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Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

T0 review · 5 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Machine unlearning leaves persistent, learnable fingerprints in large language models: a simple supervised classifier can tell an unlearned model from its original with over 90% accuracy on held-out prompts, even when the prompts are…

desk verdict Real finding, oversold abstract, and a missing control for generic fine-tuning—still worth refereeing. read the letter →

arxiv 2506.14003 v5 pith:B2Y57GN4 submitted 2025-06-16 cs.LG

Yiwei Chen , Soumyadeep Pal , Yimeng Zhang , Qing Qu , Sijia Liu This is my paper
classification cs.LG
keywords machineunlearninglargelanguagemodelstracedetectionspectralfingerprintsmodeloutputforensicspre-logitactivationsWMDPbenchmarkprivacyvulnerability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to show that machine unlearning in large language models is detectably imperfect: after a model has been unlearned, it carries 'unlearning traces' that separate it from the original model. The authors demonstrate that a simple supervised classifier can identify an unlearned model from its generated responses alone, with over 90% accuracy on many held-out prompt sets, and with near-perfect accuracy when the classifier reads pre-logit activations instead of text. Detection works even on prompts unrelated to the forgotten knowledge, which matters because an adversary cannot be assumed to know what was erased. The paper further claims that these traces live in low-dimensional, learnable structures in activation space and that larger models leave stronger fingerprints. If the claim holds, unlearning cannot be treated as making a model behaviorally indistinguishable from one that never learned the data.

What carries the argument

The load-bearing objects are 'unlearning traces'—the measurable behavioral and representational differences between an unlearned model and its original—and the 'spectral fingerprints' used to explain them. Detection is carried by a supervised binary classifier: for text, responses are embedded with LLM2Vec and fed to a two-layer MLP; for activations, pre-logit vectors from each generated token are compressed through a four-layer MLP. The mechanistic explanation is spectral fingerprinting: the paper centers the activation matrix of 3,000 MMLU responses, performs SVD, and projects activations onto the first few right singular vectors, finding that unlearned models shift along these principal directions at the layers targeted by unlearning (for RMU, the down-projection or gate-projection sublayers of modified transformer blocks; for NPO, the final RMS-normalized layer). To explain why final pre-logit activations also classify well even when their SV1 projection looks unchanged, the paper invokes the residual-stream architecture of transformers—the additive pathway through transformer layers—showing that earlier-layer fingerprints contribute nonlinearly to the final representation and that supervised UMAP reveals a separable low-dimensional manifold. This manifold is what the activation-based MLP learns.

What would settle it

Train the same text and activation classifiers on models unlearned with a sweep of hyperparameter strengths (e.g., varying RMU's scaling factor c or NPO's learning rate) while holding forget efficacy fixed on WMDP; if any configuration that still suppresses the target knowledge also drives detection accuracy on forget-irrelevant MMLU prompts down to chance, then the 'persistent fingerprint' claim is falsified.

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Extended reading notes

Core claim

The central claim is that unlearning leaves persistent 'fingerprints' in both model behavior and internal representations, detectable from observable outputs even under forget-irrelevant prompts. Concretely, a two-layer MLP trained on a mixture of forget-related (WMDP) and forget-irrelevant (MMLU) responses classifies RMU-unlearned models versus their originals with 90–96% accuracy on text across many settings, while the same classifier on pre-logit activations reaches 98–100%; for NPO-unlearned models, text alone gives 99–100% accuracy. The paper attributes the activation-based success to spectral fingerprints: SVD projection of centered activations onto top right singular vectors reveals distributional shifts at the layers RMU directly modifies (e.g., FFN down-projection sublayers) and, for NPO, at the final normalized layer. Where no linear shift is visible in final activations, supervised UMAP still separates the two classes, suggesting a low-dimensional nonlinear manifold that propagates the fingerprint from intermediate layers to the output through the residual stream. Larger models show stronger and more widespread spectral shifts, consistent with their higher detection accuracy.

Load-bearing premise

The claim that unlearning leaves persistent traces rests on the assumption that the fingerprints are intrinsic to the unlearning task rather than artifacts of the chosen hyperparameters, since the NPO results coincide with visibly degraded output and the paper does not test whether detectability persists across unlearning strengths that preserve output quality.

Editorial extensions

If this is right

  • Unlearned models cannot be assumed to be behaviorally indistinguishable from models that never saw the forgotten data, since a lightweight classifier can tell them apart from text alone.
  • Detection survives forget-irrelevant prompts: an adversary does not need to know or guess the forget set to learn that a model has been unlearned.
  • Access to pre-logit activations makes detection near-perfect across model families and test sets, so open-weight deployments that expose hidden states are substantially more vulnerable than black-box text-only settings.
  • Larger models are more, not less, detectable, meaning scale does not hide unlearning traces.
  • The traces are robust across training-data mixes, decoding temperatures sampled at test time, and paraphrasing, indicating they are not prompt-specific or sampling artifacts.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial extension: if the spectral fingerprints are as persistent as reported, a natural defense would be to regularize unlearned activations back onto the original model's principal subspace; this paper does not test that countermeasure, but the SVD-based analysis makes it a direct, testable next step.
  • Editorial extension: because NPO's near-perfect text detectability coincides with severe output degradation (repeated punctuation, raw token fragments), part of what the detector measures may be a utility collapse rather than a knowledge-removal signal; a detector trained on NPO variants with preserved output quality would separate these two explanations.
  • Editorial extension: the same detection pipeline could be repurposed as an auditing tool for unlearning claims—if a supposedly unlearned model still classifies as 'unlearned' versus a retrained-from-scratch baseline, the removal may not meet exact-unlearning standards.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes and studies "unlearning trace detection": given a model that may have undergone LLM unlearning, a supervised classifier is trained to distinguish the unlearned model from its original checkpoint using either generated text (via LLM2Vec embeddings) or pre-logit activations. The unlearning methods are RMU and NPO applied to the WMDP benchmark, evaluated on four instruction-tuned LLMs (Zephyr-7B, Llama-3.1-8B, Qwen2.5-14B, Yi-34B-Chat), with held-out test prompts from WMDP, MMLU, and UltraChat. The main empirical claims are that pre-logit activation classifiers detect RMU unlearning near-perfectly even on forget-irrelevant inputs; that text-based detection is strong for NPO (near 99-100%) and scales with model size for RMU; and that unlearning leaves low-dimensional spectral fingerprints in intermediate and final activations, which UMAP projections make separable. The paper also reports cross-model classifier generalization, training-regime ablations, an 8-way source-model/unlearning classification, forget-data detection, and robustness checks under Pass@K and paraphrasing.

Significance. If the central claim is taken at face value, the paper identifies a practically relevant forensic vulnerability: an adversary with query access could infer that a model was unlearned and then invest in relearning/jailbreaking attacks. The empirical scope is a genuine strength: four LLM families, two representative unlearning paradigms, three test distributions, released code, fixed seeds, and multiple ablations (training regimes, mixing ratios, encoder choices, classifier depths, cross-model transfer, Pass@K/paraphrase robustness). The spectral and UMAP analyses provide an interpretable mechanism for the activation-based results, and the separation between RMU's subtle response-level traces and NPO's strong traces is clearly documented. However, the paper's headline claims go beyond what the experimental design establishes, because every positive result compares an unlearned model only with its own original and no control for generic non-unlearning updates is included; additionally, the NPO results are confounded with severe output collapse.

major comments (5)
  1. [Sec. 4, Tables A3-A6] Every positive detection result compares an unlearned model only with its own original checkpoint; there is no control condition in which the same pipeline is applied to a model that received a comparable but non-unlearning update (e.g., standard fine-tuning on unrelated text, retain-set-only training, or random parameter perturbation). Since any parameter update can shift activation distributions and output text, the reported separability does not by itself establish that the detected signal is specific to unlearning, which is the paper's central claim. Please add such controls and report their detection accuracies; if benignly updated models are also classified as "unlearned," the conclusion should be reframed as generic update drift rather than unlearning-specific fingerprints.
  2. [Sec. 4, NPO results; Tables A4, A7-A9] The near-perfect text-based NPO detection is confounded with catastrophic output degradation. Table A9 reports ROUGE-L of 0.0139 (WMDP) and 0.0115 (MMLU) between NPO and original responses; Tables A7-A8 show raw token fragments and repeated punctuation; and Table A1 shows Yi-34B MMLU utility dropping from 72.35% to 41.54%. The paper itself states (Sec. 4) that NPO detection "arises from the degradation of responses in NPO-unlearned models." As presented, the NPO results demonstrate detection of model damage under one hyperparameter setting, not persistence of a subtle residual trace. Please add an NPO run with utility-preserving regularization or early stopping and show whether text-based detection persists; otherwise limit the NPO claims to the collapsed-output regime.
  3. [Abstract; Sec. 4, Fig. 3, Table A3] The unqualified claim of "over 90% accuracy even under forget-irrelevant inputs" is contradicted by the paper's own numbers: text-based RMU detection on Zephyr-7B is 53.68% on MMLU and 50.14% on UltraChat, and even Llama-3.1-8B reaches only 67.60% on UltraChat. The >90% figures hold for pre-logit activation classifiers, for NPO, or for specific larger RMU models. The abstract and introduction should qualify the claim by output modality and model/method.
  4. [Abstract; Sec. 4, Fig. 3] The conclusion that "larger LLMs exhibit stronger detectability" is not supported because the four studied models differ simultaneously in family, architecture, pretraining data, and instruction-tuning data (Zephyr-7B, Llama-3.1-8B, Qwen2.5-14B, Yi-34B-Chat). A cross-family comparison confounds scale with these factors; a within-family size sweep would be needed for the stated scaling claim, or the claim should be rephrased as an observation about the specific studied models.
  5. [Appendices A and B] Only one hyperparameter configuration per unlearning method is used (learning rates, gamma, RMU layers/c, NPO steps, and seed 42), so the paper does not show that detectability persists across unlearning strengths. The word "persistent" in the abstract therefore exceeds the evidence: a weaker RMU scaling factor or a less aggressive NPO schedule could reduce or eliminate the separation. Please either sweep unlearning strength and report detection accuracy versus the forget/utility trade-off, or soften the persistence claim and note the single-configuration limitation.
minor comments (6)
  1. [Appendix D, after Table A8] The text claims that "even though both unlearned models produce correct answer selections" on the MMLU-style forget-irrelevant prompt, but Table A7 shows the NPO Yi-34B response as a sequence of <|unused|> tokens and Table A8 shows the NPO Zephyr response as garbled punctuation; the claim is inconsistent with the tables.
  2. [Appendix H, Table A10] For Qwen2.5-14B, the NPO entries for Entropy, JS divergence, Top-k mass, and Max prob are identical to the Original entries (e.g., 4.107->4.565->4.107), which is inconsistent with the near-perfect NPO detection reported in Table A4 and suggests a copying or aggregation error.
  3. [Appendix A] The model list says "Qwen2.5-7B" while the rest of the paper (Sec. 4, Tables A1, A3, etc.) uses Qwen2.5-14B; please correct the typo.
  4. [Appendix N.1] The sentence about cross-modal traces ends with an orphaned citation "(Zhang et al., 2025)" followed immediately by "Finally,"; either integrate the citation into a complete sentence or remove it.
  5. [Appendix B] The description of cross-dimension transfer says dimensionality reduction or zero padding was "attempted" but no details or results are given for which entries of Table 2 used which method; please clarify.
  6. [Sec. 3, Fig. 2] The statement that the PPL distributions for MMLU are "indistinguishable" is made without a statistical test; a KS test or overlap measure would make the motivating claim precise.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: unlearning-trace detection is an empirical supervised classification evaluated on held-out prompts, with no load-bearing step that reduces to its own inputs by construction.

full rationale

The paper's central claim is an empirical finding: a supervised classifier can distinguish an unlearned LLM from its original using textual responses or pre-logit activations. This is not a derivation from an assumed conclusion. The training set Sfg is an equal mixture of WMDP and MMLU responses, and the test sets are explicitly disjoint from the training prompts. UltraChat is a fully separate dataset, and the activation-based classifiers additionally transfer across model families, providing independent support. The unlearning methods RMU and NPO are taken from external prior work, not from the authors' own prior results, and the detection pipeline does not fit any parameter to the test labels. The spectral fingerprint analysis in Section 5 is descriptive: it shows distributional shifts in activation projections after unlearning and uses UMAP to illustrate separability. This explains the classifier's success rather than defining the success into existence. The paper also candidly documents that NPO-unlearned models suffer degraded output quality, with ROUGE-L drops to 0.0139 and token-level garbling in Tables A7-A9. That means part of the NPO detection signal may be model collapse rather than a subtle residual trace, but this is a validity or confound concern, not circularity: the detection result is still measured against held-out outputs rather than being guaranteed by construction. The limitations section acknowledges that trace persistence across larger models, modalities, and domain-specialized models is not established. Overall, no prediction in the paper is equivalent to a fitted input, no load-bearing argument rests on a self-citation, and no known result is merely renamed. The appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The paper does not fit any parameter to produce its main detection results; the unlearning hyperparameters (RMU c, gamma, NPO learning rates) are inherited from prior methods and set per model in Appendix A, not tuned to maximize detectability. The central claim rests on the four domain assumptions listed above, the most fragile being that pre-logit activations are available to the adversary and that the traces observed are not artifacts of the specific unlearning configurations.

assumptions (4)
  • domain assumption Pre-logit activations are accessible through standard inference in open-weight LLM deployments
    Sec. 3: the threat model assumes gray-box access to pre-logit activations, which is realistic for open-weight models but not for API-only deployments. This assumption is required for the near-perfect forget-irrelevant detection results.
  • standard math The SVD and UMAP projections faithfully represent class separation in activation space
    Sec. 5 uses SVD on the centered activation matrix and supervised UMAP to visualize fingerprints; these are standard exploratory techniques.
  • domain assumption LLM2Vec embeddings capture behavior idiosyncrasies sufficient for output classification
    Sec. 4 adopts LLM2Vec from prior work; the paper tests other encoders in Appendix J but the main results use LLM2Vec.
  • domain assumption WMDP is an appropriate unlearning benchmark and the unlearning implementations are faithful to RMU and NPO
    Sec. 3 and Appendix A: the paper relies on the WMDP benchmark and its own implementations of RMU and NPO; any divergence from the original methods could affect trace characteristics.
invented entities (1)
  • Unlearning trace / fingerprint independent evidence
    purpose: Conceptual construct naming the detectable behavioral and representational residue of unlearning in a model
    The paper anchors this construct in falsifiable observations: supervised classifiers achieve high held-out accuracy and spectral projections show distributional shifts, so the entity is not merely postulated.

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Cite this review

Pith. "Pith review of Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs." pith.science (2026). https://pith.science/paper/B2Y57GN4

@misc{pith2026250614003,
  author       = {Pith},
  title        = {Pith review of: Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B2Y57GN4}},
  note         = {Machine review of arXiv:2506.14003}
}
read the original abstract

Machine unlearning (MU) for large language models (LLMs), commonly referred to as LLM unlearning, seeks to remove specific undesirable data or knowledge from a trained model, while maintaining its performance on standard tasks. While unlearning plays a vital role in protecting data privacy, enforcing copyright, and mitigating sociotechnical harms in LLMs, we identify a new vulnerability post-unlearning: unlearning trace detection. We discover that unlearning leaves behind persistent "fingerprints" in LLMs, detectable traces in both model behavior and internal representations. These traces can be identified from output responses, even when prompted with forget-irrelevant inputs. Specifically, even a simple supervised classifier can determine whether a model has undergone unlearning, using only its prediction logits or even its textual outputs. Further analysis shows that these traces are embedded in intermediate activations and propagate nonlinearly to the final layer, forming low-dimensional, learnable manifolds in activation space. Through extensive experiments, we demonstrate that unlearning traces can be detected with over 90% accuracy even under forget-irrelevant inputs, and that larger LLMs exhibit stronger detectability. These findings reveal that unlearning leaves measurable signatures, introducing a new risk of reverse-engineering forgotten information when a model is identified as unlearned, given an input query.

Figures

Figures reproduced from arXiv: 2506.14003 by the authors.

Figure 1
Figure 1. Schematic overview of unlearning trace detection. Original and unlearned models are queried with forget-relevant and irrelevant prompts, and observable model outputs are collected, either discrete textual responses or continuous pre-logit activations. A lightweight classifier is then trained to predict whether the model has undergone unlearning. Detectable behavioral shifts in both outputs and internal representatio… view at source ↗
Figure 2
Figure 2. GPT-2 perplexity distributions for Yi-34B vs. RMU-unlearned responses. (a) WMDP forget queries (3,000 samples). (b) MMLU forget￾irrelevant queries (3,000 samples). Where perplexity quantifies fluency and predictability. To further analyze this behav￾ior, we examine the perplexity (PPL) distributions of the orig￾inal and unlearned models on both prompt types, using GPT￾2 to compute PPL, a standard proxy for fluency a… view at source ↗
Figure 3
Figure 3. Radar charts of unlearning trace detection accuracy across four source LLMs evaluated on three test sets (WMDP, MMLU, UltraChat). Panel (a) reports results for models unlearned on WMDP using RMU, while panel (b) reports results for models unlearned on WMDP using NPO. Each axis corresponds to a detection setting defined by a source model A and a test dataset B, where the classifier is trained on outputs from model A … view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Projection of activations from various layers for 3000 responses to MMLU onto the top right singular vectors (denoted as SV1) for both original and unlearned models. Here, Li.D_PROJ refers to activations extracted from the down-projection sublayer of the FFN in the i-t…
Figure 5
Figure 5. Figure 5: Projection of the final￾layer normalized activations from 3,000 MMLU responses onto the first right singular vector (SV1) for the origi￾nal LLaMA3.1-8B model and its NPO￾unlearned counterpart. Spectral “fingerprints”: Definition and method. We define spectral fingerpri…
Figure 6
Figure 6. Figure 6: UMAP projections of FINAL layer activations on MMLU prompts, comparing the original and RMU-unlearned Zephyr-7B models. As shown in [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Forget-data detection accuracies across dif￾ferent unlearned models, using NPO or RMU applied to various source model types. An extended use case: Forget-data detection. While our paper primarily focuses on detecting whether a model has undergone unlearning, a natural …
Figure 8
Figure 8. Figure 8: Confusion matrices for model–unlearning pair classification. Rows denote the true classes (i.e., original or unlearned versions for each LLM type), and columns indicate the predicted classes. Diagonal entries correspond to correct predictions, while off-diagonal entrie…

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.