What Is Training Data Poisoning in LLMs & 6 Ways to Prevent It

Potential Impact of a Data Poisoning Attack 

Data poisoning can lead to various negative outcomes for large language models.

Biases Introduced into Decision-Making

Biases introduced through data poisoning can distort a model's perception of data, leading to skewed outputs and erroneous conclusions. These biases might manifest in various ways, such as racial or gender discrimination, which is particularly harmful in applications like recruitment or credit scoring. 

As LLMs integrate deeper into societal functions, these biases could perpetuate systemic inefficiencies or injustices. The roots of bias in AI models stem from their training data, which, when poisoned, shifts the model's understanding in favor of erroneous patterns. 

Reduced Accuracy, Precision, and Recall

Data poisoning can significantly reduce a model's accuracy, precision, and recall. The quality of the predictions or classifications degrades, leading to higher error rates. This drop in performance can disrupt applications relying on high accuracy, resulting in poor user experiences and decision-making failures.

Accuracy refers to a model's ability to predict or classify correctly. When training data is corrupted, the model might learn incorrect associations, leading to decreased precision—its ability to consistently perform—and recall—its effectiveness in identifying all relevant instances.

Potential for System Failure or Exploitation

When models operate on poisoned data, the risk of systemic failure or exploitation increases. An adversary could craft data poisoning strategies to trigger failures, such as denial-of-service attacks or unintended behavior in AI-driven processes. 

AI systems exploiting poisoned models can become gateways for further attacks, especially in integrated environments where multiple systems rely on shared model insights. Ensuring resilient architecture with fail-safes against poisoned data manipulation can prevent cascading failures.

Types of Data Poisoning 

Data poisoning attacks can be divided into targeted and non-targeted attacks.

Targeted Data Poisoning Attacks

Targeted data poisoning attacks focus on altering the model's behavior in specific scenarios, often with predetermined goals in mind like misclassification or bias introduction. These attacks are carefully devised to influence the model in particular situations, making them highly effective but complex to engineer and deploy. Malicious actors must have detailed knowledge of how and where the data influences model outcomes to execute these attacks.

Non-Targeted Data Poisoning Attacks

Non-targeted data poisoning attacks aim to decrease the overall performance of an AI model rather than targeting specific outcomes. The intent is to deteriorate the model's general effectiveness, causing reliability and trust to diminish across all use cases. These attacks are broader but often easier to execute, and can often go unnoticed without proper data validation.

Common Examples of Data Poisoning Vulnerabilities

Here are some examples of common vulnerabilities that enable data poisoning:

• Data injection: Malicious actors inject falsified or harmful documents into a model's pre-training or fine-tuning data. For example, an attacker might create inaccurate content aimed at skewing the model’s outputs, which could be reflected in generative AI responses. 
• Split-view data poisoning: Adversaries manipulate subsets of the data viewed by different parts of the model to introduce inconsistencies. 
• Frontrunning poisoning: The attackers introduce targeted information into the training pipeline before the legitimate data arrives, influencing the model to prioritize false patterns.
• Indirect attacks: Data poisoning can also occur inadvertently. For example, if a user unknowingly inputs sensitive or proprietary data during the training phase, it may be reflected in outputs delivered to other users. Similarly, unverified data sources can corrupt a model's learning, leading to flawed decisions and inaccurate results.

Tzvika Shneider

CEO, Pynt

Tzvika Shneider is a 20-year software Security industry leader with a robust background in product and software management.

Tips from the expert

• Use federated learning to reduce centralized risk: Federated learning distributes training across multiple devices, keeping data localized rather than pooled in a central database. This makes it harder for attackers to poison a large portion of the training data at once.
• Implement cross-validation with differential datasets: Use cross-validation on multiple, isolated subsets of your training data to detect potential poisoning. By comparing model performance across these subsets, you can identify inconsistencies or biases introduced through compromised data.
• Conduct data provenance tracking: Track the lineage of all training data to verify its source and integrity. Establishing a data pipeline with version control and signatures ensures any tampered or suspicious data can be traced and verified before inclusion in training.
• Harden preprocessing pipelines with strong validation: Before any data is used for training, ensure it goes through a robust preprocessing pipeline that filters out abnormal patterns, mislabeled samples, or statistical anomalies. This adds an extra layer of defense against poisoned data.
• Leverage blockchain for tamper-proof data verification: Use blockchain or distributed ledger technology to create immutable records of your training data sources. This makes it easier to detect unauthorized modifications and provides transparency into data handling.

How to Prevent Training Data Poisoning Attacks 

Here are some of the measures that can be used to prevent poisoning of training data.

1. Data Validation and Verification

Data validation and verification involve a combination of automated and manual processes to ensure the integrity of the training data. Automated checks can identify inconsistencies, such as duplicate entries, missing values, or outliers that don't align with expected patterns. For example, if a model is being trained on financial data, any unexpected extreme values or abnormal patterns can signal an attack or error.

Manual verification adds an additional layer of security by involving human labelers to cross-check data labeling accuracy. This helps mitigate errors that automated processes might miss, such as subtle biases or incorrect classifications that could be injected through poisoned data. Multiple reviewers can independently assess the data to ensure labels are correct.

2. Secure Data Storage

Training data should be stored in a highly secure environment, ensuring that only authorized individuals and systems have access. Techniques such as data encryption, secure access protocols, and firewalls are essential to protecting the data from unauthorized modification or theft.

Encryption ensures that even if the data is intercepted, it cannot be easily read or manipulated. Secure transfer protocols like HTTPS or SFTP prevent data from being intercepted during transmission. Firewalls, combined with access control mechanisms, limit who can access the data and from where. Access logs should be maintained to track who accesses the data and when.

3. Data Separation

Data separation helps prevent training data from being exposed to risks associated with production environments. By keeping training and production datasets isolated, the chance of cross-contamination is minimized. Training data should be collected, cleaned, and processed in a controlled environment that is separated from the system that handles live production data.

This separation also allows for more focused security measures. For example, training data can be housed in a restricted and monitored environment, while production data can be stored in an environment that prioritizes performance and availability. This prevents accidental leakage of sensitive data from production into the training process, which could introduce errors or biases.

4. Model Validation

Model validation is the process of ensuring that the trained model performs well on unseen data, which can reveal any impact caused by poisoned training data. This requires a separate validation dataset that has not been used during training. This dataset must be clean, diverse, and reflective of the real-world scenarios the model will encounter.

Validating the model on an independent dataset allows developers to observe whether the model behaves as expected. If poisoned data has affected the training process, the model’s performance on the validation set will likely degrade, showing higher error rates or unusual patterns in its predictions. 

5. Model Ensembles

Training multiple models on different subsets of the data and using their combined predictions (i.e., model ensembles) can increase a model’s resilience to data poisoning. Since each model learns from a different subset of the data, an attacker would need to poison multiple subsets to have a significant effect on the overall ensemble's performance.

Ensembles work by aggregating the predictions from these various models. For example, if one model is compromised due to poisoned data, the other models in the ensemble can "outvote" its incorrect predictions, reducing the likelihood of poisoned data influencing the final output. This method requires attackers to corrupt multiple data points across multiple training subsets.

6. Anomaly Detection

Anomaly detection involves identifying poisoned data by monitoring for unusual patterns during the training phase. By analyzing the statistical distribution of the data and the labels, anomaly detection systems can flag sudden shifts that might indicate an attack. For example, if the distribution of a feature suddenly changes, it could indicate malicious data injection.

Machine learning models can be used to continuously monitor the behavior of data streams, checking for irregularities such as mislabeled examples, out-of-range values, or unexpected changes in the distribution of data points. Anomaly detection can also be applied to model outputs during training to flag unexpected behaviors, such as a sharp drop in accuracy.

Application Security Testing for LLM APIs with Pynt

Pynt focuses on API security, the main attack vector in modern applications. Pynt’s solution aligns with application security best practices by offering automated API discovery and testing, which are critical for identifying vulnerabilities early in the development cycle. It emphasizes continuous monitoring and rigorous testing across all stages, from development to production, ensuring comprehensive API security. Pynt's approach integrates seamlessly with CI/CD pipelines, supporting the 'shift-left' methodology. This ensures that API security is not just an afterthought but a fundamental aspect of the development process, enhancing overall application security.

Learn with Pynt about prioritizing API security in your AST strategy to protect against threats and vulnerabilities, including LLM's emerging attack vectors.