When building a threat model for an Arabic AI project, you can’t just use a generic framework. You have to consider the unique characteristics of the Arabic language and the specific context of the MENA region. This includes:

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• The Diversity of Arabic Dialects: The Arabic language is not a single entity. It’s a macrolanguage with a wide range of dialects, many of which are not mutually intelligible. This creates a unique challenge for Arabic AI systems, as a model trained on one dialect may not perform well on another. It also creates a new attack surface, as an attacker could exploit these differences to poison the training data or create adversarial examples.
• The Geopolitical Context: National AI projects in the MENA region are often strategically important, and they may be targeted by state-sponsored actors. This means the threat model has to account for the possibility of highly sophisticated and well-resourced attacks.
• The Cultural Context: AI systems in the MENA region must be sensitive to local culture and values. An AI that produces content that is seen as offensive or inappropriate could cause significant reputational damage and could even lead to the project being shut down.

A comprehensive threat model for an Arabic AI project should consider the following key threat categories, many of which are outlined in the OWASP Top 10 for Large Language Model Applications:

1. Data Poisoning

This is one of the most insidious threats to AI systems. It involves an attacker deliberately contaminating the training data to manipulate the model’s behavior. For an Arabic AI system, this could involve:

• Dialect-Based Poisoning: An attacker could inject data from a specific dialect to cause the model to perform poorly on other dialects. For example, they could poison a speech recognition model with data from a North African dialect to cause it to fail on Gulf dialects.
• Cultural Bias Injection: An attacker could inject data that is biased against a particular nationality, religion, or ethnic group, causing the model to produce discriminatory or offensive outcomes.
• Backdoor Attacks: An attacker could introduce a “backdoor” into the model by poisoning the training data with specific triggers. For example, they could train a sentiment analysis model to classify any news article that mentions a specific political figure as “negative,” regardless of the actual content of the article.

2. Model Evasion: 

This involves an attacker creating inputs that are designed to evade detection or to cause the model to make a mistake. For an Arabic AI system, this could involve:

• Adversarial Examples: An attacker could make subtle changes to an input that are imperceptible to a human but that cause the model to make a wrong prediction. For example, they could add a small amount of noise to an image of a stop sign to cause a self-driving car’s AI to misclassify it as a speed limit sign.

• Linguistic Obfuscation: An attacker could use the unique features of the Arabic language, such as its complex morphology and the use of diacritics, to craft inputs that are designed to confuse the model.

3. Model Theft and Privacy Breaches

These attacks are focused on stealing the model itself or the sensitive data it contains.

• Model Extraction: An attacker could use a series of queries to the model to effectively “steal” it by creating a copy of the model.
• Model Inversion: An attacker could use the model’s predictions to reconstruct the sensitive data that it was trained on. For example, they could use a facial recognition model to reconstruct the faces of the individuals in the training data.
• Membership Inference: An attacker could determine whether a specific individual’s data was used to train the model. This is a major privacy violation, especially if the model was trained on sensitive data, such as medical records.