The correct answer is 'Data poisoning attacks,' as this is the term used for the type of cybersecurity threat where an adversary deliberately introduces false data into a machine learning model's training set to corrupt the model's learning process, leading to incorrect or biased outputs. 'SQL injection' and 'Cross-site scripting' are unrelated to machine learning training datasets; they involve injecting malicious code into databases and web applications, respectively. 'Buffer overflow' is an attack that exploits a memory overflow error in the software, which is also unrelated to training datasets for ML models.