What are data poisoning attacks?

Data poisoning attacks are a type of adversarial machine learning attack where malicious actors intentionally corrupt the data used to train a model, with the goal of degrading its performance, introducing bias, or manipulating its outputs.

How do data poisoning attacks work?

Attackers use different methods depending on their goals. In general, the process involves:

• Gaining access: Attackers find ways to insert or modify data in the training pipeline. This may be through insecure data sources, insider threats, or weak security controls.
• Injecting poisoned data: Malicious samples are added. They may be mislabeled, subtly altered, or carry hidden triggers.
• Model training: The poisoned dataset is used to train the model. Since the model treats all data as “truth,” it internalizes the wrong patterns.
• Exploitation: The compromised model behaves as intended by the attacker, such as misclassifying specific inputs or producing biased predictions.

Real-world example

A newly published study, Not all samples are equal: Quantifying instance-level difficulty

in targeted data poisoning, noted that not all data points are equally vulnerable to poisoning. While many discussions about data poisoning assume that any model input can be corrupted with the same ease, this research shows that some instances are far more resistant to manipulation than others.

The authors introduced three predictive metrics to measure susceptibility:

• Ergodic prediction accuracy (EPA): How consistently a sample is predicted across training runs (higher = harder to poison).
• Poison distance (PD): How far a sample is from poisoned data in feature space (greater distance = more resilient).
• Poison budget (PB): The minimum amount of poisoned data needed to alter a sample (higher = more costly for attackers).

The study demonstrated that models are not uniformly fragile. For instance, in image classification tasks, highly distinctive samples (e.g., a brightly colored bird in a bird-recognition dataset) were more resilient to poisoning. In contrast, more ambiguous ones (e.g., birds with overlapping features) were easier to manipulate.

This nuanced understanding opens the door to better defense strategies. By identifying which samples are most vulnerable, organizations can:

• Prioritize protections for high-risk data points.
• Develop targeted monitoring to detect attacks where they are most likely.
• Allocate computational resources more efficiently for model retraining and anomaly detection.

In practice, this means data poisoning defenses don’t have to be “one-size-fits-all.” Instead, models can be safeguarded with instance-level security, ensuring that attackers face higher barriers when attempting to compromise the most sensitive or influential samples in a dataset.

Types of data poisoning attacks

Data poisoning attacks come in several forms, as identified by IBM, each targeting different aspects of an AI model’s performance. Common types include:

Label flipping

“Label flipping attacks manipulate the labels in training data, swapping correct labels with incorrect ones.” A famous example is Nightshade, which allows artists to subtly alter images online. When AI models scrape these datasets, the manipulations can cause unpredictable misclassifications, like mistaking cows for leather bags.

Data injection

“Data injection introduces fabricated data points to steer model behavior.” Similar to an SQL injection, malicious data can mislead AI models, causing biases or errors and undermining overall robustness.

Backdoor attacks

“Backdoor attacks leave the AI system functioning normally, but trigger specific behaviors when a hidden input is encountered.” Examples include imperceptible watermarks or inaudible noise. Open-source models are especially vulnerable; ReversingLabs reported a 1300% increase in threats via open-source repositories from 2020 to 2023.

Clean-label attacks

“Clean-label attacks are the stealthiest, as poisoned data appears correctly labeled and bypasses traditional validation.” These subtle changes can still skew outputs and degrade model performance, exploiting the complexity of modern AI systems.

Defending against 

While completely eliminating the risk is difficult, organizations can take steps to reduce exposure:

• Data validation and sanitization: Rigorously check data for anomalies, inconsistencies, or outliers before using it for training.
• Robust model training: Use algorithms that are less sensitive to mislabeled or manipulated data.
• Access controls: Restrict who can contribute or modify training datasets, and monitor for insider threats.
• Continuous monitoring: Audit model behavior regularly to detect sudden performance drops or unusual misclassifications.

See also: HIPAA Compliant Email: The Definitive Guide (2025 Update)

FAQS

Can AI models recover after being poisoned?

Yes, but recovery often requires retraining with clean data. Depending on the extent of the poisoning, this can be resource-intensive.

Is data poisoning always intentional?

Not necessarily. While many cases involve malicious intent, poor data quality, human error, or mislabeled samples can also “poison” a dataset unintentionally.