Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs

## Abstract

Abstract Large language models (LLMs) are increasingly trained on tabular data, which, unlike unstructured text, often contains personally identifiable information (PII) in a highly structured and explicit format.
As a result, privacy risks arise, since sensitive records can be inadvertently retained by the model and exposed through data extraction or membership inference attacks (MIAs).
While existing MIA methods primarily target textual content, their efficacy and threat implications may differ when applied to structured data, due to its limited content, diverse data types, unique value distributions, and column-level semantics.
In this paper, we present Tab-MIA, a benchmark dataset for evaluating MIAs on tabular data in LLMs and demonstrate how it can be used.
Tab-MIA comprises five data collections, each represented in six different encoding formats.
Using our Tab-MIA benchmark, we conduct the first evaluation of state-of-the-art MIA methods on LLMs fine-tuned with tabular data across multiple encoding formats.
In the evaluation, we analyze the memorization behavior of pretrained LLMs on structured data derived from Wikipedia tables.
Our findings show that LLMs memorize tabular data in ways that vary across encoding formats, making them susceptible to extraction via MIAs.
Even when fine-tuned for as few as three epochs, models exhibit high vulnerability, with AUROC scores approaching 90% in most cases.
Tab-MIA enables systematic evaluation of these risks and provides a foundation for developing privacy-preserving methods for tabular data in LLMs.

## 1 Introduction

Large language models (LLMs) have emerged as core components of modern artificial intelligence (AI) systems due to their advanced language understanding and generation capabilities, supporting applications ranging from scientific discovery to natural, human-like interaction Zhao2023b; Wei2022b.
These models are typically trained on vast and diverse datasets comprised of web content, academic publications, code repositories, and, increasingly, structured tabular data from organizational and public databases Fang2024; Paranjape2023.

Tabular data, such as financial spreadsheets and electronic health records, serve as the basis of data-driven workflows in healthcare, finance, public administration, and other sectors.
Their structured format—rows as entities and columns as attributes—helps both humans and machine learning models learn patterns, relationships, and statistical properties efficiently.
While LLMs have traditionally been developed and applied for unstructured textual data, recent research reflects the growing interest in adapting LLMs to effectively process such structured inputs by representing tables in text-like formats herzig2020tapas; yin2020tabert; Narayan2022.
This shift extends LLMs’ capabilities to reasoning tasks involving both unstructured and structured data.

However, incorporating tabular data in the training set of an LLM poses unique challenges and risks.
Tabular data may contain personally identifiable information (PII), commercially sensitive material, or domain-specific details that are not intended for broad dissemination Yeom2018; Zeng2024.
LLMs, including those trained on structured data, can memorize and leak sensitive records
since they are vulnerable to *membership inference attacks* (MIAs), in which an adversary attempts to determine whether a particular record was included in the model’s training set Shokri2017; Carlini2022a.
These attacks typically rely on subtle differences in the model’s behavior when queried with examples it has seen during training compared to unseen examples cao2023comprehensive; hu2022membership.

MIAs on LLMs have been studied extensively in the context of textual data, where researchers typically analyze confidence scores at the sentence- or paragraph-level to detect training set membership 10888320; duan2024membershipinferenceattackswork.
These studies generally assume that the models were trained on free-form, unstructured text—such as natural language sentences and documents.
Tabular data, which is often heterogeneous, may exhibit skewed value distributions and contain explicit column-level semantics, making both the design of MIAs and the development of effective defenses more challenging Borisov2022a; Fang2024.

Recent work has shown that generative models can effectively interpret, transform, and synthesize tabular data zha2023tablegptunifyingtablesnature, and other studies have shown that the choice of table encoding format—such as JSON, HTML, Markdown, or Key-Value Pair—can impact model performance Fang2024.
However, the studies primarily focused on improving task accuracy and generalization, with comparatively little research attention given to understanding memorization risks or the potential exploitation of tabular data through MIAs.
Prior research has shown that LLM performance is highly sensitive to the input format: for instance, DFLoader and JSON have been found effective for fact-finding and transformation tasks singha2023tabular, while HTML and XML outperform plain-text formats like CSV or X-separated values in table QA and field-value prediction sui2023evaluating; sui2023gpt4table.
This performance gap is often attributed to the prevalence of web-based markup (e.g., HTML) in the pretraining data of models like GPT-3.5 and GPT-4 openai2024gpt4technicalreport, making them more effective at processing tables serialized in familiar, structured input styles.

In this paper, we present Tab-MIA, a benchmark dataset specifically designed to evaluate MIAs against LLMs fine-tuned on tabular data.
Tab-MIA includes five collections consisting of tables, each represented in six different textual encoding formats.
To our knowledge, this is the first comprehensive evaluation of MIAs on LLMs trained with structured tabular data across multiple encoding formats.
We systematically examine the sensitivity of LLMs to MIAs under various conditions, including after fine-tuning with a limited number of epochs on tabular datasets, and in the pretrained setting, where the pretrained model is assumed to be trained on a tabular subset of Wikipedia.
In our experiments, various configurations of models, data encodings, and training epochs are examined.

One evaluation shows that LLMs can memorize tabular data to a degree sufficient for effective membership inference.
Notably, even when fine-tuned for as few as three epochs, attack success rates can be high, with AUROC scores approaching 90%.
We also observed partial transferability of attacks across encoding formats, indicating that adversaries may succeed without exact knowledge of the specific format used in training.
These findings highlight the need for privacy-preserving training practices when training LLMs on structured data.
Our work broadens the scope of MIA research, which has largely not focused on structured data, and highlights the need for privacy-preserving strategies designed to address the challenges posed by the unique characteristics of tabular formats.

The main contributions of our paper are (1) we present the first benchmark dataset to evaluate MIAs against LLMs trained on tabular data; (2) we conduct the first evaluation of state-of-the-art (SOTA) MIAs on LLMs fine-tuned with tabular data across multiple encoding formats; and (3) we analyze the memorization behavior of recent SOTA LLMs on structured data derived from Wikipedia tables.

## 2 Related Work

LLMs have demonstrated promising capabilities in handling structured data across tasks such as tabular representation, question answering, and data generation.
In this section, we review prior work focused on: (1) MIAs on LLMs, (2) encoding-strategy-based methods for using tabular data with LLMs, and (3) emerging risks when incorporating structured data into LLM training sets.

### 2.1 Membership Inference Attacks on LLMs

MIAs shokri2017membership aim to determine whether a given sample $x$ is part of a training set $D_{\text{train}}$ of a model $f$. An attacker receives a sample $x$ and the trained model $f$, and applies an attack model $A$ to classify $x$ as a member $A\bigl{(}f(x)\bigr{)}=1$, or non-member otherwise.
MIAs against LLMs have received increasing attention carlini2022membership; mattern2023membership; zhang2024pretraining.
Recent studies categorized MIA methods into *reference-based* and *reference-free* approaches antebi2025tag.
Reference-based attacks primarily rely on training shadow models to mimic the target model’s behavior.
A prominent example is LiRA carlini2022membership, which estimates the likelihood ratio of a sample’s loss under two model output distributions, one where the sample was included in training and one where it was not.
While often effective, such methods are computationally expensive, as they require training multiple shadow models and calibrating their outputs.

Reference-free attacks rely on confidence metrics derived from a single model’s output. The *LOSS attack (PPL)* yeom2018privacy infers membership based on the model’s loss value relative to a fixed threshold.
The *Zlib attack* carlini2021extracting uses the ratio of log-likelihood to its Zlib compression length, while the *Neighbor attack* mattern2023membership examines perplexity shifts by substituting words with similar tokens generated by an auxiliary model.
More recently, *Min-K%* shi2024detectingpretrainingdatalarge and *Min-K%++* zhang2024min were shown to improve attack efficiency by averaging the lowest probability tokens, with Min-K%++ further applying normalization over log probabilities.
In addition, the authors of *RECALL* xie2024recall, *DC-PDD*zhang2024pretraining, and *Tag&Tab* antebi2025tag introduced more advanced strategies that improve MIA performance on LLMs compared to other methods.

### 2.2 LLMs and Tabular Data

Many enterprise and scientific datasets consist of tabular data, which is composed of rows and columns of structured attributes LLMsOnTabularSurvey. Traditional tree-based models such as XGBoost chen2016xgboost and LightGBM ke2017lightgbm have long been dominant for tabular data tasks, particularly due to their effectiveness on small-to-medium sized datasets and strong inductive biases for numerical features gorishniy2021revisiting.
However, recent research has explored the use of LLMs for tabular data applications, including classification, regression, data augmentation, data generation, and table-based QA h2023tabllm; sui2023gpt4table; Borisov2023TabData; ding2023parameter.
LLMs use their strengths, such as in-context generalization and instruction following, to better understand serialized tables, handle numeric or categorical features, and produce flexible outputs, even in scenarios that conventional machine learning models struggle with.
LLMs support table-based tasks such as *Table QA*, *fact verification*, and *Text2SQL* chen2023; ye2023dater.
Earlier methods like TAPAS herzig2020tapas and TaBERT yin2020tabert used specialized encoders, while modern LLMs process table queries by serializing them as text or leveraging external code calls sui2023gpt4table; Liu2023TimeJarvix.

A central challenge in applying LLMs to tabular data lies in how to represent structured tables in a text-based input format suitable for transformer architectures.
Prior work proposed serializing tables using various strategies, including natural language templates, JSON, Markdown, HTML, and Key-Value Pair LIFT; Slack2023TABLET; Jaitly2023SerializeLM.
The choice of serialization affects not only model performance but also how well the structure and semantics of the table are preserved.
For example, Hegselmann et al. h2023tabllm proposed TabLLM, a method that systematically evaluates multiple table encoding formats. Their evaluation showed that simple natural language patterns, such as “The [column] is [value],” can yield strong performance across a range of tabular classification tasks, likely due to their alignment with the model’s pretraining distributions.

Although LLMs can process moderately sized serialized tables, handling very large tables remains challenging due to the transformers’ fixed-length context window.
This restricts the amount of tabular data a model can process in a single input, making it difficult to handle large tables without partitioning or truncation sui2023gpt4table; sui-etal-2024-tap4llm, which can disrupt the model’s ability to capture long-range dependencies and global relationships across rows and columns.
To address this, compression-based frameworks like SHEETENCODER dong2024sheetencoder have been developed.
SHEETENCODER reduces the size of table inputs by selecting structural anchors, applying inverted-index translation to remove redundancy, and aggregating similar numeric fields, thereby preserving important relational information while remaining within context window limits.

While prior research optimized table serialization for accuracy and scalability, it largely overlooked the privacy implications of different serialization strategies.
Tab-MIA fills this gap by systematically evaluating how encoding choices affect memorization and membership inference risk.

### 2.3 Privacy Risks When Training LLMs with Structured Data

Integrating structured tabular data in LLMs offers substantial benefits for data-driven reasoning, enabling models to combine natural language understanding with structured data processing gorishniy2021revisiting; LLMsOnTabularSurvey.
However, it also introduces distinct privacy and security risks that differ from those encountered when training on unstructured text.
A critical vulnerability stems from the fact that tabular datasets often contain sensitive information, such as personal identifiers, financial records, or medical details, that are highly susceptible to memorization carlini2022membership; lukas2023analyzing.
Even seemingly benign fields, when combined, can form distinctive patterns that compromise individuals’ privacy.
Once such information is memorized by a model, it may be vulnerable to extraction via MIAs, exposing individual records or sensitive attributes carlini2021extracting.

While MIAs have been widely studied in the context of unstructured text corpora, such as books, Wikipedia, and web documents xie2024recall; antebi2025tag, there is a notable lack of benchmark datasets for structured tabular data.
Existing MIA benchmark datasets like BookMIA, WikiMIA shi2024detectingpretrainingdatalarge, and MIMIR duan2024membershipinferenceattackswork have helped characterize MIA risks in textual domains, but they do not consider the unique structural format that is present in tabular datasets.
This gap is particularly concerning in enterprise environments, where structured tables often encode sensitive financial, medical, or operational data.
In these settings, the structure itself can influence memorization patterns, making it essential to develop evaluation frameworks that can assess how different aspects of table formatting and encoding affect privacy risks.

In light of these risks, our proposed benchmark Tab-MIA is designed to systematically study membership inference on tabular datasets across a range of different table encoding formats and LLM configurations.

## 3 Construction of the Tab-MIA Benchmark

Our goal in constructing the Tab-MIA benchmark is to facilitate the systematic evaluation of how MIAs can be applied to extract the tabular data used to fine-tune LLMs.
Unlike text-based benchmarks, which focus on sentences or paragraphs, tabular benchmarks must handle heterogeneous types of columns, various encoding formats, and repeated patterns across structurally similar tables.
By creating a controlled yet realistic set of tables from publicly available datasets, Tab-MIA enables systematic evaluation of how different table-encoding strategies affect vulnerability to MIAs.
We use it to analyze how different formats affect memorization and attack performance.

Figure: Figure 1: The same 3$\times$2 table snippet serialized into the six encoding formats used in the Tab-MIA benchmark: (a) JSON, (b) HTML, (c) Markdown, (d) Key-Value Pair, (e) Key-is-Value, and (f) Line-Separated (CSV-like).

### 3.1 Datasets

The benchmark integrates real-world datasets widely used in language modeling and tabular machine learning, covering diverse structural characteristics and application domains.
To enable systematic evaluation of MIA risks in LLMs fine-tuned using tabular data, Tab-MIA includes datasets representing both short-context and long-context tables.

Short-context tables are derived from QA benchmarks in which each instance originally pairs a question with a supporting table.
In our setting, we discard the question text and retain only the *unique tables* to focus on tabular memorization effects.
We include WikiTableQuestions (WTQ) pasupat2015compositionalsemanticparsingsemistructured, WikiSQL zhong2017seq2sqlgeneratingstructuredqueries, and TabFact 2019TabFactA.
Long-context tables are derived from structured tabular benchmarks frequently used in fairness, regression, and privacy studies.
We include the Adult (Census Income) dataset adult_2 and the California Housing dataset pace1997sparse.
Due to input length limitations inherent to LLMs, long tables are segmented into row-wise *chunks* sized to fit within the model’s context window while preserving structural coherence.
A full summary of the datasets used in Tab-MIA, including record counts before and after filtering, feature dimensionality, context type (short or long), and data sources, is provided in Table [1](https://arxiv.org/html/2507.17259v1#S3.T1).

**Table 1: Summary of datasets used in Tab-MIA.**
| Name | Short/Long | # Records | # After Filter | # Features | Based On |
| --- | --- | --- | --- | --- | --- |
| WTQ | Short | 2,108 | 1,290 | $\geq$5 | Wikipedia |
| WikiSQL | Short | 24,241 | 17,900 | $\geq$5 | Wikipedia |
| TabFact | Short | 16,573 | 13,100 | $\geq$5 | Wikipedia |
| Adult (Census Income) | Long | 48,842 | 2,440 | 15 | US Census |
| California Housing | Long | 20,640 | 1,030 | 10 | US Housing Survey |

### 3.2 Data Preparation

To construct the Tab-MIA benchmark, we processed each of its constituent datasets using a standardized pipeline designed to ensure data quality, consistency, and experimental control.
First, we perform a filtering and deduplication step to ensure that each table appears only once in the benchmark, preventing artificial inflation of the memorization signal due to repeated exposure.
Next, we apply context-specific processing to match the model’s input length constraints.
For *short-context tables*, we filter out any table whose serialized representation in the Line-Separated format exceeds 10,000 characters, removing overly large tables that could dominate training dynamics or introduce truncation artifacts.
To accommodate *long-context tables*, we split each table into chunks of 20 records each to fit within the model’s input length constraints and maintain consistency across samples.

Each resulting table (or table chunk, in the case of long-context tables) is serialized into multiple textual formats to investigate how the encoding style influences memorization.
We use six encoding strategies, each reflecting a different structural abstraction of the table (illustrated in Figure [1](https://arxiv.org/html/2507.17259v1#S3.F1)):

- •
JSON: Encodes each table as a JSON array of objects, where each object corresponds to a row and stores key:value pairs for column entries.
- •
HTML: Renders the table as a structured <table> element using <tr> and <td> tags, preserving the visual and semantic layout.
- •
Markdown: Represents the table using pipe-delimited rows, headers, and alignment markers in plain text.
- •
Key-Value Pair: Flattens each row into a series of “ColumnName: entry” pairs, joined by the | symbol for linearization.
- •
Key-is-Value: Transforms each cell into a natural language phrase of the form “ColumnName is entry,” producing a list of short sentence-like descriptions per row.
- •
Line-Separated: Encodes the table as a plain-text sequence where each row is written on a separate line, with cells joined by a delimiter (e.g., comma or hash), simulating a CSV-like layout without structural tags.

All encoded variants are saved as JSONL files to support reproducible experiments.
Encoding each table in multiple ways enables us to systematically examine whether certain formats result in greater memorization by the model, and whether some styles are inherently more resistant to MIAs.

## 4 Experimental Setup

We evaluate the vulnerability of fine-tuned LLMs to MIA under various configurations of models, data encodings, and training epochs. We fine-tune four SOTA open-weight language models—LLaMA-3.1 8B, LLaMA-3.2 3B grattafiori2024llama3herdmodels, Gemma-3 4B gemmateam2025gemma3technicalreport, and Mistral 7B jiang2023mistral7b—which have diverse training objectives, tokenizer variants, and parameter scales.
All models are trained using QLoRA QLORA, a parameter-efficient fine-tuning (PEFT) method leveraging 4-bit quantized weights, with a batch size of two on a single RTX 6000 GPU.
Unless otherwise specified, models are fine-tuned for three epochs; however, in our analysis of training length, we also explore the effect of varying the number of epochs between one and three.
In each training run, half of the tables are used as member records while the remainder serve as non-members. Additional details on the hyperparameters are provided in Appendix [A.1](https://arxiv.org/html/2507.17259v1#A1.SS1).

To assess the privacy risk, we consider three black-box MIAs: the *LOSS attack (PPL)* yeom2018privacy, which relies on negative log-likelihood scores; the *Min-K% attack* shi2024detectingpretrainingdatalarge, which averages the lowest $k$% token probabilities to identify memorized content; and *Min-K%++ attack* zhang2024min, which normalizes log probabilities before aggregation to examine robustness to length and calibration effects. For each attack, we report two standard metrics, AUROC and TPR@FPR=5% carlini2022membership, measuring detection performance across decision thresholds and under strict privacy constraints, respectively.

We analyze the empirical findings of our experiments using Tab-MIA, to address four key questions.
First, how does varying the number of fine-tuning epochs affect LLMs’ vulnerability to MIAs?
Second, to what extent does the choice of table encoding format impact LLMs’ memorization and vulnerability to MIAs?
Third, to what extent do MIAs remain effective when the encoding format used during detection differs from the format used during model fine-tuning?
Fourth, to what extent do publicly available *pretrained* LLMs memorize public tabular data?

## 5 Results

In this section, we present our empirical findings using the Tab-MIA benchmark to evaluate MIAs on tabular data in LLMs. The results highlight consistent trends in vulnerability driven by fine-tuning duration, encoding format, and model architecture.

### 5.1 Effect of the Number of Fine-Tuning Epochs

MIAs generally rely on the assumption that models are expected to exhibit greater memorization of training data as the number of fine-tuning epochs increases. This motivates examining how the number of fine-tuning epochs impacts privacy leakage for various models and attack methods.
To this end, we fine-tuned each model for 1, 2, and 3 epochs on the tabular datasets included in our benchmark and evaluated the MIAs’ success.
For this experiment, the tables were serialized into the Line-Separated encoding format.

Table [2](https://arxiv.org/html/2507.17259v1#S5.T2) presents the results for the *Min-K++ 20.0%* MIA for each of the datasets.
We observe a consistent and substantial increase in vulnerability as the number of fine-tuning epochs grows. This trend holds across all models and datasets.
The effect is especially pronounced in short-context datasets, particularly on the WTQ dataset, where AUROC scores reach as high as 97.7% with Mistral 7B after three epochs and exceed 89.6% across all models.
In contrast, long-context datasets exhibit more moderate vulnerability. For example, on the Adult dataset, the highest AUROC is 71.5% with Mistral 7B, and on California Housing, the highest result is 87.8% with LLaMA-3.1 8B.

**Table 2: AUROC scores for the Min-K++ 20.0% MIA on each dataset, evaluated on tables encoded in the Line-Separated format, as a function of the number of fine-tuning epochs. Bold values highlight the best-performing dataset per row.**
| Model | # Epochs | Adult | California | WTQ | WikiSQL | TabFact |
| --- | --- | --- | --- | --- | --- | --- |
| LLaMA-3.1 8B | 1 | 55.10 | 59.00 | 61.60 | 64.50 | 64.90 |
| 2 | 60.00 | 72.80 | 80.80 | 78.60 | 79.60 |  |
| 3 | 71.10 | 87.80 | 93.60 | 88.90 | 89.90 |  |
| Llama-3.2 3B | 1 | 54.10 | 57.70 | 57.60 | 61.50 | 61.50 |
| 2 | 58.00 | 66.80 | 74.80 | 73.60 | 73.40 |  |
| 3 | 64.40 | 77.20 | 89.70 | 83.20 | 80.40 |  |
| Mistral 7B | 1 | 54.60 | 57.80 | 69.70 | 67.50 | 68.50 |
| 2 | 58.90 | 70.30 | 88.40 | 80.00 | 81.20 |  |
| 3 | 71.50 | 86.80 | 97.70 | 87.80 | 89.90 |  |
| Gemma-3 4B | 1 | 53.90 | 54.30 | 59.30 | 62.60 | 63.30 |
| 2 | 58.90 | 62.50 | 77.00 | 76.60 | 77.90 |  |
| 3 | 67.70 | 73.80 | 89.60 | 86.10 | 87.40 |  |

**Table 3: MIA results on the WikiSQL dataset for all examined models fine-tuned for 1, 2, and 3 epochs. Tables are encoded in the Line-Separated format. Bold values highlight the best-performing method per row.**
| Model | # Epochs | PPL | Min-K 20.0% | Min-K++ 20.0% |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% |
| Llama-3.2 3B | 1 | 55.90 | 7.40 | 56.40 | 7.60 | 61.50 | 7.90 |
| 2 | 62.50 | 10.80 | 63.70 | 11.10 | 73.60 | 14.40 |  |
| 3 | 69.40 | 15.90 | 71.20 | 16.10 | 83.20 | 25.30 |  |
| LLaMA-3.1 8B | 1 | 58.10 | 8.70 | 58.60 | 8.60 | 64.50 | 10.60 |
| 2 | 67.20 | 15.20 | 68.40 | 15.30 | 78.60 | 22.80 |  |
| 3 | 76.50 | 25.30 | 78.10 | 25.90 | 88.90 | 40.20 |  |
| Mistral 7B | 1 | 60.10 | 9.60 | 61.30 | 9.90 | 67.50 | 14.10 |
| 2 | 68.40 | 15.40 | 70.50 | 16.60 | 80.00 | 26.20 |  |
| 3 | 75.10 | 22.20 | 77.60 | 23.80 | 87.80 | 42.90 |  |
| Gemma-3 4B | 1 | 56.20 | 7.40 | 56.60 | 7.60 | 62.60 | 8.70 |
| 2 | 64.00 | 11.30 | 64.90 | 11.70 | 76.60 | 18.60 |  |
| 3 | 72.50 | 17.60 | 73.90 | 18.70 | 86.10 | 34.30 |  |

Table [3](https://arxiv.org/html/2507.17259v1#S5.T3), which compares the performance of the examined attacks on the WikiSQL dataset, illustrates the trends discussed above in greater detail.
For all attacks, as fine-tuning progresses, vulnerability increases, with higher AUROC scores obtained as the number of epochs grew across models.
Among them, Min-K++ 20.0% consistently performs the best, achieving an AUROC of 88.9 with LLaMA-3.1 8B and 87.8 with Mistral 7B.
Additional results for the remaining datasets and attack methods are provided in Appendix [A.2](https://arxiv.org/html/2507.17259v1#A1.SS2).

MIAs generally achieve higher AUROC scores against larger models such as LLaMA-3.1 8B and Mistral 7B, compared to smaller models like LLaMA-3.2 3B and Gemma-3 4B.
For example, after fine-tuning for three epochs, with tables encoded using the Line-Separated format on the California Housing dataset, the Min-K++ 20.0% MIA achieves AUROC scores of 86.8% and 87.8% respectively with Mistral 7B and LLaMA-3.1 8B, compared to 77.2% and 73.8% with LLaMA-3.2 3B and Gemma-3 4B.
Chen et al. chen2023tablecot found that larger models offer clear advantages in table reasoning tasks, highlighting the performance benefits of increased scale.
However, our results reveal a corresponding privacy trade-off: larger models are also significantly more vulnerable to MIAs, with differences of nearly 10 to 14 percentage points in AUROC compared to smaller LLMs. While prior work attributes such susceptibility to the greater memorization capacity of LLMs carlini2023quantifying; carlini2021extracting, our findings extend this observation to models fine-tuned on tabular data, where increased model size correlates with greater leakage under MIAs.

### 5.2 Effect of Encoding Format

Textual encoding shapes the way tabular structures are presented to LLMs and can influence their tendency to memorize data.
In this experiment, we fine-tuned the models and executed the MIAs on the datasets, using different encoding formats to assess their impact on the privacy risk.
Tables [4](https://arxiv.org/html/2507.17259v1#S5.T4) and [5](https://arxiv.org/html/2507.17259v1#S5.T5) present the AUROC scores for MIAs on the California Housing (long-context) and WTQ (short-context) datasets, using the six examined encoding formats.
On both datasets, the Line-Separated and Key-Value Pair formats exhibit the greatest vulnerability to membership inference.
On the WTQ dataset, an AUROC of 97.7% with Mistral 7B was obtained using the Line-Separated format, and on the California Housing dataset, an AUROC of 92.6% was achieved using the Key-Value Pair format.

**Table 4: Comparison of the AUROC scores achieved by different MIA methods across table encoding formats and models on the California Housing dataset. Bold values indicate the highest score per row (encoding), while underlined values indicate the highest score per column (model-method pair).**
|  | Llama-3.2 3B | Mistral 7B | Gemma-3 4B |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Encoding Method | PPL | Min-K 20.0% | Min-K++ 20.0% | PPL | Min-K 20.0% | Min-K++ 20.0% | PPL | Min-K 20.0% | Min-K++ 20.0% |
| Markdown | 60.60 | 60.90 | 72.00 | 65.60 | 73.10 | 80.00 | 59.10 | 64.10 | 67.80 |
| JSON | 59.60 | 59.60 | 53.00 | 61.40 | 61.40 | 54.50 | 58.40 | 58.40 | 55.00 |
| HTML | 59.70 | 59.70 | 55.80 | 61.70 | 61.70 | 50.60 | 59.10 | 61.20 | 55.40 |
| Key-Value Pair | 62.80 | 62.80 | 78.70 | 72.40 | 74.70 | 92.60 | 59.30 | 60.80 | 67.00 |
| Key-is-Value | 60.20 | 60.20 | 55.10 | 63.70 | 65.00 | 74.90 | 59.20 | 60.60 | 66.70 |
| Line-Separated | 61.60 | 64.90 | 77.20 | 69.70 | 84.90 | 86.80 | 62.30 | 72.10 | 73.80 |

**Table 5: Comparison of the AUROC scores achieved by different MIA methods across table encoding formats and models on the WTQ dataset. Bold values indicate the highest score per row (encoding), while underlined values indicate the highest score per column (model-method pair).**
|  | Llama-3.2 3B | Mistral 7B | Gemma-3 4B |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Encoding Method | PPL | Min-K 20.0% | Min-K++ 20.0% | PPL | Min-K 20.0% | Min-K++ 20.0% | PPL | Min-K 20.0% | Min-K++ 20.0% |
| Markdown | 68.00 | 69.50 | 85.30 | 87.00 | 88.40 | 94.20 | 73.70 | 74.80 | 86.70 |
| JSON | 67.10 | 67.50 | 79.80 | 79.40 | 79.60 | 82.70 | 70.70 | 71.00 | 79.20 |
| HTML | 66.30 | 66.60 | 79.70 | 82.80 | 83.00 | 92.90 | 72.10 | 72.80 | 83.30 |
| Key-Value Pair | 67.00 | 67.80 | 83.50 | 85.00 | 85.70 | 94.90 | 72.80 | 73.80 | 85.50 |
| Key-is-Value | 67.00 | 67.90 | 83.70 | 83.60 | 84.20 | 89.70 | 72.30 | 73.20 | 85.00 |
| Line-Separated | 70.40 | 72.40 | 89.70 | 87.30 | 90.40 | 97.70 | 74.70 | 76.50 | 89.60 |

These findings show that encoding format impacts the privacy risk.
Flat, row-based encodings like Line-Separated and Key-Value Pair produce long, continuous sequences of content tokens that align closely with tokenizer boundaries.
This structure concentrates learning on individual cell values, increasing the likelihood of memorization—resulting in the highest AUROC scores across datasets and MIA methods.

In contrast, formats such as HTML and JSON introduce structural redundancy via tags and punctuation. This disperses model attention across non-content tokens, leading to lower AUROC scores—typically 10 points lower—indicating reduced memorization.
Intermediate formats like Key-is-Value and Markdown strike a balance between structural clarity and redundancy, yielding moderate vulnerability.
Additional results are available in Appendix [A.3](https://arxiv.org/html/2507.17259v1#A1.SS3).

### 5.3 Cross-Format Generalization

In this experiment, we examine whether tabular data learned during fine-tuning with one table encoding format remains detectable by MIAs applied using a different format.
This scenario mirrors real-world deployment settings, where the encoding format used during the model’s training is unknown.
To evaluate this, we fine-tuned the Gemma-3 4B model on the TabFact dataset using one of the six encoding formats and executed the Min-K++ 20.0% attack. The results, shown in Figure [2](https://arxiv.org/html/2507.17259v1#S5.F2), reveal partial cross-format generalization: memorization signals often persist even when the evaluation format differs from the training format.
Diagonal cells (where training and evaluation formats match) tend to yield the highest AUROC values, confirming that MIAs are most effective when structural representations align.
For example, training and evaluating on the Markdown format yields an AUROC of 85.2%, whereas switching the attack format to Key-Value or Line-Separated reduces performance to 68.9% and 69.4%, respectively.

To gain additional insights, we compute the average AUROC values across the rows and columns of the heatmap.
These averages reflect how effective each encoding format is when used to encode the data for MIA detection (rows) and for model fine-tuning (columns).
The most vulnerable format for MIA detection is HTML (76.0), followed by Key-Value Pair (73.2) and JSON (71.2), suggesting that these formats offer greater advantages to attackers.
On the training side, Line-Separated and Key-is-Value induce the most memorization, resulting in average AUROCs of 74.6 and 72.8, respectively. From a defender’s perspective, selecting training formats like JSON or HTML—which yield lower average AUROCs of 69.4 and 70.1—may help reduce privacy risk.

Figure: Figure 2: Heatmap showing the AUROC achieved by the Min-K++ 20.0% MIA on the WTQ dataset using the Gemma-3 4B model. Each cell compares the encoding used during fine-tuning (columns) with the encoding used during MIA detection (rows).
Refer to caption: x1.png

### 5.4 Pretrained models

**Table 6: AUROC scores achieved by the Min-K++ 20.0% MIA on the WTQ dataset using pretrained models without fine-tuning. Synthetic data was generated to serve as non-member samples. The table compares performance across table encoding formats for each model. Bold values indicate the highest score per row (encoding), while underlined values indicate the highest score per column (model).**
| Encoding Method | Llama-3.1 8B | Llama-3.2 3B | Mistral 7B | Gemma-3 4B |
| --- | --- | --- | --- | --- |
| Markdown | 69.30 | 62.20 | 63.00 | 60.70 |
| JSON | 62.40 | 57.60 | 59.90 | 58.40 |
| HTML | 66.70 | 60.00 | 61.70 | 61.80 |
| Key-Value Pair | 72.00 | 66.20 | 66.90 | 63.40 |
| Key-is-Value | 71.60 | 65.90 | 64.10 | 61.90 |
| Line-Separated | 71.50 | 63.80 | 62.90 | 60.90 |

In this experiment, we assess LLMs’ vulnerability to MIAs in their pretrained state—prior to any fine-tuning.
Our goal is to determine whether publicly available models have inadvertently memorized examples from the WTQ dataset, which forms part of our benchmark.
Given WTQ’s wide use and its reliance on Wikipedia tables, we assume that its contents may have been included in the pretraining corpora of many open-weight LLMs.
To simulate an MIA setting, we treated the original WTQ tables as member samples and generated synthetic non-member tables using the *GPT-4o mini* openai2024gpt4omini model.
We then used the Min-K++ 20.0% attack to test each pretrained model for evidence of memorization of the WTQ tables.

Table [6](https://arxiv.org/html/2507.17259v1#S5.T6) presents the AUROC scores for four models with the six encoding formats.
The results show pretrained models without further fine-tuning exhibit moderate levels of data leakage. The highest AUROC of 72.0 is observed for LLaMA-3.1 8B with the Key-Value Pair format.
Formats such as Key-Value Pair, Key-is-Value, and Line-Separated consistently result in greater vulnerability across models, with AUROC scores frequently exceeding 60%, indicating that the models likely memorized these tables during pretraining.

## 6 Conclusion

Tab-MIA is the first benchmark for evaluating MIAs on LLMs trained on tabular data. Through controlled experiments on four SOTA open-source LLMs and six encoding strategies, our experiments show that fine-tuning LLMs on tabular data might cause memorization and thus make them vulnerable to MIAs.
Some attacks can achieve AUROC scores exceeding 95% with minimal fine-tuning, underscoring the risk of memorization and privacy leakage.
In contrast, we find that using encodings that introduce syntactic noise (e.g., verbose or structured formats such as HTML or JSON) mitigates attack success.
Our benchmark provides a foundation for the systematic evaluation of privacy risks in various scenarios with different models and table encoding formats.
While our benchmark encompasses five datasets from three distinct sources, future work should expand to broader domains to improve generalizability.

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## Appendix A Technical Appendices and Supplementary Material

### A.1 Training and Evaluation Configurations

This appendix contains the training configurations used in our experiments. All models are fine-tuned using QLoRA [15], a PEFT method that enables efficient training with 4-bit quantized weights. Fine-tuning is performed using a batch size of two on a single RTX 6000 GPU (48GB VRAM). We apply a learning rate of 3e-4, use the paged_adamw_8bit optimizer, and set warmup_steps to 20. We use a fixed random seed of 42 for all dataset splits and data loading to ensure reproducibility.

For each dataset, 50% of the tables are selected as member records for fine-tuning, with the remaining used as non-members for MIA evaluation. All experiments are implemented using HuggingFace Transformers and PEFT libraries, with evaluation scripts provided in the public code repository.

### A.2 Effect of Fine-Tuning Epochs on MIA Vulnerability

This section presents comprehensive results on how the number of fine-tuning epochs affects vulnerability to MIAs across all model–dataset configurations in our benchmark. For this experiment, we report results using the Line-Separated encoding format, as it consistently exhibits high memorization rates across models and datasets, making it a strong representative for analyzing privacy risk.
Tables [7](https://arxiv.org/html/2507.17259v1#A1.T7)–[10](https://arxiv.org/html/2507.17259v1#A1.T10) summarize AUROC and TPR@FPR=5% metrics across three representative MIA methods: LOSS (PPL), Min-K 20.0%, and Min-K++ 20.0%. Across all methods, we observe that longer fine-tuning leads to increased model memorization and thus greater vulnerability to MIAs.

**Table 7: MIA results on the Adult dataset for all examined models fine-tuned for 1, 2, and 3 epochs. Tables are encoded in the Line-Separated format. Bold values highlight the best-performing method per row.**
| Model | # Epochs | PPL | Min-K 20.0% | Min-K++ 20.0% |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% |
| Llama-3.2 3B | 1 | 53.30 | 5.20 | 53.30 | 4.90 | 54.10 | 5.30 |
| 2 | 56.50 | 7.00 | 56.50 | 6.30 | 58.00 | 7.30 |  |
| 3 | 62.40 | 9.60 | 62.60 | 9.80 | 64.40 | 10.20 |  |
| LLaMA-3.1 8B | 1 | 53.80 | 6.30 | 53.70 | 6.40 | 55.10 | 6.70 |
| 2 | 58.10 | 7.50 | 58.10 | 8.40 | 60.00 | 8.00 |  |
| 3 | 73.90 | 24.20 | 74.30 | 25.80 | 71.10 | 15.50 |  |
| Mistral 7B | 1 | 54.00 | 5.20 | 54.10 | 4.40 | 54.60 | 5.20 |
| 2 | 57.10 | 6.80 | 57.60 | 6.10 | 58.90 | 6.80 |  |
| 3 | 65.90 | 10.80 | 67.40 | 11.80 | 71.50 | 14.70 |  |
| Gemma-3 4B | 1 | 53.20 | 6.20 | 53.10 | 5.70 | 53.90 | 5.00 |
| 2 | 56.70 | 7.30 | 57.20 | 6.10 | 58.90 | 7.50 |  |
| 3 | 63.00 | 10.50 | 64.80 | 12.00 | 67.70 | 11.70 |  |

**Table 8: MIA results on the California Housing dataset for all examined models fine-tuned for 1, 2, and 3 epochs. Tables are encoded in the Line-Separated format. Bold values highlight the best-performing method per row.**
| Model | # Epochs | PPL | Min-K 20.0% | Min-K++ 20.0% |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% |
| Llama-3.2 3B | 1 | 53.90 | 8.30 | 55.20 | 7.40 | 57.70 | 7.40 |
| 2 | 57.00 | 12.00 | 59.00 | 9.90 | 66.80 | 15.50 |  |
| 3 | 61.60 | 14.90 | 64.90 | 14.00 | 77.20 | 26.60 |  |
| LLaMA-3.1 8B | 1 | 54.10 | 9.30 | 55.30 | 8.50 | 59.00 | 10.70 |
| 2 | 58.70 | 13.40 | 61.10 | 11.20 | 72.80 | 22.70 |  |
| 3 | 66.30 | 19.60 | 70.40 | 19.60 | 87.80 | 52.50 |  |
| Mistral 7B | 1 | 55.00 | 9.50 | 57.30 | 10.10 | 57.80 | 12.40 |
| 2 | 59.70 | 13.40 | 68.20 | 18.80 | 70.30 | 23.60 |  |
| 3 | 69.70 | 19.40 | 84.90 | 45.00 | 86.80 | 56.80 |  |
| Gemma-3 4B | 1 | 53.80 | 9.70 | 54.30 | 9.30 | 54.30 | 7.90 |
| 2 | 56.90 | 10.70 | 61.40 | 14.10 | 62.50 | 12.60 |  |
| 3 | 62.30 | 13.20 | 72.10 | 20.70 | 73.80 | 23.30 |  |

**Table 9: MIA results on the WTQ dataset for all examined models fine-tuned for 1, 2, and 3 epochs. Tables are encoded in the Line-Separated format. Bold values highlight the best-performing method per row.**
| Model | # Epochs | PPL | Min-K 20.0% | Min-K++ 20.0% |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% |
| Llama-3.2 3B | 1 | 51.50 | 3.70 | 51.90 | 5.10 | 57.60 | 5.90 |
| 2 | 59.70 | 8.20 | 60.80 | 8.70 | 74.80 | 19.00 |  |
| 3 | 70.40 | 14.80 | 72.40 | 16.30 | 89.70 | 48.40 |  |
| LLaMA-3.1 8B | 1 | 53.70 | 5.10 | 54.10 | 5.10 | 61.60 | 9.00 |
| 2 | 64.70 | 10.70 | 65.80 | 12.00 | 80.80 | 30.50 |  |
| 3 | 77.90 | 27.20 | 79.50 | 29.50 | 93.60 | 66.40 |  |
| Mistral 7B | 1 | 58.40 | 8.60 | 59.80 | 7.80 | 69.70 | 15.40 |
| 2 | 74.30 | 20.80 | 76.80 | 21.20 | 88.40 | 55.20 |  |
| 3 | 87.30 | 47.00 | 90.40 | 51.30 | 97.70 | 88.20 |  |
| Gemma-3 4B | 1 | 52.50 | 4.20 | 53.00 | 3.70 | 59.30 | 7.50 |
| 2 | 61.90 | 9.50 | 62.90 | 9.00 | 77.00 | 24.90 |  |
| 3 | 74.70 | 16.50 | 76.50 | 20.20 | 89.60 | 54.10 |  |

**Table 10: MIA results on the TabFact dataset for all examined models fine-tuned for 1, 2, and 3 epochs. Tables are encoded in the Line-Separated format. Bold values highlight the best-performing method per row.**
| Model | # Epochs | PPL | Min-K 20.0% | Min-K++ 20.0% |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% |
| Llama-3.2 3B | 1 | 55.10 | 6.60 | 55.50 | 6.50 | 61.50 | 8.80 |
| 2 | 62.10 | 9.90 | 63.10 | 10.10 | 73.40 | 14.90 |  |
| 3 | 67.80 | 13.80 | 69.40 | 14.00 | 80.40 | 31.10 |  |
| LLaMA-3.1 8B | 1 | 57.90 | 7.90 | 58.30 | 8.20 | 64.90 | 11.20 |
| 2 | 67.80 | 15.10 | 68.90 | 15.20 | 79.60 | 24.20 |  |
| 3 | 77.40 | 26.40 | 78.70 | 27.00 | 89.90 | 47.00 |  |
| Mistral 7B | 1 | 60.00 | 9.60 | 60.70 | 10.00 | 68.50 | 14.70 |
| 2 | 69.20 | 16.00 | 70.60 | 16.70 | 81.20 | 29.40 |  |
| 3 | 77.00 | 24.60 | 78.90 | 25.50 | 89.90 | 50.50 |  |
| Gemma-3 4B | 1 | 55.40 | 7.00 | 55.40 | 7.00 | 63.30 | 10.80 |
| 2 | 63.80 | 11.40 | 64.40 | 11.70 | 77.90 | 20.10 |  |
| 3 | 72.70 | 17.40 | 73.60 | 18.20 | 87.40 | 37.40 |  |

### A.3 Impact of Table Encoding Formats on MIA Performance

This section provides detailed results on the effect of different table encoding formats on models’ susceptibility to MIAs. Tables [11](https://arxiv.org/html/2507.17259v1#A1.T11)- [15](https://arxiv.org/html/2507.17259v1#A1.T15) report the AUROC and TPR@FPR=5% values for six encoding schemes (HTML, JSON, Key-Value Pair, Key-is-Value, Line-Separated, and Markdown) for all model–dataset configurations.

**Table 11: MIA results on the Adult dataset for the examined models, with the various encoding formats. For each method, both AUROC and TPR@FPR=5% are reported. Bold values highlight the best-performing method per row.**
| Model | Encoding | PPL | Min-K 20.0% | Min-K++ 20.0% |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% |
| LLaMA-3.1 8B | HTML | 62.40 | 9.30 | 62.40 | 9.20 | 54.90 | 6.50 |
| JSON | 69.90 | 17.30 | 69.90 | 17.30 | 68.80 | 14.50 |  |
| Key-is-Value | 70.50 | 18.30 | 70.40 | 17.80 | 70.50 | 15.40 |  |
| Key-Value Pair | 72.60 | 21.90 | 72.60 | 22.00 | 71.30 | 16.50 |  |
| Line-Separated | 73.90 | 24.20 | 74.30 | 25.80 | 71.10 | 15.50 |  |
| Markdown | 75.70 | 27.60 | 75.70 | 28.20 | 73.20 | 19.70 |  |
| Llama-3.2 3B | HTML | 61.60 | 9.70 | 61.60 | 9.70 | 62.70 | 8.80 |
| JSON | 61.40 | 9.30 | 61.40 | 9.30 | 63.70 | 9.40 |  |
| Key-is-Value | 60.50 | 8.80 | 60.40 | 8.70 | 63.10 | 9.50 |  |
| Key-Value Pair | 60.20 | 8.10 | 60.20 | 8.40 | 63.00 | 9.40 |  |
| Line-Separated | 62.40 | 9.60 | 62.60 | 9.80 | 64.40 | 10.20 |  |
| Markdown | 62.80 | 9.80 | 62.80 | 9.80 | 65.10 | 10.90 |  |
| Mistral 7B | HTML | 71.00 | 17.70 | 71.00 | 17.60 | 75.30 | 21.90 |
| JSON | 56.90 | 5.80 | 56.90 | 5.90 | 50.90 | 3.80 |  |
| Key-is-Value | 67.40 | 12.50 | 67.40 | 12.60 | 73.30 | 19.40 |  |
| Key-Value Pair | 66.90 | 10.90 | 67.00 | 11.00 | 72.40 | 15.50 |  |
| Line-Separated | 65.90 | 10.80 | 67.40 | 11.80 | 71.50 | 14.70 |  |
| Markdown | 71.60 | 14.40 | 71.90 | 14.70 | 78.20 | 27.30 |  |
| Gemma-3 4B | HTML | 59.20 | 7.90 | 59.20 | 8.20 | 54.20 | 7.60 |
| JSON | 55.70 | 6.80 | 55.70 | 6.70 | 50.80 | 8.00 |  |
| Key-is-Value | 57.40 | 6.50 | 57.40 | 6.60 | 59.80 | 6.80 |  |
| Key-Value Pair | 57.60 | 7.00 | 57.60 | 7.10 | 59.80 | 6.80 |  |
| Line-Separated | 63.00 | 10.50 | 64.80 | 12.00 | 67.70 | 11.70 |  |
| Markdown | 58.20 | 7.10 | 58.40 | 7.00 | 61.30 | 8.00 |  |

**Table 12: MIA results on the California Housing dataset for the examined models, with the various encoding formats. For each method, both AUROC and TPR@FPR=5% are reported. Bold values highlight the best-performing method per row.**
| Model | Encoding | PPL | Min-K 20.0% | Min-K++ 20.0% |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% |
| LLaMA-3.1 8B | HTML | 69.40 | 21.30 | 69.40 | 21.10 | 88.90 | 56.80 |
| JSON | 63.80 | 15.10 | 63.80 | 15.10 | 54.60 | 8.30 |  |
| Key-is-Value | 64.30 | 14.30 | 64.30 | 14.10 | 56.00 | 9.50 |  |
| Key-Value Pair | 68.30 | 18.40 | 68.20 | 18.40 | 88.20 | 51.20 |  |
| Line-Separated | 66.30 | 19.60 | 70.40 | 19.60 | 87.80 | 52.50 |  |
| Markdown | 64.60 | 15.50 | 64.90 | 15.90 | 80.00 | 34.50 |  |
| Llama-3.2 3B | HTML | 59.70 | 13.20 | 59.70 | 13.00 | 55.80 | 7.00 |
| JSON | 59.60 | 12.20 | 59.60 | 12.40 | 53.00 | 4.50 |  |
| Key-is-Value | 60.20 | 13.60 | 60.20 | 13.60 | 55.10 | 10.10 |  |
| Key-Value Pair | 62.80 | 16.10 | 62.80 | 15.90 | 78.70 | 26.20 |  |
| Line-Separated | 61.60 | 14.90 | 64.90 | 14.00 | 77.20 | 26.60 |  |
| Markdown | 60.60 | 13.20 | 60.90 | 13.00 | 72.00 | 22.10 |  |
| Mistral 7B | HTML | 61.70 | 13.40 | 61.70 | 13.40 | 50.60 | 5.00 |
| JSON | 61.40 | 14.10 | 61.40 | 14.00 | 54.50 | 6.80 |  |
| Key-is-Value | 63.70 | 15.50 | 65.00 | 14.70 | 74.90 | 28.70 |  |
| Key-Value Pair | 72.40 | 24.20 | 74.70 | 24.20 | 92.60 | 68.00 |  |
| Line-Separated | 69.70 | 19.40 | 84.90 | 45.00 | 86.80 | 56.80 |  |
| Markdown | 65.60 | 17.60 | 73.10 | 23.10 | 80.00 | 39.10 |  |
| Gemma-3 4B | HTML | 59.10 | 11.00 | 61.20 | 11.80 | 55.40 | 7.80 |
| JSON | 58.40 | 11.40 | 58.40 | 11.40 | 55.00 | 7.60 |  |
| Key-is-Value | 59.20 | 10.30 | 60.60 | 11.00 | 66.70 | 15.70 |  |
| Key-Value Pair | 59.30 | 11.20 | 60.80 | 11.20 | 67.00 | 15.30 |  |
| Line-Separated | 62.30 | 13.20 | 72.10 | 20.70 | 73.80 | 23.30 |  |
| Markdown | 59.10 | 11.20 | 64.10 | 13.20 | 67.80 | 15.30 |  |

**Table 13: MIA results on the WTQ dataset for the examined models, with the various encoding formats. For each method, both AUROC and TPR@FPR=5% are reported. Bold values highlight the best-performing method per row.**
| Model | Encoding | PPL | Min-K 20.0% | Min-K++ 20.0% |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% |
| LLaMA-3.1 8B | HTML | 71.60 | 17.30 | 71.80 | 17.30 | 82.00 | 41.70 |
| JSON | 72.90 | 19.10 | 73.20 | 19.30 | 81.70 | 44.60 |  |
| Key-is-Value | 73.20 | 21.20 | 73.90 | 21.20 | 86.50 | 50.20 |  |
| Key-Value Pair | 73.80 | 23.20 | 74.40 | 22.90 | 86.70 | 51.80 |  |
| Line-Separated | 77.90 | 27.20 | 79.50 | 29.50 | 93.60 | 66.40 |  |
| Markdown | 74.40 | 19.80 | 75.60 | 20.50 | 89.40 | 54.70 |  |
| Llama-3.2 3B | HTML | 66.30 | 10.40 | 66.60 | 10.60 | 79.70 | 28.60 |
| JSON | 67.10 | 11.70 | 67.50 | 11.70 | 79.80 | 33.00 |  |
| Key-is-Value | 67.00 | 12.80 | 67.90 | 13.10 | 83.70 | 38.30 |  |
| Key-Value Pair | 67.00 | 12.10 | 67.80 | 12.80 | 83.50 | 40.40 |  |
| Line-Separated | 70.40 | 14.80 | 72.40 | 16.30 | 89.70 | 48.40 |  |
| Markdown | 68.00 | 12.60 | 69.50 | 13.50 | 85.30 | 31.90 |  |
| Mistral 7B | HTML | 82.80 | 29.70 | 83.00 | 30.00 | 92.90 | 70.00 |
| JSON | 79.40 | 29.20 | 79.60 | 29.10 | 82.70 | 53.80 |  |
| Key-is-Value | 83.60 | 34.70 | 84.20 | 35.60 | 89.70 | 68.10 |  |
| Key-Value Pair | 85.00 | 37.60 | 85.70 | 38.60 | 94.90 | 79.20 |  |
| Line-Separated | 87.30 | 47.00 | 90.40 | 51.30 | 97.70 | 88.20 |  |
| Markdown | 87.00 | 36.70 | 88.40 | 36.90 | 94.20 | 84.00 |  |
| Gemma-3 4B | HTML | 72.10 | 12.90 | 72.80 | 12.90 | 83.30 | 42.30 |
| JSON | 70.70 | 12.10 | 71.00 | 12.10 | 79.20 | 37.00 |  |
| Key-is-Value | 72.30 | 14.50 | 73.20 | 14.60 | 85.00 | 46.50 |  |
| Key-Value Pair | 72.80 | 15.60 | 73.80 | 15.70 | 85.50 | 49.30 |  |
| Line-Separated | 74.70 | 16.50 | 76.50 | 20.20 | 89.60 | 54.10 |  |
| Markdown | 73.70 | 16.30 | 74.80 | 16.80 | 86.70 | 49.10 |  |

**Table 14: MIA results on the WikiSQL dataset for the examined models, with the various encoding formats. For each method, both AUROC and TPR@FPR=5% are reported. Bold values highlight the best-performing method per row.**
| Model | Encoding | PPL | Min-K 20.0% | Min-K++ 20.0% |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% |
| LLaMA-3.1 8B | HTML | 74.50 | 18.90 | 74.50 | 18.90 | 81.90 | 30.40 |
| JSON | 75.20 | 20.10 | 75.30 | 20.10 | 82.70 | 32.40 |  |
| Key-is-Value | 75.50 | 20.90 | 75.80 | 21.10 | 86.10 | 37.60 |  |
| Key-Value Pair | 75.60 | 21.20 | 75.80 | 21.40 | 86.00 | 36.30 |  |
| Line-Separated | 76.50 | 25.30 | 78.10 | 25.90 | 88.90 | 40.20 |  |
| Markdown | 75.60 | 20.20 | 76.10 | 20.70 | 86.20 | 33.70 |  |
| Llama-3.2 3B | HTML | 67.90 | 11.70 | 68.00 | 11.80 | 76.80 | 19.50 |
| JSON | 69.10 | 13.10 | 69.20 | 13.10 | 78.50 | 22.40 |  |
| Key-is-Value | 64.60 | 8.70 | 65.10 | 8.80 | 72.30 | 12.60 |  |
| Key-Value Pair | 69.10 | 13.40 | 69.50 | 13.50 | 80.70 | 23.10 |  |
| Line-Separated | 69.40 | 15.90 | 71.20 | 16.10 | 83.20 | 25.30 |  |
| Markdown | 68.10 | 11.50 | 69.00 | 11.70 | 71.40 | 14.60 |  |
| Mistral 7B | HTML | 72.20 | 16.50 | 72.30 | 16.50 | 79.60 | 31.00 |
| JSON | 72.90 | 16.80 | 73.10 | 16.70 | 80.30 | 30.70 |  |
| Key-is-Value | 74.70 | 19.50 | 75.40 | 19.80 | 85.30 | 37.50 |  |
| Key-Value Pair | 74.60 | 19.90 | 75.20 | 20.20 | 85.40 | 37.70 |  |
| Line-Separated | 75.10 | 22.20 | 77.60 | 23.80 | 87.80 | 42.90 |  |
| Markdown | 75.10 | 19.80 | 76.20 | 20.40 | 86.10 | 39.10 |  |
| Gemma-3 4B | HTML | 72.00 | 16.00 | 72.50 | 16.20 | 83.40 | 29.20 |
| JSON | 71.20 | 14.40 | 71.30 | 14.40 | 80.00 | 26.90 |  |
| Key-is-Value | 72.00 | 15.70 | 72.50 | 15.80 | 84.20 | 30.30 |  |
| Key-Value Pair | 71.90 | 15.80 | 72.50 | 15.80 | 84.50 | 31.20 |  |
| Line-Separated | 72.50 | 17.60 | 73.90 | 18.70 | 86.10 | 34.30 |  |
| Markdown | 71.20 | 14.30 | 71.90 | 14.60 | 83.00 | 26.80 |  |

**Table 15: MIA results on the TabFact dataset for the examined models, with the various encoding formats. For each method, both AUROC and TPR@FPR=5% are reported. Bold values highlight the best-performing method per row.**
| Model | Encoding | PPL | Min-K 20.0% | Min-K++ 20.0% |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% | AUROC | TPR@FPR=5% |
| LLaMA-3.1 8B | HTML | 76.10 | 19.50 | 76.10 | 19.50 | 83.70 | 34.50 |
| JSON | 70.30 | 13.00 | 70.40 | 13.00 | 69.40 | 21.20 |  |
| Key-is-Value | 77.60 | 21.70 | 77.90 | 21.90 | 88.30 | 41.10 |  |
| Key-Value Pair | 78.10 | 22.10 | 78.30 | 22.40 | 88.40 | 42.10 |  |
| Line-Separated | 77.40 | 26.40 | 78.70 | 27.00 | 89.90 | 47.00 |  |
| Markdown | 78.00 | 22.20 | 78.70 | 22.80 | 88.70 | 40.50 |  |
| Llama-3.2 3B | HTML | 68.60 | 11.40 | 68.70 | 11.40 | 78.20 | 20.30 |
| JSON | 69.50 | 11.90 | 69.60 | 12.00 | 80.20 | 23.30 |  |
| Key-is-Value | 64.10 | 8.60 | 64.70 | 8.80 | 71.90 | 12.20 |  |
| Key-Value Pair | 67.70 | 11.60 | 68.20 | 11.60 | 78.80 | 20.60 |  |
| Line-Separated | 67.80 | 13.80 | 69.40 | 14.00 | 80.40 | 31.10 |  |
| Markdown | 67.70 | 10.90 | 68.80 | 11.20 | 73.90 | 12.80 |  |
| Mistral 7B | HTML | 74.60 | 18.60 | 74.60 | 18.60 | 82.40 | 37.60 |
| JSON | 75.70 | 19.60 | 75.80 | 19.60 | 83.90 | 39.10 |  |
| Key-is-Value | 76.80 | 21.00 | 77.40 | 21.10 | 87.70 | 43.30 |  |
| Key-Value Pair | 76.90 | 21.80 | 77.50 | 21.90 | 88.00 | 43.80 |  |
| Line-Separated | 77.00 | 24.60 | 78.90 | 25.50 | 89.90 | 50.50 |  |
| Markdown | 77.50 | 23.10 | 78.70 | 23.60 | 89.10 | 47.20 |  |
| Gemma-3 4B | HTML | 72.40 | 15.50 | 72.90 | 15.60 | 85.00 | 33.10 |
| JSON | 72.00 | 14.20 | 72.20 | 14.20 | 82.00 | 30.90 |  |
| Key-is-Value | 72.70 | 14.90 | 73.10 | 15.10 | 86.10 | 33.00 |  |
| Key-Value Pair | 72.50 | 14.70 | 72.90 | 14.80 | 86.10 | 33.90 |  |
| Line-Separated | 72.70 | 17.40 | 73.60 | 18.20 | 87.40 | 37.40 |  |
| Markdown | 71.80 | 14.90 | 72.50 | 15.20 | 85.20 | 29.60 |  |

### A.4 Dataset and Code Release

Following the NeurIPS Datasets and Benchmarks Track guidelines, we publicly release the Tab-MIA benchmark on HuggingFace: [https://huggingface.co/datasets/germane/Tab-MIA](https://huggingface.co/datasets/germane/Tab-MIA). All training, evaluation, and MIA attack scripts, along with instructions for reproducing our experiments, are available in the GitHub repository: [https://anonymous.4open.science/r/Tab-MIA](https://anonymous.4open.science/r/Tab-MIA). The dataset is released under the CC BY 4.0 license, and the code is provided under an open-source license to support transparent and reproducible research.

All source datasets used to construct Tab-MIA are openly accessible via HuggingFace or Kaggle, and each is released under a license that permits reuse for research and dataset creation.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 0
presentation: 2
questions: See above
soundness: 2
strengths: - Running three exisitng MIAs (LOSS attack, Min-K% attack, Min-K%++ ) on LLaMA-3.1 8B, LLaMA-3.2 3B, Gemma-3 4B, and Mistral 7B QLoRA fine-tuned on Tab-MIA dataset. 

- Highlighting that Tabular data may contain personally identifiable information (PII), commercially sensitive material, or domainspecific details that are not intended for broad dissemination
summary: This paper introduces a benchmark dataset, Tab-MIA, for evaluating MIAs on tabular data in LLMs. Tab-MIA includes short-context and long-context tables, each represented in six different textual encoding formats. 


Short-context tables are derived from QA benchmarks WikiTableQuestions (WTQ), WikiSQL, and TabFact. Long-context tables are derived from structured tabular benchmarks frequently used in fairness, regression, and privacy studies: Adult (Census Income) dataset and the California Housing dataset.  

Encoding fromats are: JSON, HTML, Markdown, Key-Value Pair, Key-is-Value, and Line-Separated (CSV-like).
weaknesses: - Lack Dataset novelty/validity: Tab-MIA is a recombination of existing tabular datasets. The construction also risks the member vs. non-member boundary, as there are no guarantees that chosen LLMs have not already seen all data.

- No methodological novelty: No new membership-inference attack tailored to tabular data is proposed.

- Results lack novelty: The paper largely reiterates established findings, nothing new about tabular data:
	- LLMs can memorize tabular data
	- partial transferability of attacks across encoding formats
	- a consistent and substantial increase in vulnerability as the number of fine-tuning epochs grows
	- larger models are also significantly more vulnerable to MIAs

- Use of pre-training and fine-tuning is not consistent

- Typo in line 121: DC-PDD(

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 3
questions: 1. How do you work around limitations of some formats- eg lack of ability for some formats to allow spans across multiple columns or embed sub-tables?  The universe of tables in public data, especially html-formatted tables, is much richer than simple grid format.

2. Aside from table representation do you have any other insights as to the kinds of tables that are easily memorized? 

3. Generating synthetic non-member tables from Gpt-4o-mini: these are clearly contaminated by both the in-context sample and any public tables in GPT-4o's training set. I wonder If there are alternatives that would generate high-quality, plausble tables that aren't influenced by the in-member data.  One option that comes to mind is using a library like SmartNoise to generate tables from public seeds that meet a row-wise differential privacy guarantee, but would still be some level of contamination.

4. Evaluation against larger models and/or a public model API that provides logits for a very large model would be nice to have.
soundness: 3
strengths: * The work fills an important gap in auditing LLMs for privacy risks, beyond conventional text membership tests.
* Strong evaluation on a suite of LLMs under a fine-tuning paradigm, as well as evaluation of some public pretrained models.
summary: This paper introduces Tab-MIA- a dataset for evaluating membership inference of tabular data in LLMs. Tab-MIA consists of a set of tables sourced from public datasets, represented in a variety of common formats used for LLM training. The authors present an overview of the dataset and run a series of experiments to demonstrate its utility for auditing publicly trained and privately fine-tuned models.
weaknesses: * I wonder about the realism of fine-tuning a model on some context-free tables- in most natural language tasks a table will be associated with accompanying text to set context, provide explanation, inject a user query, etc.  From this perspective I'm not sure how to interpret the significance of the attack results. 

* Experiments on larger models would be valuable, even just targeting large pretrained models without fine-tuning.

* An experiment demonstrating a defense (eg training with DP-SGD or privatizing the data itself with a DP method) and potential privacy-utility tradeoffs would strengthen the paper.

---

confidence: 4
contribution: 3
initial_rating: 2
presentation: 2
questions: - The filtering step (line 246) is unclear, why does the Adult dataset shrink from 48k to 3k samples? How exactly was the filtering applied to reduce the Adult dataset size?
- What would happen if models were fine-tuned using a combination of encoding formats, would this amplify or mitigate memorization?
soundness: 2
strengths: - The paper is easy to follow and presents a useful and timely contribution, introducing the benchmark for MIAs on tabular data in LLMs.

- Provides valuable empirical insights into memorization patterns and attack transferability, highlighting underexplored privacy risks in adapting tabular data to LLM.

- The methodology and benchmark design have practical utility for future privacy research on structured data.
summary: The paper introduces Tab-MIA, a benchmark dataset for evaluating membership inference attacks (MIAs) on large language models (LLMs) trained with tabular data. Using this benchmark, the authors conduct systematic evaluation of MIA methods across multiple tabular encoding/serialization formats. They find that LLMs can memorize structured data to an extent that enables effective membership inference, with AUROC scores approaching 90% after minimal fine-tuning. The study also reveals partial transferability of attacks across encoding formats, emphasizing the need for stronger privacy-preserving training practices when adapting LLMs to tabular data.
weaknesses: - The bullet points in lines 257–269 repeat the content of Figure 1; consider moving these details to the appendix for brevity.

- Consider including a visual example contrasting long- vs short-context table encodings to help readers intuitively understand the setup.

- The paper relies heavily on MIA metrics but provides limited explanation of them. Expanding the description of attack metrics in the main text would make the results more interpretable.

- Consider reordering the results section, starting with Section 5.2 (encoding comparison) would align better with the paper’s central research question.

I am assigning a low score primarily due to concerns about reproducibility. For a benchmark paper, the release of code is essential to validate results and facilitate future research. Without access to the implementation, it is difficult to fully trust or reproduce the reported findings. I would be willing to raise my score if the authors make the code publicly available and provide clear implementation details of the MIA metrics in the paper, since much of the paper’s analysis depends on these metrics.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 4
questions: - Is there evidence that people have been finetuning or pretraining LLMs on tabular dataset including PII? It could be a nice addition to provide an overview of this. 

- Could authors also add the Ref or Ratio attack from [1] or as used by [2]? You could easily just divide the target finetuned model loss by the loss of the pretrained model. From my experience, this should be better than any of the other methods you currently have. 
	

[1] Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., ... & Raffel, C. (2021). Extracting training data from large language models. In 30th USENIX security symposium (USENIX Security 21) (pp. 2633-2650).

[2] Duan, M., Suri, A., Mireshghallah, N., Min, S., Shi, W., Zettlemoyer, L., ... & Hajishirzi, H. (2024). Do membership inference attacks work on large language models?. arXiv preprint arXiv:2402.07841.
soundness: 3
strengths: - The idea of specifically looking at privacy risk associated with structured/tabular data in LLM training data is, from what I know of the literature, novel and interesting. 
- The paper is well written and clearly structured. 
- I find the related work very well written and easy to follow. It might be worth adding this recent work of reference-based MIAs against LLMs [1]. 
- The work has an extensive evaluation framework, considering 6 different encoding formats, multiple models (and sizes), different regimes of memoriation by varying the number of epochs, 3 different MIAs and 5 datasets.  
- Section 5.3 on cross-format generalization is a nice addition. You might want to link this to what others have found to be a 'mosaic memory', i.e. LLMs are very good at piecing together near-duplicate information, even when random tokens separate the information meaningful for membership [2]. 

[1] Hayes, J., Shumailov, I., Choquette-Choo, C. A., Jagielski, M., Kaissis, G., Lee, K., ... & Cooper, A. F. (2025). Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models. arXiv preprint arXiv:2505.18773.

[2] Shilov, I., Meeus, M., & de Montjoye, Y. A. (2024). The Mosaic Memory of Large Language Models. arXiv preprint arXiv:2405.15523.
summary: The paper introduces a benchmark for membership inference attacks on tabular data in LLM finetuning. 

Authors argue that many prior works have focused on evaluating MIAs on unstructured text included in LLM training data, while increasingly LLMs are also trained on more structured, tabular data. At the same time, data entries in tabular data might also contain sensitive information, and the way they are encoded might lead to different trends in privacy risk. 

In this work, they propose a benchmark to evaluate MIAs on tabular data encoded and included in LLM finetuning. They consider 6 encoding formats, 5 datasets, 3 MIAs, 4 models and analyze the results. They confirm that larger models, trained for more epochs memorize more, and find that encodings using less redundancies are more at risk. They also find that their attacks generalize across formats, and apply their MIAs to LLM pretraining.
weaknesses: When discussing the results, I find that, at least in section 5.1, authors devote a substantial amount of time on memorization trends that are already quite well-known and in are in my opinion not the most interesting ones in this benchmark. For instance, the fact that AUC increases with model size or with number of epochs is already well known [1, 2, 3]. Moreover, the absolute value of AUC is ultimately a factor of the finetuning parameters such as learning rate, and is not really what matters in this case. Instead, the relative performance across tabular datasets, and encoding formats is more interesting and worth exploring a bit deeper in my opinion. 

For instance, the difference in MIA performance across datasets in Table 2 is quite striking and interesting. Could you explain this by e.g. examining the length of the records (as longer sequences are more at risk [1,2,3]) or by examining how unique the records are (either in perplexity using the base model as in [3] or in overlap between members and non-members as explored in [4], or simply by analyzing the uniqueness of attributes across records). 

The intuition why certain encoding formats are more at risk than others is quite interesting. However, I find the argument a bit confusing at the moment, i.e. you first mention that methods like HTML and JSON introduce structural redundancies so lower AUC, while in lines 405-406 you mention that longer context would increase the AUC. How are both arguments compatible? It might also be interesting to look at how efficiently each encoding format is tokenized by the target model. 

- Results in Section 5.4 might be questionable. Namely, prior work has found that if members and non-members do not come from exactly the same distribution, any MIA performance might be due to the attack being able to detect a distribution shift rather than the target model memorizing the members [4, 5, 6, 7]. To understand this, I think it's important to include a blind/model-less baseline as in [6,7]. If this baseline is substantially better than a random guess, it would be hard to interpret any of the results from Table 6. 

[1] Kandpal, N., Wallace, E., & Raffel, C. (2022, June). Deduplicating training data mitigates privacy risks in language models. In International Conference on Machine Learning (pp. 10697-10707). PMLR.
	
[2] Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., & Zhang, C. (2022, February). Quantifying memorization across neural language models. In The Eleventh International Conference on Learning Representations.

[3] Meeus, M., Shilov, I., Faysse, M., & de Montjoye, Y. A. Copyright Traps for Large Language Models. In Forty-first International Conference on Machine Learning.

[4] Duan, M., Suri, A., Mireshghallah, N., Min, S., Shi, W., Zettlemoyer, L., ... & Hajishirzi, H. (2024). Do membership inference attacks work on large language models?. arXiv preprint arXiv:2402.07841.

[5] Maini, P., Jia, H., Papernot, N., & Dziedzic, A. (2024). LLM Dataset Inference: Did you train on my dataset?. Advances in Neural Information Processing Systems, 37, 124069-124092.

[6] Meeus, M., Shilov, I., Jain, S., Faysse, M., Rei, M., & de Montjoye, Y. A. (2025, April). Sok: Membership inference attacks on llms are rushing nowhere (and how to fix it). In 2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) (pp. 385-401). IEEE.

[7 ]Das, D., Zhang, J., & Tramèr, F. (2025, May). Blind baselines beat membership inference attacks for foundation models. In 2025 IEEE Security and Privacy Workshops (SPW) (pp. 118-125). IEEE.

**I hope these suggestions help improve the paper and, if addressed, I'd be willing to increase my score.**


Decision: Accept (Poster)