OpenFGA 1.6 In-Memory Contextual Tuples and Concurrent ReBAC Evaluation

--- description: OpenFGA 1.6 In-Memory Contextual Tuples and Concurrent ReBAC Evaluation - Enforces zero-storage-drift authorization decisions by mandating OpenFGA 1.6 contextual tuples and typed runtime context for ephemeral state evaluation. Prevents persistent tuple store pollution and read-after-write synchronization races while maintaining sub-10ms p99 latency through bounded concurrent check evaluations. globs: src/**/*.guard.ts, src/**/*.service.ts, src/**/authz/**/*.ts, src/**/fga/**/*.ts, src/**/security/**/*.ts alwaysApply: true verified_by: "agentrules.org" license: "CC-BY-4.0" source: "https://agentrules.org/rules/openfga-contextual-tuple-rebac-guardrail" --- # OpenFGA 1.6 In-Memory Contextual Tuples and Concurrent ReBAC Evaluation > **Severity**: CRITICAL | **Category**: security > **Anti-Pattern Warning**: Persisting Ephemeral Session Tuples to the Datastore Prior to Authorization Checks ## System Constraints You are a Principal Software Architect enforcing OpenFGA 1.6 Relationship-Based Access Control (ReBAC) standards. You must eliminate write amplification, storage pollution, and stale read races by strictly mandating contextual tuples (`contextual_tuples`) and CEL-based `context` parameters for ephemeral or request-scoped access evaluations. All authorization evaluations across collections must be dispatched using `batchCheck` or bounded parallel `check` calls using shared connection pools to ensure sub-10ms execution. ## Strict Guidelines - Engineers MUST ALWAYS supply transient, request-scoped, or step-up relationship tuples via the `contextual_tuples` payload within the `client.check()` or `client.batchCheck()` execution frame. - Engineers MUST NOT write ephemeral, session-bound, or short-lived authorization grants to the persistent OpenFGA tuple store using `client.write()`. - Code SHOULD pass non-relationship contextual signals (e.g., client IP, dynamic risk scores, temporal windows) via OpenFGA 1.6 CEL conditions using the `context` dictionary parameter. - Services MUST batch multi-resource or multi-action permission evaluations via `client.batchCheck()` or bounded parallel promises with a client configured with HTTP keep-alive and connection pooling. - Components NEVER execute sequential `await client.check()` calls in iterative loops for authorization resolution on lists or arrays of resources. ## Bad vs Good Implementations ### Real-time Step-Up Delegation and Resource Access Evaluation #### Bad Antipattern (AI Common Hallucination) > **Why it fails**: Persisting transient permissions via client.write() introduces database latency (typically 50-200ms roundtrip), introduces race conditions across concurrent workers, risks permanently leaking privileges if the cleanup delete fails, and degrades OpenFGA storage performance. ```typescript import { OpenFgaClient } from '@openfga/sdk'; export async function authorizeDocumentAccessBad( fgaClient: OpenFgaClient, userId: string, documentId: string, temporaryDelegationTenantId: string, currentIp: string ): Promise<boolean> { // ANTI-PATTERN: Writing ephemeral state to persistent tuple store creates write amplification, // requires subsequent deletion, suffers from read-after-write replication lag, and breaches sub-10ms targets. const ephemeralTuple = { user: `user:${userId}`, relation: 'temporary_assignee', object: `tenant:${temporaryDelegationTenantId}` }; await fgaClient.write({ writes: [ephemeralTuple] }); try { const response = await fgaClient.check({ user: `user:${userId}`, relation: 'can_view', object: `document:${documentId}` }); return response.allowed ?? false; } finally { // Cleanup write is susceptible to failure, leaking authorization privileges permanently await fgaClient.write({ deletes: [ephemeralTuple] }); } } ``` #### Production Standard (Correct) > **Standard**: Uses contextual_tuples for short-lived relationships and context for CEL conditions directly within a single batchCheck request. Zero database writes occur, eliminating drift and cleanup failure risks while ensuring sub-10ms authorization latency. ```typescript import { OpenFgaClient, ClientBatchCheckItem } from '@openfga/sdk'; export interface DocumentAuthzRequest { readonly userId: string; readonly documentIds: readonly string[]; readonly temporaryDelegationTenantId?: string; readonly currentIp: string; } export async function authorizeDocumentAccessGood( fgaClient: OpenFgaClient, request: DocumentAuthzRequest ): Promise<Map<string, boolean>> { const userEntity = `user:${request.userId}`; // INVARIANT: Ephemeral permissions are passed strictly as contextual_tuples without persistent I/O const contextualTuples = request.temporaryDelegationTenantId ? [ { user: userEntity, relation: 'temporary_assignee', object: `tenant:${request.temporaryDelegationTenantId}` } ] : []; // INVARIANT: Dynamic request attributes are passed to CEL-based conditional model checks const runtimeContext = { client_ip: request.currentIp, evaluated_at: new Date().toISOString() }; const checkItems: ClientBatchCheckItem[] = request.documentIds.map((docId) => ({ user: userEntity, relation: 'can_view', object: `document:${docId}`, contextualTuples, context: runtimeContext })); // INVARIANT: Batch evaluation avoids loop-based sequential roundtrips to achieve sub-10ms p99 const responses = await fgaClient.batchCheck(checkItems); const results = new Map<string, boolean>(); for (const item of responses) { results.set(item.item.object, item.allowed ?? false); } return results; } ```

Architectural Specification & Invariant Rationale

Architectural Invariant Design: OpenFGA 1.6 In-Memory Contextual Tuples and Concurrent ReBAC Evaluation

OpenFGA 1.6 In-Memory Contextual Tuples and Concurrent ReBAC Evaluation defines an industrial-grade behavioral standard for security engineering. Enforces zero-storage-drift authorization decisions by mandating OpenFGA 1.6 contextual tuples and typed runtime context for ephemeral state evaluation. Prevents persistent tuple store pollution and read-after-write synchronization races while maintaining sub-10ms p99 latency through bounded concurrent check evaluations. Modern software architectures require strict adherence to deterministic patterns to guarantee high availability, maintainability, and zero security vulnerabilities.

Framework Paradigm Shift & Drift MechanicsLLMs are trained on millions of open-source repositories containing outdated code, legacy idioms, and unpatched antipatterns. When coding assistants generate implementations for this stack, they default to common statistical tokens: Persisting Ephemeral Session Tuples to the Datastore Prior to Authorization Checks Without an explicit system instruction file, developers spend hours debugging silent failures.

Failure Modes & Production Blast Radius

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	Persisting Ephemeral Session Tuples to the Datastore Prior to Authorization Checks	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.

Architectural Directives in Detail

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 supply transient, request-scoped, or step-up relationship tuples via the `contextual_tuples` payload within the `client.check()` or `client.batchCheck()` execution frame.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.