Zod Schema Validation & Strict Sanitization

--- description: Zod Schema Validation & Strict Sanitization - Bulletproof data validation using Zod: enforce safeParse, handle type coercion safely, sanitize strings, and prevent prototype pollution. globs: **/*.ts, **/*.tsx alwaysApply: true verified_by: "agentrules.org" license: "CC-BY-4.0" source: "https://agentrules.org/rules/zod-v3-validation" --- # Zod Schema Validation & Strict Sanitization > **Severity**: CRITICAL | **Category**: frontend > **Anti-Pattern Warning**: AI uses .parse() directly causing unhandled runtime crashes, or relies on unsafe type assertions instead of safeParse. ## System Constraints You are a data validation security engineer. Always use Zod schemas at system boundaries (forms, API responses, query params). Never use schema.parse() directly on untrusted user input; use schema.safeParse() and handle failure cleanly. ## Strict Guidelines - Use safeParse() at application boundaries to handle errors gracefully without unhandled throws. - Infer TypeScript types directly from schemas using z.infer<typeof Schema> to maintain single source of truth. - Avoid z.coerce on raw strings without boundary validation, as unexpected inputs can coerce to NaN or invalid dates. ## Bad vs Good Implementations ### Safe Parsing with Error Formatting #### Bad Antipattern (AI Common Hallucination) > **Why it fails**: .parse() throws an unhandled exception that can crash edge functions or return unformatted 500 errors to clients. ```typescript // Anti-pattern: Unhandled throw on bad data export function handleApi(reqBody: unknown) { const user = UserSchema.parse(reqBody); // Crashes API if invalid! return saveUser(user); } ``` #### Production Standard (Correct) > **Standard**: safeParse safely returns a discriminated result union, allowing user-friendly error response mapping. ```typescript // Production Standard: safeParse with formatted errors export function handleApi(reqBody: unknown) { const result = UserSchema.safeParse(reqBody); if (!result.success) { return { status: 400, errors: result.error.flatten().fieldErrors, }; } return saveUser(result.data); } ```

Architectural Specification & Invariant Rationale

Architectural Invariant Design: Zod Schema Validation & Strict Sanitization

Zod Schema Validation & Strict Sanitization defines an industrial-grade behavioral standard for frontend engineering. Bulletproof data validation using Zod: enforce safeParse, handle type coercion safely, sanitize strings, and prevent prototype pollution. 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: AI uses .parse() directly causing unhandled runtime crashes, or relies on unsafe type assertions instead of safeParse. 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	AI uses .parse() directly causing unhandled runtime crashes, or relies on unsafe type assertions instead of safeParse.	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:

Use safeParse() at application boundaries to handle errors gracefully without unhandled throws.

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.

Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.

Infer TypeScript types directly from schemas using z.infer<typeof Schema> to maintain single source of truth.

Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.

Avoid z.coerce on raw strings without boundary validation, as unexpected inputs can coerce to NaN or invalid dates.

Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.

Technical Questions & Answers

Frequently Asked Technical Questions

Common questions developers face when configuring AI coding assistants for this stack:

Q:Why is Zod Schema Validation & Strict Sanitization critical for AI coding assistants?

Bulletproof data validation using Zod: enforce safeParse, handle type coercion safely, sanitize strings, and prevent prototype pollution. 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 (AI uses .parse() directly causing unhandled runtime crashes, or relies on unsafe type assertions instead of safeParse.) 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.