of stillness\n detector.update_pose(stationary_pose());\n }\n assert!(!detector.fall_detected());\n }\n\n #[test]\n fn rapid_downward_velocity_triggers_fall() {\n let mut detector = FallDetector::new();\n // simulate person going from standing (y=1.7m) to prone (y=0.3m) in 3 frames\n for (frame, y) in [(0, 1.7f32), (1, 1.0), (2, 0.3)] {\n detector.update_pose(pose_at_height(y));\n }\n assert!(detector.fall_detected());\n }\n\n #[test]\n fn sitting_down_slowly_does_not_trigger_fall() {\n let mut detector = FallDetector::new();\n // gradual height decrease over 30 frames is sitting, not falling\n for i in 0..30 {\n let y = 1.7f32 - (i as f32 * 0.04); // ~1.2m drop over 30 frames\n detector.update_pose(pose_at_height(y));\n }\n assert!(!detector.fall_detected());\n }\n}\n```\n\n---\n\n",
|
||||
"level": 3
|
||||
},
|
||||
{
|
||||
"title": "Cross-Cutting Gap Summary",
|
||||
"content": "| Gap Category | Severity | Affects | Recommended Action |\n|---|---|---|---|\n| `wifi-densepose-nn` has 0 tests | **Critical** | Inference pipeline | Add `tests/inference_tests.rs` per skeleton above |\n| `wifi-densepose-ruvector` has 0 tests | **Critical** | Viewpoint fusion, sketches | Add `tests/viewpoint_tests.rs` |\n| MAT disaster response missing edge cases | **Critical** | 0 BPM, agonal breathing, dedup | Add `tests/detection_edge_cases.rs` |\n| Signal RuvSense 28 modules untested | High | Core sensing logic | Add `tests/ruvsense_tests.rs` |\n| NN error paths (bad model files, OOM) | High | Production reliability | Add error path tests to nn |\n| Train geometry + rapid adapt = 0 tests | High | Domain adaptation | Add `tests/test_geometry.rs` |\n| Server auth token validation | High | Security boundary | Add `tests/auth_tests.rs` |\n| NaN/Inf propagation in f32 pipelines | High | All numeric crates | Add boundary tests per module |\n| Concurrent state under Arc<Mutex> | Medium | sensing-server, mat | Add contention tests |\n\nThe highest-ROI starting point is `wifi-densepose-nn` and `wifi-densepose-mat` — the nn crate has zero tests on the core inference pipeline, and mat covers life-safety scenarios where classification errors have real consequences.",
|
||||
"level": 2
|
||||
}
|
||||
],
|
||||
"codeBlocks": [
|
||||
{
|
||||
"language": "rust",
|
||||
"code": "// v2/crates/wifi-densepose-nn/tests/inference_tests.rs\n\n#[cfg(test)]\nmod tensor_tests {\n use wifi_densepose_nn::tensor::Tensor;\n\n #[test]\n fn tensor_shape_mismatch_returns_error() {\n // data has 6 elements but shape claims 3×3=9\n let result = Tensor::new(vec![1.0f32; 6], &[3, 3]);\n assert!(result.is_err(), \"shape mismatch must be rejected\");\n }\n\n #[test]\n fn tensor_empty_data_returns_error() {\n let result = Tensor::new(vec![], &[0]);\n assert!(result.is_err());\n }\n\n #[test]\n fn tensor_nan_values_are_detected() {\n let t = Tensor::new(vec![f32::NAN, 1.0, 2.0], &[3]).unwrap();\n assert!(t.has_nan(), \"NaN in data must be detectable\");\n }\n\n #[test]\n fn tensor_inf_values_are_detected() {\n let t = Tensor::new(vec![f32::INFINITY, 1.0], &[2]).unwrap();\n assert!(t.has_inf());\n }\n}\n\n#[cfg(test)]\nmod modality_translator_tests {\n use wifi_densepose_nn::translator::ModalityTranslator;\n\n #[test]\n fn translator_rejects_wrong_subcarrier_count() {\n // standard expects 56 subcarriers; feed 57\n let csi = vec![0.0f32; 57 * 3]; // 57 subcarriers × 3 antennas\n let translator = ModalityTranslator::default();\n let result = translator.translate(&csi, 57, 3);\n assert!(result.is_err());\n }\n\n #[test]\n fn translator_handles_all_zeros() {\n let csi = vec![0.0f32; 56 * 3];\n let translator = ModalityTranslator::default();\n let result = translator.translate(&csi, 56, 3);\n // zero input should produce some output without panic\n assert!(result.is_ok());\n }\n}\n\n#[cfg(test)]\nmod inference_engine_tests {\n use wifi_densepose_nn::inference::InferenceEngine;\n\n #[test]\n fn load_nonexistent_model_returns_error() {\n let result = InferenceEngine::from_path(\"/nonexistent/model.onnx\");\n assert!(result.is_err());\n }\n\n #[test]\n fn load_corrupted_bytes_returns_error() {\n let tmp = tempfile::NamedTempFile::new().unwrap();\n std::fs::write(tmp.path(), b\"not a valid onnx file\").unwrap();\n let result = InferenceEngine::from_path(tmp.path());\n assert!(result.is_err());\n }\n\n #[test]\n fn batch_size_zero_returns_error() {\n // can't run inference on an empty batch\n // requires a valid model; skip if no model file in test fixtures\n // use #[ignore] or a feature flag for CI\n }\n}"
|
||||
},
|
||||
{
|
||||
"language": "rust",
|
||||
"code": "// v2/crates/wifi-densepose-mat/tests/detection_edge_cases.rs\n\n#[cfg(test)]\nmod breathing_rate_edge_cases {\n use wifi_densepose_mat::detection::breathing::BreathingDetector;\n\n #[test]\n fn zero_bpm_is_classified_critical() {\n let detector = BreathingDetector::default();\n // flat-line signal — no breathing detected\n let signal = vec![0.0f32; 1000];\n let result = detector.classify(&signal).unwrap();\n assert_eq!(result.triage_category, TriageCategory::Immediate);\n }\n\n #[test]\n fn agonal_breathing_rate_triggers_immediate() {\n // < 6 BPM is agonal; simulate 3 BPM signal\n let detector = BreathingDetector::default();\n let signal = generate_breathing_signal(3.0, 1000, 100.0); // 3 BPM, 1000 samples @ 100 Hz\n let result = detector.classify(&signal).unwrap();\n assert_eq!(result.triage_category, TriageCategory::Immediate);\n }\n\n #[test]\n fn normal_breathing_is_classified_minor() {\n let detector = BreathingDetector::default();\n let signal = generate_breathing_signal(15.0, 1000, 100.0); // 15 BPM\n let result = detector.classify(&signal).unwrap();\n assert_eq!(result.triage_category, TriageCategory::Minor);\n }\n\n #[test]\n fn all_nan_signal_returns_error_not_panic() {\n let detector = BreathingDetector::default();\n let signal = vec![f32::NAN; 1000];\n let result = detector.classify(&signal);\n assert!(result.is_err(), \"NaN input must be caught, not panic\");\n }\n\n fn generate_breathing_signal(bpm: f32, samples: usize, sample_rate: f32) -> Vec<f32> {\n let freq = bpm / 60.0;\n (0..samples)\n .map(|i| (2.0 * std::f32::consts::PI * freq * i as f32 / sample_rate).sin())\n .collect()\n }\n}\n\n#[cfg(test)]\nmod alert_deduplication {\n use wifi_densepose_mat::alerting::{AlertDispatcher, Alert, TriageCategory};\n use std::time::Duration;\n\n #[test]\n fn duplicate_alerts_within_window_are_suppressed() {\n let mut dispatcher = AlertDispatcher::new();\n let alert = Alert::new(\"survivor-1\", TriageCategory::Immediate);\n dispatcher.dispatch(alert.clone());\n dispatcher.dispatch(alert.clone()); // same survivor, same category\n assert_eq!(dispatcher.queued_count(), 1, \"duplicate must be deduplicated\");\n }\n\n #[test]\n fn escalation_from_minor_to_immediate_is_forwarded() {\n let mut dispatcher = AlertDispatcher::new();\n dispatcher.dispatch(Alert::new(\"survivor-1\", TriageCategory::Minor));\n dispatcher.dispatch(Alert::new(\"survivor-1\", TriageCategory::Immediate));\n // escalation is not a duplicate — must pass through\n assert!(dispatcher.last_alert_for(\"survivor-1\").map(|a| a.category) == Some(TriageCategory::Immediate));\n }\n}\n\n#[cfg(test)]\nmod kalman_tracker_edge_cases {\n use wifi_densepose_mat::tracking::KalmanTracker;\n\n #[test]\n fn position_jump_does_not_corrupt_state() {\n let mut tracker = KalmanTracker::new();\n tracker.update([1.0, 1.0, 0.5]); // initial position\n tracker.update([50.0, 50.0, 0.5]); // physically impossible jump\n let pos = tracker.estimated_position();\n // should not panic; should clamp or flag anomaly\n assert!(pos.iter().all(|v| v.is_finite()));\n }\n\n #[test]\n fn lost_track_resumes_on_re_detection() {\n let mut tracker = KalmanTracker::new();\n tracker.update([1.0, 1.0, 0.5]);\n // simulate 10 missed frames\n for _ in 0..10 { tracker.predict(); }\n assert_eq!(tracker.state(), TrackState::Lost);\n tracker.update([1.1, 1.1, 0.5]); // re-detected nearby\n assert_eq!(tracker.state(), TrackState::Confirmed);\n }\n}"
|
||||
},
|
||||
{
|
||||
"language": "rust",
|
||||
"code": "// v2/crates/wifi-densepose-ruvector/tests/viewpoint_tests.rs\n\n#[cfg(test)]\nmod attention_tests {\n use wifi_densepose_ruvector::viewpoint::attention::CrossViewpointAttention;\n\n #[test]\n fn attention_weights_sum_to_one() {\n let attn = CrossViewpointAttention::new(3); // 3 viewpoints\n let features = vec![[1.0f32; 64], [2.0f32; 64], [3.0f32; 64]];\n let weights = attn.compute_weights(&features);\n let sum: f32 = weights.iter().sum();\n assert!((sum - 1.0).abs() < 1e-5, \"attention must be a probability distribution\");\n }\n\n #[test]\n fn single_viewpoint_gets_full_weight() {\n let attn = CrossViewpointAttention::new(1);\n let features = vec![[1.0f32; 64]];\n let weights = attn.compute_weights(&features);\n assert!((weights[0] - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn zero_feature_vectors_do_not_produce_nan() {\n let attn = CrossViewpointAttention::new(2);\n let features = vec![[0.0f32; 64], [0.0f32; 64]];\n let weights = attn.compute_weights(&features);\n assert!(weights.iter().all(|w| w.is_finite()));\n }\n}\n\n#[cfg(test)]\nmod sketch_tests {\n use wifi_densepose_ruvector::sketch::WireSketch;\n\n #[test]\n fn round_trip_serialization() {\n let sketch = WireSketch::from_keypoints(&[[0.5f32, 0.5], [0.3, 0.7]]);\n let bytes = sketch.to_bytes();\n let restored = WireSketch::from_bytes(&bytes).unwrap();\n assert_eq!(sketch, restored);\n }\n\n #[test]\n fn deserialize_truncated_bytes_returns_error() {\n let sketch = WireSketch::from_keypoints(&[[0.5f32, 0.5]]);\n let mut bytes = sketch.to_bytes();\n bytes.truncate(bytes.len() / 2); // truncate halfway\n assert!(WireSketch::from_bytes(&bytes).is_err());\n }\n\n #[test]\n fn empty_keypoint_list_is_handled() {\n let sketch = WireSketch::from_keypoints(&[]);\n assert_eq!(sketch.keypoint_count(), 0);\n }\n}"
|
||||
},
|
||||
{
|
||||
"language": "rust",
|
||||
"code": "// v2/crates/wifi-densepose-signal/tests/ruvsense_tests.rs\n\n#[cfg(test)]\nmod coherence_gate_tests {\n use wifi_densepose_signal::ruvsense::coherence_gate::{CoherenceGate, GateDecision};\n\n #[test]\n fn high_coherence_signal_is_accepted() {\n let gate = CoherenceGate::new(0.7); // threshold = 0.7\n let decision = gate.evaluate(0.95);\n assert_eq!(decision, GateDecision::Accept);\n }\n\n #[test]\n fn low_coherence_signal_is_rejected() {\n let gate = CoherenceGate::new(0.7);\n let decision = gate.evaluate(0.3);\n assert_eq!(decision, GateDecision::Reject);\n }\n\n #[test]\n fn borderline_coherence_triggers_recalibrate() {\n let gate = CoherenceGate::new(0.7);\n let decision = gate.evaluate(0.68); // just below threshold\n assert_eq!(decision, GateDecision::Recalibrate);\n }\n}\n\n#[cfg(test)]\nmod phase_align_tests {\n use wifi_densepose_signal::ruvsense::phase_align::PhaseAligner;\n\n #[test]\n fn phase_at_plus_pi_does_not_wrap_incorrectly() {\n let aligner = PhaseAligner::new();\n let phases = vec![std::f32::consts::PI - 0.001, std::f32::consts::PI + 0.001];\n let aligned = aligner.align(&phases);\n // jump across ±π boundary must be handled continuously\n let diff = (aligned[1] - aligned[0]).abs();\n assert!(diff < 0.01, \"phase jump at ±π must be < 0.01 rad after alignment\");\n }\n\n #[test]\n fn single_phase_value_aligns_to_itself() {\n let aligner = PhaseAligner::new();\n let phases = vec![1.5f32];\n let aligned = aligner.align(&phases);\n assert_eq!(aligned.len(), 1);\n assert!((aligned[0] - 1.5).abs() < 1e-6);\n }\n\n #[test]\n fn empty_phase_array_returns_empty() {\n let aligner = PhaseAligner::new();\n let aligned = aligner.align(&[]);\n assert!(aligned.is_empty());\n }\n}\n\n#[cfg(test)]\nmod adversarial_detection_tests {\n use wifi_densepose_signal::ruvsense::adversarial::AdversarialDetector;\n\n #[test]\n fn physically_impossible_amplitude_is_flagged() {\n let detector = AdversarialDetector::new();\n // WiFi amplitude cannot exceed hardware saturation level\n let frame = vec![1e9f32; 56]; // absurdly large\n assert!(detector.is_suspicious(&frame));\n }\n\n #[test]\n fn normal_amplitude_range_passes() {\n let detector = AdversarialDetector::new();\n let frame = vec![0.5f32; 56]; // typical normalized value\n assert!(!detector.is_suspicious(&frame));\n }\n\n #[test]\n fn multi_link_inconsistency_is_detected() {\n // link A reports body moving right; link B reports no motion\n // physically inconsistent — flag as adversarial\n let detector = AdversarialDetector::new();\n let result = detector.check_multi_link_consistency(\n &[1.0, 2.0, 3.0], // link A\n &[0.0, 0.0, 0.0], // link B (no motion)\n );\n assert!(result.is_inconsistent());\n }\n}"
|
||||
},
|
||||
{
|
||||
"language": "rust",
|
||||
"code": "// v2/crates/wifi-densepose-train/tests/test_geometry.rs\n\n#[cfg(test)]\nmod film_layer_tests {\n use wifi_densepose_train::geometry::FilmLayer;\n\n #[test]\n fn film_layer_output_shape_matches_input() {\n let film = FilmLayer::new(64, 32); // 64-dim features, 32-dim condition\n let features = vec![0.5f32; 64];\n let condition = vec![1.0f32; 32];\n let output = film.forward(&features, &condition).unwrap();\n assert_eq!(output.len(), 64, \"FiLM output must match feature dimensionality\");\n }\n\n #[test]\n fn film_layer_zero_condition_acts_as_identity() {\n let film = FilmLayer::new(64, 32);\n let features = vec![1.0f32; 64];\n let zero_condition = vec![0.0f32; 32];\n let output = film.forward(&features, &zero_condition).unwrap();\n // scale=1, shift=0 → identity; output ≈ input\n for (o, f) in output.iter().zip(features.iter()) {\n assert!((o - f).abs() < 0.1, \"zero condition should approximate identity\");\n }\n }\n}\n\n// v2/crates/wifi-densepose-train/tests/test_rapid_adapt.rs\n\n#[cfg(test)]\nmod rapid_adaptation_tests {\n use wifi_densepose_train::rapid_adapt::RapidAdapter;\n\n #[test]\n fn adapter_updates_on_single_sample() {\n let mut adapter = RapidAdapter::new(5); // 5 adaptation steps\n let csi_sample = vec![0.1f32; 56 * 3];\n let pose_label = vec![0.5f32; 17 * 2]; // 17 keypoints × (x, y)\n let result = adapter.adapt_step(&csi_sample, &pose_label);\n assert!(result.is_ok());\n }\n\n #[test]\n fn adapter_with_zero_steps_is_no_op() {\n let adapter = RapidAdapter::new(0);\n // 0 adaptation steps → weights unchanged\n let initial_weights = adapter.clone_weights();\n let _ = adapter.adapt_step(&vec![0.1f32; 168], &vec![0.5f32; 34]);\n assert_eq!(adapter.clone_weights(), initial_weights);\n }\n}"
|
||||
},
|
||||
{
|
||||
"language": "rust",
|
||||
"code": "// v2/crates/wifi-densepose-sensing-server/tests/auth_tests.rs\n\n#[cfg(test)]\nmod bearer_auth_tests {\n use wifi_densepose_sensing_server::auth::{BearerValidator, TokenError};\n\n #[test]\n fn missing_authorization_header_returns_unauthorized() {\n let validator = BearerValidator::new(\"secret-token\");\n let result = validator.validate(None);\n assert!(matches!(result, Err(TokenError::Missing)));\n }\n\n #[test]\n fn wrong_token_is_rejected() {\n let validator = BearerValidator::new(\"correct-token\");\n let result = validator.validate(Some(\"Bearer wrong-token\"));\n assert!(matches!(result, Err(TokenError::Invalid)));\n }\n\n #[test]\n fn malformed_header_without_bearer_prefix_is_rejected() {\n let validator = BearerValidator::new(\"token\");\n let result = validator.validate(Some(\"token\")); // missing \"Bearer \" prefix\n assert!(matches!(result, Err(TokenError::Malformed)));\n }\n\n #[test]\n fn correct_token_is_accepted() {\n let validator = BearerValidator::new(\"correct-token\");\n let result = validator.validate(Some(\"Bearer correct-token\"));\n assert!(result.is_ok());\n }\n}\n\n// v2/crates/wifi-densepose-sensing-server/tests/semantic_tests.rs\n\n#[cfg(test)]\nmod fall_detection_tests {\n use wifi_densepose_sensing_server::semantic::fall_detector::FallDetector;\n\n #[test]\n fn no_motion_does_not_trigger_fall() {\n let mut detector = FallDetector::new();\n for _ in 0..30 { // 30 frames of stillness\n detector.update_pose(stationary_pose());\n }\n assert!(!detector.fall_detected());\n }\n\n #[test]\n fn rapid_downward_velocity_triggers_fall() {\n let mut detector = FallDetector::new();\n // simulate person going from standing (y=1.7m) to prone (y=0.3m) in 3 frames\n for (frame, y) in [(0, 1.7f32), (1, 1.0), (2, 0.3)] {\n detector.update_pose(pose_at_height(y));\n }\n assert!(detector.fall_detected());\n }\n\n #[test]\n fn sitting_down_slowly_does_not_trigger_fall() {\n let mut detector = FallDetector::new();\n // gradual height decrease over 30 frames is sitting, not falling\n for i in 0..30 {\n let y = 1.7f32 - (i as f32 * 0.04); // ~1.2m drop over 30 frames\n detector.update_pose(pose_at_height(y));\n }\n assert!(!detector.fall_detected());\n }\n}"
|
||||
}
|
||||
]
|
||||
},
|
||||
"rawOutputPreview": "The exploration is complete. Here's the full coverage analysis with test skeletons for each critical gap.\n\n---\n\n## Test Coverage Gap Analysis — wifi-densepose\n\n### Coverage Summary by Crate\n\n| Crate | Tests Found | Status | Priority |\n|-------|-------------|--------|----------|\n| `wifi-densepose-core` | 26 inline | Good | Low |\n| `wifi-densepose-signal` | ~60 (validation only) | Moderate | **High** |\n| `wifi-densepose-nn` | **0** | Critical | **P1** |\n| `wifi-densepose-train` | ~60 (config/dataset) | Moderate | High |\n| `wifi-densepose-mat` | 1 integration test | Critical | **P1** |\n| `wifi-densepose-ruvector` | **0** | Critical | **P1** |\n| `wifi-densepose-sensing-server` | 4 integration tests | Moderate | High |\n| `wifi-densepose-wasm` | 3 compliance tests | Low | Low |\n\n---\n\n## Tier 1: Critical Gaps\n\n### 1. `wifi-densepose-nn` — Zero test coverage\n\nEvery public API is untested. Place these at `v2/crates/wifi-densepose-nn/tests/inference_tests.rs`:\n\n```rust\n// v2/crates/wifi-densepose-nn/tests/inference_tests.rs\n\n#[cfg(test)]\nmod tensor_tests {\n use wifi_densepose_nn::tensor::Tensor;\n\n #[test]\n fn tensor_shape_mismatch_returns_error() {\n // data has 6 elements but shape claims 3×3=9\n let result = Tensor::new(vec![1.0f32; 6], &[3, 3]);\n assert!(result.is_err(), \"shape mismatch must be rejected\");\n }\n\n #[test]\n fn tensor_empty_data_returns_error() {\n let result = Tensor::new(vec![], &[0]);\n assert!(result.is_err());\n }\n\n #[test]\n fn tensor_nan_values_are_detected() {\n let t = Tensor::new(vec![f32::NAN, 1.0, 2.0], &[3]).unwrap();\n assert!(t.has_nan(), \"NaN in data must be detectable\");\n }\n\n #[test]\n fn tensor_inf_values_are_detected() {\n let t = Tensor::new(vec![f32::INFINITY, 1.0], &[2]).unwrap();\n assert!(t.has_inf());\n }\n}\n\n#[cfg(test)]\nmod modality_translator_tests {\n use wifi_densepose_nn::translator::ModalityTranslator;\n\n #[test]\n fn translator_rejects",
|
||||
"rawOutputLength": 18269
|
||||
}
|
||||
Reference in New Issue
Block a user