Files
ruvnet--RuView/docs/adr
rUv 5bc3b634b7 fix(automation security): template-bomb DoS (100MB/11s render → fuel-bounded, HIGH) + delay panic-on-config (MEDIUM) (#1083)
* fix(homecore-automation): bound template render to stop unbounded-expansion DoS (HC-SEC-01)

A `template:` condition / value_template comes straight from user
automation config and was rendered with MiniJinja's default (no
instruction budget, no output cap). A single condition such as
`{% for i in range(5000) %}{% for j in range(5000) %}xxxx{% endfor %}{% endfor %}`
rendered a 100 MB string over ~11 s on one render call (proven
empirically) — a CPU/memory denial of service, the bfld-class
"unbounded expansion".

Fix:
- Enable MiniJinja's `fuel` feature and set a per-render instruction
  budget (`set_fuel(Some(1_000_000))`). A nested loop burns one unit
  per iteration, so the budget caps total work regardless of nesting;
  the attack now fails fast (~90 ms) with "engine ran out of fuel".
- Reject template sources over 64 KiB before compilation (defense in
  depth so a pathological literal can neither compile nor emit verbatim).

Legitimate HA templates (a few dozen instructions) are unaffected.

Tests (fail on old — unbounded render / no rejection):
- nested_loop_template_is_bounded_not_unbounded_dos
- single_huge_repeat_template_is_bounded
- oversized_template_source_is_rejected
- legitimate_template_still_renders_within_fuel (no regression)

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(homecore-automation): stop crafted delay/timeout from panicking the run task (HC-SEC-02)

`Action::Delay { seconds }` and `Action::WaitForTrigger { timeout_seconds }`
fed the user-supplied float straight into `Duration::from_secs_f64`, which
PANICS on negative, NaN, infinite, or overflowing inputs. All of those are
reachable from a crafted (or simply typo'd) automation YAML —
`delay: {seconds: -1}`, `.nan`, `.inf`, `1e308` — so one hostile config
aborts the spawned automation task with a panic
("cannot convert float seconds to Duration: value is negative", proven
empirically).

Fix: a `safe_duration_from_secs` guard that saturates instead of panicking,
matching Home Assistant's lenient "non-positive delay = no delay":
- NaN / ±inf / negative -> Duration::ZERO
- absurdly large (would overflow) -> clamped to ~100 years (MAX_DELAY_SECS)

Tests (fail on old — panic = failure):
- delay_negative_seconds_does_not_panic
- delay_nan_seconds_does_not_panic
- delay_infinite_seconds_does_not_panic
- wait_for_trigger_negative_timeout_does_not_panic
- safe_duration_saturates_hostile_values (incl. overflow clamp)

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs(homecore-automation): record HC-SEC-01/02 security review (CHANGELOG + ADR-129 §8a)

Document the two DoS findings (template unbounded-expansion HC-SEC-01,
delay panic-on-config HC-SEC-02) and the dimensions probed clean
(condition fail-closed, bounded run-modes, sandboxed read-only templates).

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-06-14 20:22:07 -04:00
..

Architecture Decision Records

This folder contains 45 Architecture Decision Records (ADRs) that document every significant technical choice in the RuView / WiFi-DensePose project.

Why ADRs?

Building a system that turns WiFi signals into human pose estimation involves hundreds of non-obvious decisions: which signal processing algorithms to use, how to bridge ESP32 firmware to a Rust pipeline, whether to run inference on-device or on a server, how to handle multi-person separation with limited subcarriers.

ADRs capture the context, options considered, decision made, and consequences for each of these choices. They serve three purposes:

  1. Institutional memory — Six months from now, anyone (human or AI) can read why we chose IIR bandpass filters over FIR for vital sign extraction, not just see the code.

  2. AI-assisted development — When an AI agent works on this codebase, ADRs give it the constraints and rationale it needs to make changes that align with the existing architecture. Without them, AI-generated code tends to drift — reinventing patterns that already exist, contradicting earlier decisions, or optimizing for the wrong tradeoffs.

  3. Review checkpoints — Each ADR is a reviewable artifact. When a proposed change touches the architecture, the ADR forces the author to articulate tradeoffs before writing code, not after.

ADRs and Domain-Driven Design

The project uses Domain-Driven Design (DDD) to organize code into bounded contexts — each with its own language, types, and responsibilities. ADRs and DDD work together:

  • ADRs define boundaries: ADR-029 (RuvSense) established multistatic sensing as a separate bounded context from single-node CSI. ADR-042 (CHCI) defined a new aggregate root for coherent channel imaging.
  • DDD models define the language: The RuvSense domain model defines terms like "coherence gate", "dwell time", and "TDM slot" that ADRs reference precisely.
  • Together they prevent drift: An AI agent reading ADR-039 knows that edge processing tiers are configured via NVS keys, not compile-time flags — because the ADR says so. The DDD model tells it which aggregate owns that configuration.

How ADRs are structured

Each ADR follows a consistent format:

  • Context — What problem or gap prompted this decision
  • Decision — What we chose to do and how
  • Consequences — What improved, what got harder, and what risks remain
  • References — Related ADRs, papers, and code paths

Statuses: Proposed (under discussion), Accepted (approved and/or implemented), Superseded (replaced by a later ADR).


ADR Index

Hardware and firmware

ADR Title Status
ADR-012 ESP32 CSI Sensor Mesh for Distributed Sensing Accepted (partial)
ADR-018 ESP32 Development Implementation Path Proposed
ADR-028 ESP32 Capability Audit and Witness Record Accepted
ADR-029 RuvSense Multistatic Sensing Mode (TDM, channel hopping) Proposed
ADR-032 Multistatic Mesh Security Hardening Accepted
ADR-039 ESP32-S3 Edge Intelligence Pipeline (on-device vitals) Accepted (hardware-validated)
ADR-040 WASM Programmable Sensing (Tier 3) Accepted
ADR-041 WASM Module Collection (65 edge modules) Accepted (hardware-validated)
ADR-044 Provisioning Tool Enhancements Proposed
ADR-110 ESP32-C6 firmware extension — Wi-Fi 6 / 802.15.4 / TWT / LP-core Accepted, P1-P10 complete, firmware-side substrate closed at v0.7.0-esp32. Companion docs: WITNESS-LOG-110 (13 §A0.x entries · 99.56 % cross-board RX · 104.1 µs smoothed sync stdev · ≤100 µs target met), ADR-110-REVIEW-GUIDE (one-page reviewer tour), ADR-110-BRANCH-STATE (coordination map vs feat/adr-115-ha-mqtt-matter). Host decoders + tests: Python SyncPacketParser (10) + Rust wifi_densepose_hardware::SyncPacket (15), cross-language hex pin gates drift.

Signal processing and sensing

ADR Title Status
ADR-013 Feature-Level Sensing on Commodity Gear Accepted
ADR-014 SOTA Signal Processing Algorithms Accepted
ADR-021 Vital Sign Detection (breathing, heart rate) Partial
ADR-030 Persistent Field Model and Drift Detection Proposed
ADR-033 CRV Signal Line Sensing Integration Proposed
ADR-037 Multi-Person Pose Detection from Single ESP32 Proposed
ADR-042 Coherent Human Channel Imaging (beyond CSI) Proposed
ADR-134 First-Class Channel Impulse Response (CIR) Support Proposed
ADR-135 Empty-Room Baseline Calibration (per-subcarrier Welford statistics) Proposed

Machine learning and training

ADR Title Status
ADR-005 SONA Self-Learning for Pose Estimation Partial
ADR-006 GNN-Enhanced CSI Pattern Recognition Partial
ADR-015 Public Dataset Strategy (MM-Fi, Wi-Pose) Accepted
ADR-016 RuVector Training Pipeline Integration Accepted
ADR-017 RuVector Signal + MAT Integration Proposed
ADR-020 Migrate AI Inference to Rust (ONNX Runtime) Accepted
ADR-023 Trained DensePose Model with RuVector Pipeline Proposed
ADR-024 Project AETHER: Contrastive CSI Embeddings Required
ADR-027 Project MERIDIAN: Cross-Environment Generalization Proposed
ADR-149 AetherArena: public spatial-intelligence benchmark on Hugging Face Proposed
ADR-150 RF Foundation Encoder: pose-preserving, subject/room/device-invariant CSI embedding Proposed
ADR-151 Per-Room Calibration & Specialized Model Training (room-first → bank of small ruVector specialists) Proposed
ADR-152 WiFi-Pose SOTA 2026 Intake: geometry-conditioned calibration, external benchmarks, foundation-encoder recipe Proposed

Platform and UI

ADR Title Status
ADR-019 Sensing-Only UI with Gaussian Splats Accepted
ADR-022 Windows WiFi Enhanced Fidelity (multi-BSSID) Partial
ADR-025 macOS CoreWLAN WiFi Sensing Proposed
ADR-031 RuView Sensing-First RF Mode Proposed
ADR-034 Expo React Native Mobile App Accepted
ADR-035 Live Sensing UI Accuracy and Data Transparency Accepted
ADR-036 Training Pipeline UI Integration Proposed
ADR-043 Sensing Server UI API Completion (14 endpoints) Accepted
ADR-115 Home Assistant integration via MQTT auto-discovery + Matter bridge (HA-DISCO + HA-FABRIC + HA-MIND) Accepted (MQTT track) / Proposed (Matter SDK P8b)
ADR-169 adam-mode — light theme toggle for the three.js realtime demo Proposed
ADR-170 yoga-mode — yoga pose detection, classification, and scoring for the three.js realtime demo Proposed

Architecture and infrastructure

ADR Title Status
ADR-001 WiFi-Mat Disaster Detection Architecture Accepted
ADR-002 RuVector RVF Integration Strategy Superseded
ADR-003 RVF Cognitive Containers for CSI Proposed
ADR-004 HNSW Vector Search for Fingerprinting Partial
ADR-007 Post-Quantum Cryptography for Sensing Proposed
ADR-008 Distributed Consensus for Multi-AP Proposed
ADR-009 RVF WASM Runtime for Edge Deployment Proposed
ADR-010 Witness Chains for Audit Trail Integrity Proposed
ADR-011 Proof-of-Reality and Mock Elimination Proposed
ADR-026 Survivor Track Lifecycle (MAT crate) Accepted
ADR-038 Sublinear GOAP for Roadmap Optimization Proposed
ADR-095 rvCSI — Edge RF Sensing Runtime Platform Proposed
ADR-096 rvCSI — Crate Topology, the napi-c Shim, and the napi-rs Node Surface Proposed
ADR-097 Adopt rvCSI as RuView's primary CSI runtime (phased adoption) Proposed
ADR-098 Evaluate ruvnet/midstream for RuView's CSI / WebSocket / mesh pipeline Rejected
ADR-099 Adopt midstream as RuView's real-time introspection + low-latency tap Proposed

  • DDD Domain Models — Bounded context definitions, aggregate roots, and ubiquitous language
  • User Guide — Setup, API reference, and hardware instructions
  • Build Guide — Building from source