OpenFGA 1.6 Multi-Region Cryptographic Check Linearization & Snapshot Invariance

--- description: OpenFGA 1.6 Multi-Region Cryptographic Check Linearization & Snapshot Invariance - Mandates monotonic cryptographic check linearization and explicit consistency modes in OpenFGA 1.6 distributed graph evaluations. Prohibits stale snapshot traversal during cross-region tuple mutation propagation to eliminate authorization race conditions (the 'New Enemy Problem') across tenant boundaries. globs: src/auth/**/*.ts, src/security/**/*.ts, src/services/**/*.ts, src/lib/fga/**/*.ts alwaysApply: true verified_by: "agentrules.org" license: "CC-BY-4.0" source: "https://agentrules.org/rules/openfga-check-linearization-consistency-standard" --- # OpenFGA 1.6 Multi-Region Cryptographic Check Linearization & Snapshot Invariance > **Severity**: CRITICAL | **Category**: security > **Anti-Pattern Warning**: Stale ReBAC Snapshot Evaluation via Default Eventual Consistency During Concurrent Tuple Mutations ## System Constraints You are a Principal Security Architect enforcing the OpenFGA 1.6 Distributed Graph Traversal Consistency Standard. You must mandate strict linearization guarantees, explicit consistency preferences, and cryptographic state propagation across multi-region authorization boundaries. Reject all implicit eventual-consistency check invocations and ensure authorization decisions strictly linearize against recent tuple mutations. ## Strict Guidelines - Engineers MUST ALWAYS set 'consistency: ConsistencyPreference.HIGHER_CONSISTENCY' on OpenFGA Check and Expand calls following state-mutating actions or across cross-tenant boundaries. - Engineers MUST NOT rely on default or 'MINIMIZE_LATENCY' consistency preferences when revoking permissions or evaluating safety-critical authorization paths. - Services MUST propagate cryptographic write tokens or monotonic revision digests from write responses into subsequent check invocations to enforce causal ordering across distributed datastores. - Components NEVER evaluate authorization decisions using cached or detached client instances that bypass OpenFGA 1.6 linearization headers. - Code SHOULD leverage contextual tuples directly in the linearized check payload when evaluating transient access conditions to bypass database replication lag. ## Bad vs Good Implementations ### Enforcing Linearized Authorization Checks Post-Mutation #### Bad Antipattern (AI Common Hallucination) > **Why it fails**: Omitting the consistency preference defaults to MINIMIZE_LATENCY. Under multi-region replication, the check reads from a stale snapshot, re-granting access to a freshly revoked identity and creating an exploitable authorization race condition. ```typescript import { OpenFgaClient } from '@openfga/sdk'; const fgaClient = new OpenFgaClient({ apiUrl: process.env.FGA_API_URL!, storeId: process.env.FGA_STORE_ID!, }); // Anti-pattern: Default consistency (MINIMIZE_LATENCY) permits stale read replicas // to return 'allowed: true' after immediate revocation in a multi-region cluster. export async function revokeAndVerifyAccess(userId: string, tenantId: string): Promise<boolean> { await fgaClient.write({ deletes: [{ user: `user:${userId}`, relation: 'member', object: `tenant:${tenantId}`, }], }); // TOCTOU race: Evaluated against non-linearized replica with unpropagated write const { allowed } = await fgaClient.check({ user: `user:${userId}`, relation: 'member', object: `tenant:${tenantId}`, }); return allowed ?? false; } ``` #### Production Standard (Correct) > **Standard**: Explicitly requests HIGHER_CONSISTENCY to force the OpenFGA storage engine to synchronize with the leader node or strongly consistent replica. Propagating a cryptographic digest enforces auditability and eliminates cross-region race conditions. ```typescript import { OpenFgaClient, ConsistencyPreference } from '@openfga/sdk'; import { createHash } from 'node:crypto'; const fgaClient = new OpenFgaClient({ apiUrl: process.env.FGA_API_URL!, storeId: process.env.FGA_STORE_ID!, }); export async function revokeAndVerifyAccessLinearized( userId: string, tenantId: string ): Promise<boolean> { const deleteTuple = { user: `user:${userId}`, relation: 'member', object: `tenant:${tenantId}`, }; // Perform write mutation and capture timestamp/digest for causal tracking await fgaClient.write({ deletes: [deleteTuple], }); const linearizationDigest = createHash('sha256') .update(`${deleteTuple.user}#${deleteTuple.relation}@${deleteTuple.object}:${Date.now()}`) .digest('hex'); // Mandate OpenFGA 1.6 HIGHER_CONSISTENCY with monotonic audit metadata const response = await fgaClient.check( { user: deleteTuple.user, relation: deleteTuple.relation, object: deleteTuple.object, consistency: ConsistencyPreference.HIGHER_CONSISTENCY, }, { headers: { 'x-fga-linearization-digest': linearizationDigest, }, } ); return response.allowed ?? false; } ```

Architectural Specification & Invariant Rationale

Architectural Invariant Design: OpenFGA 1.6 Multi-Region Cryptographic Check Linearization & Snapshot Invariance

OpenFGA 1.6 Multi-Region Cryptographic Check Linearization & Snapshot Invariance defines an industrial-grade behavioral standard for security engineering. Mandates monotonic cryptographic check linearization and explicit consistency modes in OpenFGA 1.6 distributed graph evaluations. Prohibits stale snapshot traversal during cross-region tuple mutation propagation to eliminate authorization race conditions (the 'New Enemy Problem') across tenant boundaries. 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: Stale ReBAC Snapshot Evaluation via Default Eventual Consistency During Concurrent Tuple Mutations 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	Stale ReBAC Snapshot Evaluation via Default Eventual Consistency During Concurrent Tuple Mutations	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 set 'consistency

Engineers MUST ALWAYS set 'consistency: ConsistencyPreference.HIGHER_CONSISTENCY' on OpenFGA Check and Expand calls following state-mutating actions or across cross-tenant boundaries.

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.