Malicious Unlearning Attacks in ML

Updated 17 August 2025

Malicious Unlearning Attacks are adversarial strategies that exploit unlearning mechanisms, targeting design flaws to degrade model performance, privacy, and overall integrity.
Techniques such as poisoned data triggering, adaptive forget set optimization, and backdoor reactivation illustrate how attackers use both direct and staged approaches to induce costly retraining.
Defense strategies focus on algorithmic safeguards, adversarial latent training, gradient filtering, and cryptographic methods to counter privacy leakage and denial-of-service risks.

Malicious unlearning attacks constitute a rapidly evolving class of adversarial manipulations targeting the integrity, privacy, or efficiency of machine unlearning (MU) mechanisms. These attacks leverage the fundamental objective of unlearning—to remove the influence of specific data on a model—by exploiting design or procedural vulnerabilities to subvert, undermine, or induce costly failure modes in the unlearning process. The attack landscape encompasses denial-of-service, backdoor reactivation, privacy inversion, utility degradation, federation-level manipulation, uncertainty manipulation, and stealthy or adaptive attacks. Recent research documents their tangible impact at scale, their resistance to conventional defenses, and the novel computational and privacy risks they introduce throughout the machine learning lifecycle.

1. Taxonomy and Mechanisms of Malicious Unlearning Attacks

Malicious unlearning attacks are typically partitioned into two major categories: (i) direct unlearning attacks, in which adversaries issue specially crafted unlearning requests (often adversarially optimized forget sets) at inference time without prior data manipulation; and (ii) preconditioned or staged attacks, in which the training phase is manipulated—by poisoning, backdoor insertion, or information condensation—followed by strategic unlearning requests that activate latent vulnerabilities (Liu et al., 20 Mar 2024). Central techniques include:

Poisoned Data Triggering Retraining: Adversarially injected data are engineered such that their later removal causes the unlearning error (e.g., gradient residual norm) to exceed a system threshold, forcing costly full retraining instead of inexpensive approximate updates (Marchant et al., 2021).
Adaptive Forget Set Optimization: In direct attacks, the adversary optimizes the forget set (possibly not even present in the original training data) to maximally deteriorate the retained model performance post-unlearning by moving parameters into regions that induce over-unlearning or targeted misclassification (Huang et al., 12 Oct 2024, Liu et al., 20 Mar 2024).
Backdoor and Mitigation Data Unlearning: Poison and mitigation samples are injected together in training; subsequent unlearning requests target only mitigation samples, causing an initially hidden backdoor to become active (Zhang et al., 2023).
Informative Benign Data Condensation: A small set of synthetic but informative “benign” data is condensed from the original data distribution such that its removal causes catastrophic loss in test accuracy, with synthetic samples evading hash or perturbation-based detection (Ma et al., 6 Jul 2024).
Clean Unlearning Attacks: Distributed, low-magnitude triggers are embedded across classes (including forget-set), and unlearning of clean samples inadvertently amplifies backdoor associations—without explicit poisoning during the unlearning phase (Arazzi et al., 14 Jun 2025).
Unlearning Inversion Attacks: By comparing models before and after unlearning, adversaries reconstruct the features or infer the labels of unlearned samples, exploiting model differences as information leaks (Hu et al., 4 Apr 2024).
Uncertainty Manipulation: Forget-set optimization is carried out not to change the output class but to manipulate predictive uncertainties (over- or under-confidence) on specific samples, thus bypassing accuracy-based detection systems and subverting risk-aware applications (Qian et al., 10 Aug 2025).

These mechanisms operate through optimization over forget sets, influence functions, adversarial suffix generation (in LLMs), and attacks on latent representations, often evading naive hash or membership-based filtering.

2. Denial of Service and Efficiency Attacks

Attacks targeting the computational aspect of machine unlearning present unique denial-of-service (DoS) risks. In certified unlearning paradigms—such as influence function-based updates with error monitoring—a poison subset is optimized so that, when forgotten, the residual error surpasses the retraining trigger, forcing the system to revert to computationally prohibitive model retraining (Marchant et al., 2021).

Formally, the attacker solves a bilevel optimization:

$\max_{D_{\text{psn}}}\ C(\hat{\theta}, D_{\text{psn}}),$

where $C(\hat{\theta}, D_{\text{psn}})$ (e.g., gradient norm, influence norm) mimics the unlearning error bound, against constraint $\hat{\theta} = \arg \min_\theta R(\theta; D_{\text{clean}} \cup D_{\text{psn}})$ . PGD methods enforce stealth under $\ell_p$ -norm constraint.

Empirical results show that even a sub-1% fraction of optimized poisons can reduce the fast unlearning “retrain interval” to near-zero and induce frequent full retraining events. Thus, adversaries can exploit privacy regulations (e.g., GDPR “right to be forgotten”) not for privacy, but for resource exhaustion, effectively mounting service disruption independently of any intended privacy benefit (Marchant et al., 2021, Liu et al., 20 Mar 2024).

3. Backdoors, Information Persistence, and Unlearning Exploitation

Malicious unlearning attacks enable sophisticated backdoor strategies that leverage both the training and unlearning phases. In preconditioned backdoor attacks, adversaries inject poison samples (with target triggers) alongside mitigation samples labeled correctly, so the model initially appears benign. Subsequent iterative unlearning of mitigation data via removal requests causes the model to progressively lose the “canceling” effect, thus revealing and activating the backdoor (Zhang et al., 2023).

Empirical studies confirm that this can raise the attack success rate from near zero to 100% after the mitigation set is unlearned while keeping clean accuracy unchanged (losses below 0.34%). Both exact unlearning (full retraining) and approximate slicing-based schemes (e.g., SISA) are vulnerable, though sharding increases the attack cost but also decreases defensive sensitivity (Zhang et al., 2023).

This paradigm demonstrates a dual-use risk: machine unlearning, designed for privacy and compliance, can be turned into a vector for malicious capability activation or information persistence, especially if unlearn requests can strategically manipulate the function space without model owner control.

In LLMs, dynamic unlearning attacks (DUA) optimize adversarial suffixes that, when appended to a prompt, “reactivate” unlearned knowledge—demonstrated to succeed in over 55% of tested queries (Yuan et al., 20 Aug 2024). This vulnerability holds even if the adversary has no direct access to the unlearned model parameters.

4. Federation-Level Manipulation and Protocol Security

Malicious unlearning attacks in federated settings (federated unlearning) are distinct due to distributed data, aggregation rules, and the need for privacy-preserving unlearning without full retraining. Several new attack paradigms have been reported:

Influence-Based Manipulation (FedMUA): Malicious clients identify and subtly manipulate influential samples for a target victim within their own local dataset via classical influence function analysis. Feature perturbations minimize distance to the target in representation space, and malicious unlearning requests are limited (as little as 0.3% of total) but induce misclassification with attack success rates up to 80% on real datasets (Chen et al., 21 Jan 2025).
BadUnlearn Poisoning: Rather than attacking the training phase, poisoning occurs via manipulated local updates at the federated unlearning step, designed so the aggregated “unlearned” global model remains as close as possible to the previously poisoned model—negating the intent of unlearning (Wang et al., 29 Jan 2025).

Mitigation strategies in these scenarios include gradient distribution analysis (e.g., interquartile range filtering, scaling down abnormally large gradients) and cross-round update prediction with distance/direction filtering (UnlearnGuard), which provide provable robustness bounds towards train-from-scratch reference models (Wang et al., 29 Jan 2025). Nonetheless, challenges persist for adaptive and stealthy attacks.

5. Privacy and Information Leakage via Malicious Unlearning

Unlearning introduces novel information leakage vectors:

Unlearning Inversion Attacks: By differencing original versus unlearned models, adversaries (with access to parameters or outputs) can reconstruct feature vectors or infer labels of forgotten samples. Recovery is achieved by optimizing surrogate inputs to maximize the similarity of their gradients to observed model differences; in the black-box setting, output confidence changes upon crafted “probing” samples reveal the class label of the forgotten data (Hu et al., 4 Apr 2024).
Attack on Predictive Uncertainty: Adversaries can manipulate the forget set so as to control the predictive uncertainty of the model, e.g., causing the model to be over- or under-confident on specified inputs without altering labels. The attack uses bi-level optimization incorporating hinge losses for uncertainty control and a proximity-based KL regularizer to avoid suspicious artifacts (Qian et al., 10 Aug 2025). Conventional defenses targeting accuracy or adversarial examples are ineffective, and uncertainty-oriented attacks can evade calibration-monitoring detection.

Empirical analysis shows that these attacks are effective in both white-box and black-box settings and affect uncertainty metrics (such as ECE, ACE, Brier Score) much more than traditional misclassification-based attacks, indicating a significant privacy risk not measured by standard performance metrics (Hu et al., 4 Apr 2024, Qian et al., 10 Aug 2025).

6. Stealth, Benign-Looking, and Clean Unlearning Attacks

Recent works highlight attacks that evade both automatic detection and classical defenses:

Unlearning Usability Attack: Adversaries inject a compact set of synthetic samples—constructed via data condensation and embedding matching (e.g., via MMD)—that encapsulate high information content while appearing benign for training. Upon unlearning, these “informative benign data” cause a severe performance collapse (test accuracy drop up to 50%) despite being only 1% of the training set and passing all standard poisoning checks (Ma et al., 6 Jul 2024).
Clean Unlearning Backdoor Attack: Rather than poisoning the forget-set, a distributed, frequency-domain-based trigger is embedded across multiple classes in training. When a clean unlearning request is made (non-poisoned samples only), removal induces gradient realignment, amplifying the target backdoor association. This approach renders the attack stealthy: both learning and unlearning appear benign at the data level, but the system is highly vulnerable at the decision boundary level (Arazzi et al., 14 Jun 2025).
Stealthy Attacks in Instruction/LLM Unlearning: By increasing the frequency of benign tokens in forget requests, attackers cause the model to associate common, innocuous tokens with unlearning signals. Afterward, normal user queries containing such tokens trigger undesirable unlearning behaviors or denials, degrading overall utility without explicit data or label poisoning (Ren et al., 31 May 2025).

These strategies evade hash/membership verification and are undetectable by classic defenses, highlighting a categorical need to reconsider defense strategies beyond perturbation- or membership-centric frameworks.

7. Defense Methodologies and Open Challenges

Mitigating malicious unlearning attacks demands defenses that address both procedural and mechanistic vulnerabilities:

Algorithmic Safeguards: Data-independent, fixed-complexity unlearning updates, robust influence monitoring, increased regularization, or closed-form unlearning algorithms mitigate data-dependent triggers and high-influence poisons (Marchant et al., 2021).
Adversarial Latent Training: In LLMs, latent adversarial unlearning leverages a min–max outer-inner optimization that strengthens the model’s robustness to adversarial suffixes or latent reactivation queries. AdvNPO and AdvGA extend standard preference optimization with adversarial perturbations in the latent space (Yuan et al., 20 Aug 2024).
Gradient and Update Filtering in FL: Federated approaches implement statistical filtering (IQR), historical consistency (UnlearnGuard), and magnitude/direction analysis to prevent update-based poisoning during distributed unlearning (Wang et al., 29 Jan 2025, Chen et al., 21 Jan 2025).
Utility Restoration by Healing: “Healing” mechanisms proactively substitute removed data with highly similar instances drawn from a reserved pool (spare set or twins), using Euclidean, cosine, or Mahalanobis distance matching. This mitigates adversarial utility degradation but presumes the feasibility of constructing such pools and choosing optimal matches within threshold δ (Jasiorski et al., 15 Jul 2025).
Cryptographic and Certificate-based Approaches: Differential privacy during both training and unlearning, cryptographic commitments, and zero-knowledge proofs (ZKPs) supporting request verification and tamper-proof logging are advocated in high-stakes applications (Brodzinski, 29 Sep 2024).
Scope Control and Objective Augmentation: Scope-aware unlearning introduces auxiliary loss terms that constrain the unlearning effect only to the explicit scope of forgotten knowledge, preventing spurious utility degradation through associative triggers in LLMs (Ren et al., 31 May 2025).

The principal open challenges include constructing scalable, privacy-preserving, and resilient unlearning protocols robust to both label-manipulating and uncertainty-manipulating attacks; defining formal verification methods to distinguish between legitimate and malicious forget requests under strict privacy regulation; and understanding the trade-off between unlearning efficacy, computational/vulnerability risks, and utility preservation, especially under adversarial settings and deployment in federated or MLaaS contexts (P. et al., 26 Mar 2025, Ma et al., 6 Jul 2024, Qian et al., 10 Aug 2025).

This synthesis underscores that the security of machine unlearning is not only a technical or regulatory challenge but also a fertile adversarial landscape. The technical innovations fueling the efficacy and privacy compliance of MU mechanisms simultaneously introduce complex and often underappreciated vulnerabilities that remain at the research frontier.