Threat Models to Machine Unlearning

1. Malicious Unlearning Request Attack

A malicious unlearning request attack involves an adversary exploiting the unlearning process itself by submitting false or deceptive requests to force the model to forget important or benign data, thus causing it to underperform or behave abnormally. These types of attacks target the unlearning mechanism that is designed to remove certain data points from a model. However, if an attacker can manipulate this mechanism, they can misuse the unlearning process to achieve malicious ends, such as weakening the model’s ability to make accurate predictions or disrupting its operational effectiveness. [9] is an attack with poisoned training dataset that involves unexpected unlearning request attack. The original model cannot differentiate the valid request and the malicious one.

In malicious unlearning request attacks, adversaries typically pose as legitimate users who wish to remove data from the model, but their true goal is to remove strategically important data or otherwise undermine the model’s functionality. This can be accomplished in several ways: 1. Overloading the unlearning system with an excessive number of unlearning requests can cause the model to experience a performance degradation, as repeated requests for unlearning certain data points could impact the model’s ability to generalize effectively. 2. Selective unlearning of critical data: An attacker might target specific, highly informative data points for unlearning. By removing or forgetting highly influential data, the model may become less accurate, biased, or incapable of responding appropriately to certain inputs.

An example of a malicious unlearning request attack could occur in the context of a recommender system that uses machine learning models to predict user preferences. An adversary could submit numerous unlearning requests to delete the data related to popular items or preferences, thereby skewing the recommendations toward lesser-known or even harmful items. In another instance, a malicious actor might target the unlearning of critical data in a healthcare model that predicts patient diagnoses, causing the model to lose valuable diagnostic insights.

In terms of defense against such an attack, for instance, imagine a fraud detection system designed to detect fraudulent financial transactions. An attacker who successfully requests unlearning of key fraudulent transaction patterns could cause the system to forget these crucial patterns, leaving it unable to detect future fraud. This not only weakens the model’s ability to catch fraudulent activities but also opens the door for the adversary to launch further financial attacks undetected.

To defend against this type of attack, it is essential to verify the authenticity of unlearning requests by employing robust identity verification processes and auditing systems that track the legitimacy of the unlearning request. Techniques such as request authentication, limiting the number of unlearning requests that a user can submit in a certain time period, and monitoring patterns of suspicious unlearning requests can help mitigate this risk. Moreover, incorporating data importance weighting mechanisms can ensure that highly critical or valuable data is protected from unlearning without rigorous verification [10].

2. Data Reconstruction Attack

A data reconstruction attack is a highly sophisticated security threat in which adversaries attempt to recover forgotten data after it has been removed from the model. Despite efforts to unlearn specific data, the model may still retain subtle traces or patterns that allow an attacker to reconstruct the original data with sufficient accuracy. This type of attack capitalizes on the fact that many machine learning models, especially complex ones like deep neural networks, may not completely “forget” certain information even after the unlearning process has been applied.

Data reconstruction attacks typically rely on model inversion or gradient-based methods to uncover information that has been theoretically unlearned. By probing the model with specific inputs or by analyzing the gradients during the learning process, attackers can infer how certain data influenced the model’s parameters, even if the data was supposedly forgotten. This allows adversaries to reverse-engineer the underlying patterns, relationships, or even the exact data points that were used to train the model. [11] describes a carefully designed unlearned data request that passes the security defense. [12] introduces a similar crafted request that passes both the security and membership verification. They suggest that hash verification and membership verification are both needed to counter such attacks.

For example, in a machine learning model trained on facial recognition data, an attacker might exploit a data reconstruction attack to infer facial features or even generate an image that closely resembles a person whose data was meant to be unlearned. Similarly, in financial models, an attacker could reconstruct key financial records or transaction details that were theoretically removed, leading to severe privacy breaches and security vulnerabilities.

An illustrative example of a data reconstruction attack can be found in medical data systems. Suppose a model is trained on patient health data to predict disease outcomes. Even after a patient request that their data be removed through machine unlearning, an adversary could employ data reconstruction techniques to recover sensitive health information. This could include inferring the patient’s disease history or even identifying specific individuals, despite their data being “forgotten.”

Defending against data reconstruction attacks requires careful design of the unlearning process. Differential privacy is a critical defense mechanism that can be employed to ensure that the removed data cannot be reconstructed from the remaining information. Additionally, model auditing tools that analyze the extent of forgetting after unlearning can be used to assess whether the unlearning process was fully effective. Implementing model regularization techniques can also reduce the amount of residual information left behind after unlearning, making it more difficult for adversaries to reconstruct forgotten data.

3. Jailbreak Attack

A jailbreak attack targets the internal constraints or limitations imposed on machine learning models, often aimed at exploiting the model’s ability to perform unauthorized or unintended actions. In the context of machine unlearning, a jailbreak attack involves bypassing the protections designed to ensure that the model has forgotten specific data. This attack can be viewed as an effort to “unlock” or regain access to information that has been removed or restricted by the unlearning process. In such cases, attackers may find ways to extract forgotten data or regain control over model behaviors that were disabled through unlearning. [13] describes such an attack during machine unlearning process that is embedded in in-context learning.

In a jailbreak attack, the adversary seeks to manipulate the model’s operational constraints by exploiting weaknesses in the unlearning process. This could be achieved through various means: 1. Bypassing access controls or security mechanisms designed to enforce unlearning. An attacker could attempt to trick the system into ignoring unlearning constraints, effectively restoring access to forgotten data. 2. Activating hidden functionalities or bypassing input validation checks that were disabled during unlearning. By sending crafted inputs, an adversary can cause the model to behave in ways it was not intended to after the unlearning process.

For instance, in a security system based on machine learning that has undergone unlearning to remove certain privileges or access patterns, a jailbreak attack could enable the adversary to bypass these restrictions and regain unauthorized access to protected areas of the system.

[14] and [15] illustrate jailbreak attacks to machine unlearning, both suggest the increase of adversarial mechanisms as the ultimate defense solution. An example of a jailbreak attack could occur in the context of AI systems used in autonomous vehicles. Suppose a model has been unlearned to disable certain driving behaviors deemed unsafe, such as speeding in restricted areas. A skilled adversary might launch a jailbreak attack to re-enable these behaviors, allowing the vehicle to disregard speed limits or other safety constraints. In another example, an attacker might bypass the unlearning protections in a machine learning system that controls access to sensitive databases, enabling them to recover previously restricted or forgotten data.

To defend against jailbreak attacks, it is important to implement strong security policies that prevent the reactivation of unlearned behaviors or the restoration of forgotten data. Input validation and strict access control mechanisms can help ensure that unauthorized actions cannot be triggered. Moreover, continuous monitoring of model outputs and auditing of system behaviors after unlearning is necessary to detect and mitigate potential jailbreak attempts. By incorporating robust logging and anomaly detection systems, administrators can quickly identify when an adversary is attempting to circumvent the constraints imposed by unlearning.