Data Poisoning Attacks How to Beef up Your AI Security?

In the expansive realm of artificial intelligence (AI) and machine learning (ML), where data is paramount, a threat has surfaced with significant consequences: data poisoning. This insidious attack strategy targets the core foundation of machine learning models—the data itself. 

This article aims to explore the intricacies of data poisoning, understand its mechanisms, and map out its impact on AI security, providing valuable insights for those defending the frontlines of AI technology.

What is Data Poisoning? 

Data poisoning is a cyberattack where an adversary deliberately corrupts a training dataset used by an AI or machine learning (ML) model to manipulate its behavior or performance.

The goal of the cyber threat actor is to corrupt this training dataset so that the AI/ML model learns biased or incorrect information that it can further exploit.

The cyber threat actor can carry out data poisoning in multiple ways. They can modify a small portion of the dataset or a big portion of a completely false dataset can be injected into the original dataset. 

Now that we have defined data poisoning, let’s understand their types.

Types of Data Poisoning

Data poisoning is typically classified based on the outcome of the attacks. There are two most common types of data poisoning attacks.

Targeted Data Poisoning Attacks: 

In these attacks, an adversary aims to manipulate the model’s behavior in specific situations. For example, a cybercriminal might train a cybersecurity tool to misidentify a particular file they plan to use in a future attack or to ignore suspicious activities from a certain user. Although targeted attacks can have severe and widespread consequences, they do not degrade the overall performance of the AI model.

Non-Targeted Data Poisoning Attacks: 

In contrast, non-targeted attacks involve manipulating the dataset to undermine the model’s overall performance. An adversary might introduce false data, reducing accuracy and impairing predictive or decision-making capabilities.

Now that we are familiar with the types of data poisoning attacks, let’s go through some examples of data poisoning.

Examples of Data Poisoning Attacks 

Backdoor Poisoning

Backdoor poisoning involves injecting malicious data into the training set to introduce a vulnerability, or "backdoor," which an attacker can later exploit. This backdoor allows the attacker to manipulate the model’s performance and output. Backdoor poisoning can be targeted or non-targeted, depending on the attacker's specific goals.

Availability Attack

An availability attack aims to disrupt the availability of a system or service by contaminating its data. Adversaries use data poisoning to degrade the performance or functionality of the targeted system. This can result in the system producing false positives or negatives, failing to process requests efficiently, or even crashing entirely. Such disruptions render the application or system unreliable or unavailable for its intended users.

Model Inversion Attacks

In a model inversion attack, the adversary uses the model’s outputs to reconstruct the dataset or infer information about the inputs. This attack is typically carried out by an insider, such as an employee or approved system user, who has access to the model’s outputs.

Stealth Attacks

A stealth attack is a subtle form of data poisoning where an adversary gradually modifies the dataset or injects compromising information to avoid detection. Over time, these incremental changes can introduce biases impacting the model’s accuracy. Because stealth attacks operate "under the radar," tracing the issue back through the training dataset can be challenging, even after the problem is identified.

Now that the examples are set in stone, let’s understand some best practices to defend against data poisoning.

Best Practices for Defending Against Data Poisoning

Data Validation

Preventing data poisoning is more effective than attempting to clean up and restore a compromised dataset. Organizations should employ advanced data validation and sanitization techniques to detect and remove suspicious data points before they are included in the training set.

Monitoring, Detection, and Auditing

Continuous monitoring is essential for quickly detecting and responding to potential AI/ML systems risks. Organizations should use cybersecurity platforms that offer continuous monitoring, intrusion detection, and endpoint protection. Regular auditing models can help identify early signs of performance degradation or unintended outcomes.

Implementing live monitoring of input and output data can enhance this process. Organizations can quickly take security measures to protect their systems by continuously scrutinizing the data for anomalies or deviations. User and entity behavior analytics (UEBA) can be used to establish a behavioral baseline for the ML model, making it easier to detect unusual patterns.

Adversarial Training

Adversarial training is a proactive defence strategy introducing adversarial examples into a model’s training data. This teaches the model to correctly classify these inputs as intentionally misleading, helping it recognize and defend against attempts to manipulate its training data.

Data Provenance

Maintaining detailed records of all data sources, updates, modifications, and access requests is crucial. While these measures may not directly detect a data poisoning attack, they are invaluable for recovering from security events and identifying responsible parties. Robust data provenance can also act as a deterrent against white-box attacks.

Secure Data Handling

Establishing and enforcing strong access controls is vital, especially for sensitive data. Implement the principle of least privilege (POLP), ensuring that users only have access rights necessary for their tasks. Comprehensive data security measures should be employed, including data encryption, obfuscation, and secure storage.

To protect your AI systems from data manipulation, it is essential to understand the nature of these attacks, and their connection to deep fakes, and to implement effective countermeasures.