Secure Coding Practices Questions
Some common security vulnerabilities in machine learning include:
1. Adversarial attacks: These are deliberate attempts to manipulate or deceive machine learning models by introducing malicious inputs or perturbations. Adversarial attacks can lead to incorrect predictions or decisions by the model.
2. Data poisoning: This occurs when an attacker manipulates the training data used to train a machine learning model. By injecting malicious or biased data, the attacker can influence the model's behavior and compromise its integrity.
3. Model inversion: This vulnerability allows an attacker to infer sensitive information about the training data or the individuals represented in the data by analyzing the outputs of the machine learning model.
4. Model stealing: In this vulnerability, an attacker can extract or replicate a machine learning model by querying it with carefully crafted inputs. This can lead to intellectual property theft or unauthorized access to proprietary models.
5. Privacy breaches: Machine learning models trained on sensitive or personal data can inadvertently leak information about individuals through their predictions or outputs. This can violate privacy regulations or expose sensitive information.
6. Bias and discrimination: Machine learning models can inherit biases present in the training data, leading to discriminatory or unfair outcomes. This can perpetuate social inequalities or result in biased decision-making.
7. Model evasion: Attackers can manipulate the inputs to a machine learning model to evade detection or classification. By carefully crafting inputs, they can bypass security measures or exploit vulnerabilities in the model's decision-making process.
To mitigate these vulnerabilities, secure coding practices in machine learning involve robust data preprocessing, careful model selection and evaluation, regular model updates and monitoring, and incorporating fairness and privacy considerations into the development process.