# Improper Input Validation (CWE-20)

The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.

**Stack:** Python

- Prevalence: Élevée Fréquemment exploitée
- Impact: Élevé 6 règles de sévérité élevée
- Prevention: Documentée 13 exemples de correctifs

**OWASP:** Broken Access Control (A01:2021-Broken Access Control) - #1

## Description

Input validation is a frequently-used technique for checking potentially dangerous inputs in order to ensure that the inputs are safe for processing within the code, or when communicating with other components. When software does not validate input properly, an attacker is able to craft the input in a form that is not expected by the rest of the application.

## Prevention

Stratégies de prévention pour Improper Input Validation basées sur 2 règles de détection Shoulder.

### Python

Use Pydantic models with Field validators instead of raw Request objects

Validate business-critical inputs with range constraints using Pydantic or manual checks

## Warning Signs

- [MEDIUM] FastAPI endpoints that accept raw Request objects instead of
Pydantic models
- [MEDIUM] business-critical input values (discount, refund, quantity, price)
that are used in operations witho

## Consequences

- Exécuter du code non autorisé
- Modification des données de l'application
- DoS
- Lecture des données de l'application

## Mitigations

- Supposez que toute entrée est malveillante. Utilisez une stratégie de validation acceptant uniquement les entrées connues comme valides
- Lors de la validation des entrées, prenez en compte toutes les propriétés potentiellement pertinentes
- Ne vous fiez pas exclusivement à la recherche d'entrées malveillantes ou malformées

## Detection

- Total rules: 13
- Languages: python, go, javascript, typescript

## Rules by Language

### Python (2 rules)

- **FastAPI Missing Request Validation** [MEDIUM]: Detects FastAPI endpoints that accept raw Request objects instead of
Pydantic models. This bypasses FastAPI's automatic validation and can
lead to type confusion and injection vulnerabilities.
  - Remediation: Use Pydantic models instead of raw Request objects for automatic validation.

```python
from pydantic import BaseModel, EmailStr, Field

class UserCreate(BaseModel):
    username: str = Field(min_length=3, max_length=50)
    email: EmailStr

@app.post("/users")
async def create_user(user: UserCreate):
    return {"user": user}
```

Learn more: https://shoulder.dev/learn/python/cwe-20/missing-validation
- **Business Logic Input Validation** [MEDIUM]: Detects business-critical input values (discount, refund, quantity, price)
that are used in operations without proper validation. Missing validation
can lead to financial fraud, inventory errors, or business logic bypass.
  - Remediation: Validate business-critical inputs with range constraints using Pydantic.

```python
from pydantic import BaseModel, Field

class DiscountRequest(BaseModel):
    discount: float = Field(..., ge=0, le=100)
    quantity: int = Field(..., gt=0)

@app.post('/apply-discount')
async def apply_discount_route(request: DiscountRequest):
    apply_discount(request.discount)
```

Learn more: https://shoulder.dev/learn/python/cwe-20/input-validation