# Deserialization of Untrusted Data (CWE-502)

The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid.

**Stack:** Python

- Prevalence: Moyenne 3 langages couverts
- Impact: Critique 3 règles de sévérité critique
- Prevention: Documentée 7 exemples de correctifs

**OWASP:** Software and Data Integrity Failures (A08:2021-Software and Data Integrity Failures) - #8

## Description

Many programming languages allow the serialization of objects for storage or transmission. When untrusted data is deserialized, it can lead to code execution, denial of service, or other unintended consequences.

## Prevention

Stratégies de prévention pour Deserialization of Untrusted Data basées sur 3 règles de détection Shoulder.

### Python

Validate training data with Pydantic schemas and apply content moderation before ingestion

Replace pickle/marshal with JSON or other safe serialization formats

Use yaml.safe_load() instead of yaml.load() to prevent code execution

## Warning Signs

- [HIGH] untrusted or unvalidated data flowing into AI/LLM fine-tuning or training
processes
- [CRITICAL] untrusted user input being deserialized using unsafe methods like
pickle
- [CRITICAL] unsafe YAML deserialization using yaml

## Consequences

- Exécuter du code non autorisé
- DoS : crash / sortie / redémarrage
- Modification des données de l'application

## Mitigations

- Évitez si possible la désérialisation de données non fiables
- Si la désérialisation est nécessaire, utilisez des formats plus sûrs comme JSON
- Mettez en place des contrôles d'intégrité tels que des signatures numériques
- Isolez la désérialisation dans des environnements à faibles privilèges

## Detection

- Total rules: 7
- Critical: 3
- Languages: go, javascript, typescript, python

## Rules by Language

### Python (3 rules)

- **LLM Training Data Poisoning** [HIGH]: Detects untrusted or unvalidated data flowing into AI/LLM fine-tuning or training
processes. OWASP LLM03 - Training Data Poisoning.

Training data poisoning can:
- Introduce backdoors into model behavior
- Bias model outputs maliciously
- Embed harmful content that appears in responses
- Compromise model accuracy and reliability
- Create security vulnerabilities in model behavior
  - Remediation: Validate training data with Pydantic and use content moderation.

```python
from pydantic import BaseModel, validator

class TrainingData(BaseModel):
    examples: list

    @validator('examples', each_item=True)
    def validate_example(cls, v):
        if len(v.get('content', '')) > 4000:
            raise ValueError('Content too long')
        return v

data = TrainingData(**request.json)
moderation = await openai.moderations.create(input=data.json())
```

Learn more: https://shoulder.dev/learn/python/cwe-502/llm-training-data-poisoning
- **Unsafe Deserialization** [CRITICAL]: Detects untrusted user input being deserialized using unsafe methods like
pickle.loads() or yaml.load().
  - Remediation: Use json.loads() or yaml.safe_load() instead of pickle.

```python
import json
obj = json.loads(user_data)
```

Learn more: https://shoulder.dev/learn/python/cwe-502/unsafe-deserialization
- **Unsafe YAML Deserialization** [CRITICAL]: Detects unsafe YAML deserialization using yaml.load() without SafeLoader.
  - Remediation: Use yaml.safe_load() instead of yaml.load().

```python
config = yaml.safe_load(yaml_string)
```

Learn more: https://shoulder.dev/learn/python/cwe-502/yaml-deserialization