Structured Output Enforcement

The core mechanism that restricts token generation to only those valid within a target grammar. Logit masking sets the probability of invalid tokens to negative infinity, ensuring the model cannot deviate from the required structure.

• JSON Schema: Defines required keys, types, and nesting
• Regular Expressions: Enforces patterns like dates or IDs
• Context-Free Grammars (CFG): Handles complex nested structures

This guarantees that even a fully compromised prompt can only produce a valid JSON object, not executable commands or exfiltrated data.

A post-generation security layer that validates the model's output against the expected schema before it reaches downstream systems. If the output fails validation, the gate can trigger a retry, return a safe default, or log an anomaly.

• Type checking: Ensures fields are strings, integers, or booleans as expected
• Range enforcement: Validates numeric fields fall within acceptable bounds
• Enum whitelisting: Restricts string fields to a predefined set of safe values

This acts as a final safety net, catching any edge cases where constrained decoding might produce structurally valid but semantically dangerous content.

A safety framework that establishes a strict privilege hierarchy for instructions. System-level directives (the schema and core rules) are treated as immutable and higher-priority than user-level or tool-level inputs.

• System Prompt: Defines the output format and unalterable constraints
• User Input: Treated as untrusted data to be placed into the schema, never as instructions
• Tool Output: Sanitized and inserted into designated fields only

By architecturally preventing lower-privilege inputs from overriding the output format, injection attacks are rendered inert regardless of their creativity.

Before user-provided content is inserted into the structured output, it undergoes rigorous sanitization to neutralize any embedded control sequences or escape attempts.

• Escape special characters: Prevents JSON injection via unescaped quotes or brackets
• Strip control characters: Removes null bytes, zero-width characters, and other invisible attacks
• Truncate to max length: Prevents context window exhaustion through oversized fields

This ensures that even if malicious content is placed into a string field, it remains inert data rather than executable structure.

The downstream application consuming the model's output must use strict, schema-aware parsers that reject any deviation. This creates a hard contract between the AI and the application logic.

• Reject on extra keys: Prevents attackers from injecting unexpected fields
• Fail closed: Any parse error triggers a safe fallback, never best-effort interpretation
• Versioned schemas: Allows safe evolution of the output contract without breaking defenses

This defense-in-depth approach ensures that even if the model is tricked into adding malicious fields, the consuming system will reject the entire payload.

When structured output triggers downstream actions (API calls, database queries), a Tool Authorization Gate validates that the requested action is permitted for the current context. The structured format makes this validation deterministic.

• Action whitelisting: Only pre-approved function names can be invoked
• Parameter validation: Each argument is checked against allowed ranges and types
• Rate limiting: Prevents abuse even with validly structured requests

By decoupling output formatting from execution authority, the system prevents injection attacks from translating into unauthorized actions.

The process of cleaning and normalizing user-provided text to remove or neutralize potentially malicious control sequences before model processing. This is often the first line of defense.

• Canonicalization: Converting input to a standard Unicode form (e.g., NFC) to defeat homoglyph attacks.
• Stripping: Removing zero-width characters and escape sequences.
• Validation: Rejecting inputs that fail a strict schema check, a prerequisite for structured output enforcement.

The practice of designing robust system-level instructions that are resistant to override attempts. This works synergistically with structured output enforcement.

• Instructional Hierarchy: Prioritizing system directives over user directives.
• Delimiter-Based Defense: Using separators like --- to strictly partition trusted instructions from untrusted input.
• Refusal Training: Fine-tuning a model to explicitly reject requests to 'ignore previous instructions' or output free-form text.

An attack where malicious instructions are injected into data sources a model retrieves, such as web pages or documents, rather than the direct user query. Structured output enforcement is a key mitigation.

• RAG Injection: Poisoning a vector database to manipulate a Retrieval-Augmented Generation system.
• Data Source Poisoning: Inserting hidden text (e.g., white-on-white) into a website to hijack a browsing agent.
• Defense: Enforcing a strict JSON output schema prevents the model from executing injected free-form commands.

A secondary, often smaller, model that screens inputs and outputs of a primary model to detect and block policy violations or injection attacks. It acts as a safety layer independent of the main model.

• Input Guard: Classifies user prompts for malicious intent before they reach the core LLM.
• Output Guard: Validates that the final response conforms to the expected schema and doesn't leak system prompts.
• Synergy: A guard model can enforce structured output constraints even if the primary model is compromised.

A Web Application Firewall-like layer that inspects and blocks malicious prompts at the API gateway before they reach the language model. It provides a fast, deterministic first pass.

• Signature-Based Detection: Blocking known injection strings and bypass patterns.
• Perplexity Analysis: Identifying anomalous prompts that deviate from normal user input distributions.
• Schema Validation: Rejecting any request that attempts to define an alternative output format, reinforcing server-side structured output enforcement.

A security checkpoint that validates and authorizes any function call or API request a model attempts to make. This prevents an injection from autonomously triggering destructive actions.

• Parameter Validation: Ensuring the arguments of a tool call match a strict, expected schema.
• Human-in-the-Loop (HITL): Requiring manual approval for high-risk operations like sending emails or executing code.
• Least Privilege: Granting the model access only to the minimum set of tools required, limiting the blast radius of a successful injection.