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"Not a Pentest" Trust-Anker: Input validation guide for your own AI systems.
Moltbot AI Security · Input Validation

AI Agent Input Validation

AI agent input validation for Moltbot. Schema validation, sanitization, allowlisting and multi-layer input defense for secure AI agent input processing.

What is Input Validation? Simply Explained

Input validation is like a bouncer for AI agent inputs: it checks every input before it's processed. Schema-based validation checks structure and types. Input sanitization cleans harmful content like HTML or SQL code. Allowlisting is safer than denylisting — only explicitly allowed values are accepted. Length & complexity limits prevent token flooding. Multi-layer defense means validation at multiple levels — API gateway, application and LLM layer. Without input validation, attackers can inject malicious prompts, manipulate tool parameters, or overwhelm the system.

Jump to core concepts and implementation

Core Concepts

1. Schema-based Validation

Strict schema validation of all agent inputs. JSON Schema, Pydantic or Zod for type-safe input processing.

2. Input Sanitization

Sanitization of inputs before processing. HTML encoding, SQL escaping and shell escaping for tool calls.

3. Allowlisting statt Denylisting

Allowlist-based validation is safer than denylisting. Accept only explicitly allowed values and patterns.

4. Length & Complexity Limits

Limit maximum length and complexity for all inputs. Prevents token flooding and resource exhaustion.

5. Multi-Layer Defense

Multi-layer input validation. API gateway → application → LLM layer all with their own validation logic.

Advanced Techniques

Semantic Input Validation

Semantic validation of inputs beyond pure syntax. LLM-based intent analysis for malicious content detection.

Tool Call Validation

Strict validation of tool call parameters before execution. Type checking, range validation and business logic checks.

Rate Limiting per Input Type

Granular rate limiting depending on input type and risk profile. Stricter limits for sensitive operations.

Adversarial Input Testing

Regular testing of validation logic with adversarial inputs. Fuzzing and known injection patterns.

Implementation Steps

1
Define input schemas
Document all possible agent inputs with JSON Schema or Pydantic. Types, lengths and allowed values.
2
Add validation layer
Place a central validation layer before all agent inputs. No direct LLM processing without validation.
3
Implement sanitization functions
Individual sanitization function for each input type. Encoding, escaping and normalization.
4
Validate tool parameters
Validate each tool call parameter individually before the tool is executed. Strict mode for all tools.
5
Test and monitor validation
Automated tests for all validation rules. Monitoring of validation failures for security insights.

🔗 Further Resources

CG

ClawGuru Security Team

✓ Verified
Security Research & Engineering · Input Validation Specialists
📅 Published: 28.04.2026🔄 Last reviewed: 28.04.2026
This guide is based on practical experience with input validation implementations for AI systems in production environments. The described best practices have been proven in real deployments and continuously improved.
🔒 Verified by ClawGuru Security Team·All information fact-checked and peer-reviewed
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