According to latest reports, 48% of businesses globally use machine learning as of 2024. 65% of companies planning to adopt machine learning say the technology helps businesses in decision-making. These Statistics show that in today's tech-driven world, Machine Learning (ML) has become a big deal.       

Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and models. This enables computers to make predictions based on data. ML systems can perform a number of tasks like image classification, language translation and autonomous vehicle control. Also many industries, including healthcare, finance, e-commerce, and manufacturing, have embraced ML to improve efficiency, productivity, and decision-making processes. ML algorithms allow computers to identify patterns, classify data, and make decisions with minimal human intervention.                  

However, despite these exceptional abilities, ML introduces new cyber security risks like, adversarial attack, data poisoning, model inversion and privacy concerns. In this blog we will be discussing the relationship between ML and cyber security, risks faced by ML models and how threat modeling could be a great approach in defending  your ML models.                                        

Most Common threats faced by Machine Learning Models  

There are a number of potential risks to the security and reliability of machine learning (ML) models. Here's a detailed explanation of some of the most common threats:     

• Data Poisoning: Data poisoning is a type of attack that corrupts ML models. Malicious samples are inserted by the attacker in order to inject biases or open up vulnerabilities in the existing dataset used for training. It causes models to learn wrong patterns hence make inaccurate predictions leading to poor decision making. Also, data poisoning attacks undermine the reliability and effectiveness of ML systems due to their nature.  These attacks can be difficult to identify and mitigate because they take place during the model training phase and only become visible once it’s deployed in production. 
• Adversarial attack: Adversarial attack is one of the most common threats faced by ML  models. In these types of attacks, adversaries manipulate input data with the intention to make models produce wrong predictions. A minor change in input data can lead to adversarial examples that may not be detectable by humans but can significantly change a model output. For instance, high-stake decisions based on model predictions such as self-driving cars could face decrease trustworthiness because of adversarial attacks
• Model Evasion: Model evasion seeks to avoid the model’s defenses by finding weak spots in its decision boundary. Malicious agents may come with adversarial examples, which are tailor-made to enable them to evade detection or classification by the model. For example, in image recognition systems an attacker could alter images to resemble other categories or add noise in order to make the model be confused. In this context, model evasion attacks pose a major challenge on how robustly and universally ML models operate especially under adversarial environments where attackers actively work against the intentions of the system.
• Model Stealing and Reverse Engineering: Model stealing attacks involve reverse-engineering a target ML model and creating an identical version of an ML model by querying it and using responses. Attackers may use this vulnerability to steal intellectual property, bypass licensing restrictions, or gain insights into the model's architecture and decision-making process. By doing so they dislocate organizations that have invested heavily into their exclusive machine learning models.
• Privacy Violations: ML models trained on confidential data may leak information about the training data through their outputs. This vulnerability, known as model inversion, can be exploited by adversaries to infer sensitive information about individuals or organizations. For example, in privacy-sensitive applications such as medical diagnosis or personalized recommendations, adversaries may exploit model outputs to infer sensitive attributes or characteristics of individuals in the training dataset, posing privacy risks to data subjects. 

The Importance of Threat Modeling in ML Security 

An effective method for protecting machine learning models is threat modeling. Threat modeling is an organized way to detect, prioritize and minimize security risks present in a system or application. Here is how this model can be employed towards reducing the risks faced by machine learning (ML) systems:

• Identify Assets and Threats: The first thing to do in threat modeling is to list all the assets that need protection and define possible hazards. In the case of ML systems, these include trained ML models, training data, and any sensitive information that is handled by the model.
• Understand Attack Vectors: Consider all potential methods through which adversaries could exploit weaknesses to carry out attacks like data poisoning ones where they could introduce vulnerabilities into a model manipulating training datasets or adversarial attacks in which they craft adversarial examples to deceive the model into making wrong predictions.
• Assess Vulnerabilities: Determine potential vulnerabilities within your ML system that may enable attackers to exploit them for such kinds of attacks. Such vulnerabilities may result from weak data validation mechanisms, lack of robustness within the ML model or inadequate counter adversary measures.
• Mitigate Risks: Mitigate identified risks and vulnerabilities by developing countermeasures. In implementing data poisoning attacks, it is worth noting that robust data validation and preprocessing techniques need to be put in place to enable the detection and mitigation of malicious data manipulation. This can include data quality checks, anomaly detection, and outlier removal to ensure the integrity and reliability of the training data. Besides this, there should be application of methods like adversarial training, diversity injection, or alternatively data augmentation with an aim of ensuring that model resists poisoning.
• Continuously Monitor and Update: Threat modeling is an iterative process that requires continuous monitoring and updating to adapt to evolving threats and vulnerabilities. Regularly assess the effectiveness of implemented countermeasures while adjusting them as required in response to emerging risks or challenges.

Although machine learning has been widely adopted for various uses, it has brought about a new set of security problems. A security-conscious business can be able protect its ML models from adversarial attacks or even data poisoning incidents by applying threat modeling approaches.

Protect your ML models with ease.

Book a demo with Threat Modeler, an automated threat modeling platform, today.