Architectural Invariant Design: Capacitor 7 WebAuthn PRF Hardware Key Derivation Invariant
Capacitor 7 WebAuthn PRF Hardware Key Derivation Invariant defines an industrial-grade behavioral standard for mobile engineering. Enforces hardware-backed zero-knowledge key derivation using the WebAuthn Pseudo-Random Function (PRF) extension within Capacitor 7 native bridges. Ensures derived symmetric encryption keys are computed in native secure enclaves/Keystore without exposing long-lived master secrets, credential IDs, or raw entropy directly to the untrusted WebView JavaScript context. Modern software architectures require strict adherence to deterministic patterns to guarantee high availability, maintainability, and zero security vulnerabilities.
Hazard Analysis: What Breaks Without This Rule
The following failure modes have been cataloged from hundreds of hours of production code reviews where AI assistants produced non-compliant implementations:
| Failure Mode | AI Symptom (The Anti-Pattern) | Severity | Production Impact & Remedy |
|---|---|---|---|
| AI Hallucination & Deprecation Drift | Deriving master encryption keys in WebView JavaScript or caching PRF raw outputs in unencrypted storage | Critical | Violating this industrial standard introduces regression bugs, security risks, and unexpected runtime failures in production. Enforce explicit behavioral system directives across all AI coding assistants in your repository. |
| Silent Architecture Inconsistency | AI assistants produce mixed paradigms across files, creating fragmented codebase conventions. | Medium | Increases cognitive load during code reviews and leads to maintenance debt across engineering teams. Pin project conventions in .cursor/rules, CLAUDE.md, and AGENTS.md so all team agents follow identical guardrails. |
Step-by-Step Production Directives
Each rule constraint below is injected into the AI agent's system prompt to establish deterministic boundaries:
Engineers MUST ALWAYS verify that the WebAuthn authenticator supports the 'prf' extension during registration (navigator.credentials.create) and authentication (navigator.credentials.get).
Engineers MUST ALWAYS verify that the WebAuthn authenticator supports the 'prf' extension during registration (navigator.credentials.create) and authentication (navigator.credentials.get).
Engineers MUST NEVER persist the raw PRF output (results.prf) to localStorage, IndexedDB, or any client-side JavaScript storage.
Engineers MUST NEVER persist the raw PRF output (results.prf) to localStorage, IndexedDB, or any client-side JavaScript storage.
Engineers MUST derive ephemeral operational keys using HKDF (HMAC-based Extract-and-Expand Key Derivation Function) with high-entropy native salts and explicit context strings.
Engineers MUST derive ephemeral operational keys using HKDF (HMAC-based Extract-and-Expand Key Derivation Function) with high-entropy native salts and explicit context strings.
Services MUST clear sensitive TypedArrays from memory (ArrayBuffer/Uint8Array zeroing) immediately after completing the cryptographic operation in the WebView.
Services MUST clear sensitive TypedArrays from memory (ArrayBuffer/Uint8Array zeroing) immediately after completing the cryptographic operation in the WebView.
Native plugins MUST route PRF evaluation requests through platform-native biometrics (Credential Manager on Android 14+ / ASAuthorizationPlatformPublicKeyCredentialProvider on iOS 17+) when using hybrid plugins.
Native plugins MUST route PRF evaluation requests through platform-native biometrics (Credential Manager on Android 14+ / ASAuthorizationPlatformPublicKeyCredentialProvider on iOS 17+) when using hybrid plugins.
Frequently Asked Technical Questions
Common questions developers face when configuring AI coding assistants for this stack:
Q:Why is Capacitor 7 WebAuthn PRF Hardware Key Derivation Invariant critical for AI coding assistants?
Enforces hardware-backed zero-knowledge key derivation using the WebAuthn Pseudo-Random Function (PRF) extension within Capacitor 7 native bridges. Ensures derived symmetric encryption keys are computed in native secure enclaves/Keystore without exposing long-lived master secrets, credential IDs, or raw entropy directly to the untrusted WebView JavaScript context. Without explicit behavioral boundaries, AI coding assistants (Cursor, Claude Code, Windsurf, Codex) rely on training data averages, which frequently produce deprecated, inefficient, or vulnerable code patterns.
Q:How does adopting this rule prevent AI hallucinations?
This rule explicitly declares what the AI must avoid (Deriving master encryption keys in WebView JavaScript or caching PRF raw outputs in unencrypted storage) and prescribes verified positive examples. Grounding the LLM with negative and positive pairs eliminates guesswork.
Q:Which AI coding environments are supported?
This specification is multi-platform compatible: Cursor (.cursor/rules/*.mdc & .cursorrules), Anthropic Claude Code (CLAUDE.md), Windsurf (.windsurfrules), Codex / Cline (AGENTS.md), and GitHub Copilot (.github/copilot-instructions.md).
Q:Can I combine this rule with other technology stacks?
Yes. Use the Interactive Stack Builder or download an Agent Pack to merge this rule with other frontend, backend, database, security, and DevOps rules without syntax collision.