---
title: 'TM-RugPull: Temporal Rug Pull Benchmark'
url: https://www.emergentmind.com/topics/tm-rugpull
type: topic
---

# TM-RugPull: Temporal Rug Pull Benchmark

Searching arXiv for TM-RugPull and closely related rug-pull detection/dataset papers to ground the article in current literature.
A useful way to understand TM-RugPull is as a benchmark design for **causally valid early-warning research** on rug pulls in tokenized ecosystems. Introduced as **“TM-RugPull: A Temporally Sound, Multimodal Dataset for Early Detection of Rug Pulls Across the Tokenized Ecosystem”**, it is a public dataset of **1,028 token projects** spanning **Ethereum, BSC, Polygon, Arbitrum, and Fantom** from **2016 to 2025**, with features extracted **strictly from the first half of each project’s lifespan** and labels grounded in forensic corroboration and expert review [2602.21529]. Its central purpose is to address persistent weaknesses in prior resources—especially temporal leakage, DeFi-only scope, on-chain-only feature design, and ambiguous labels—by providing a leakage-resistant, multimodal benchmark for studying rug-pull dynamics before collapse becomes obvious [2602.21529].

## 1. Definition, scope, and benchmark rationale

TM-RugPull is explicitly framed as a response to a methodological problem in rug-pull research: many existing datasets are not suitable for realistic early detection because they incorporate post-collapse evidence, constrain analysis to narrow on-chain views, focus mainly on DeFi, or rely on weak heuristics and non-public labeling procedures [2602.21529]. The dataset is positioned not merely as another collection of scam tokens, but as a benchmark built to reflect what an analyst could have known *before* a scam fully unfolded [2602.21529].

The dataset’s coverage is broader than earlier DeFi-centric resources. It includes **DeFi**, **meme coins**, **NFT/gaming projects**, and **celebrity-themed or impersonation-based tokens**, rather than only protocol-style DeFi scams [2602.21529]. This breadth is empirically motivated: the paper reports that **non-DeFi tokens account for over 40% of scam cases** in the dataset, indicating that DeFi-only benchmarks omit a substantial part of the attack surface [2602.21529]. This suggests that TM-RugPull should be read as a cross-ecosystem benchmark rather than a protocol-specific fraud corpus.

The benchmark is public and released as **RugPull1K** on **Hugging Face Datasets**, under **CC BY 4.0**, with `rugpull1k.csv`, a data dictionary in `features.md`, and Python scripts for replication and visualization [2602.21529]. The paper treats this release as the public instantiation of TM-RugPull, although it does not explicitly explain the naming distinction between **TM-RugPull** and **RugPull1K** [2602.21529].

## 2. Operational definition of a rug pull and labeling policy

TM-RugPull adopts a conservative operational definition rather than treating all suspicious short-lived tokens as scams. In the paper’s background formulation, a rug pull requires all three of the following: **(i)** an abrupt and near-total withdrawal of liquidity from decentralized exchanges, **(ii)** cessation of meaningful on-chain activity within a short post-withdrawal window, and **(iii)** collapse of token price or trading volume to a non-determinable or negligible state, leaving the asset effectively worthless [2602.21529].

In dataset construction, this is tightened into a concrete labeling rule. A project is labeled as a rug pull **if and only if all three conditions hold for at least 72 consecutive hours**. Those conditions are stated as **zero liquidity**, **zero meaningful transactional activity**, and **undefined or untraceable price/volume** on major DEXs [2602.21529]. This rule makes the benchmark deliberately precision-oriented: suspicious behavior alone is insufficient, and ambiguous or borderline failures are excluded rather than folded into the positive class [2602.21529].

Labeling is also evidence-driven. A project is labeled **scam/rug pull** only when it both satisfies the formal operational rule and is **corroborated by at least two independent sources**, with **CertiK**, **Rekt.news**, and **De.Fi** named as examples [2602.21529]. A project is labeled **legitimate** only if it shows **more than one year of sustained on-chain activity** and no scam reports over that period [2602.21529]. Ambiguous cases are excluded entirely, reviewed by **at least two analysts**, and final labeling requires **consensus** [2602.21529].

This conservative design has methodological consequences. It reduces weak-label contamination and hindsight-driven categorization, but it also biases the benchmark toward clear-cut cases. A plausible implication is that TM-RugPull prioritizes analytical clarity over exhaustive ecological coverage of gray-area fraud.

## 3. Temporal hygiene and leakage resistance

The dataset’s core design principle is what the paper repeatedly calls **temporal hygiene** or **temporal integrity** [2602.21529]. TM-RugPull introduces an explicit temporal anchor, the **project midpoint**, defined as the halfway point between project initiation and project termination or last observed activity [2602.21529]. Every feature is extracted **strictly before this midpoint**, and all modalities are **temporally aligned at the project midpoint** [2602.21529].

This midpoint policy is meant to prevent contamination from post-collapse evidence such as the final price crash, the liquidity drain itself, or social-media backlash after exposure [2602.21529]. The paper visually validates the policy with a midpoint-ratio figure centered near **0.5**, which it uses to argue that the boundary is applied consistently across projects with heterogeneous lifespans [2602.21529].

The temporal design is one of TM-RugPull’s strongest contributions, but it is also bounded. The paper does **not** provide formal mathematical notation for midpoint computation, temporal window construction, or train/test chronology, and it does **not** describe a chronological train/test split for benchmark experiments [2602.21529]. Thus TM-RugPull is leakage-resistant at the **feature extraction** level, but the paper stops short of defining a full calendar-based evaluation protocol.

This temporal framing closely aligns with later leakage-aware work. The **LROO Rug Pull Detector** paper similarly treats early rug-pull detection as a pre-collapse forecasting problem, using features extracted **strictly prior to any liquidity withdrawal** and evaluating on a **temporally isolated 20\% test set** [2603.11324]. Taken together, these papers indicate a shift from retrospective scam classification toward causal forecasting under explicit temporal constraints [2603.11324].

## 4. Data collection, curation, and multimodal schema

TM-RugPull is built through a **multi-stage workflow** combining **on-chain behavioral data**, **smart contract metadata**, **off-chain OSINT signals**, and **label evidence** under a unified temporal framework [2602.21529]. All projects are sourced from **real deployed smart contracts on public blockchains**, and the dataset contains **no synthetic, simulated, or artificially generated data** [2602.21529].

The curation process is unusually stringent. All **1,028 token projects** were **individually inspected** by a team of **three blockchain security analysts** with expertise in DeFi, NFTs, and token forensics [2602.21529]. The authors excluded projects with **incomplete on-chain records**, **inaccessible contracts**, **inconsistent metadata**, or **ambiguous behavioral patterns**, reporting that this filtering step removed **over 200 borderline candidates** [2602.21529]. This reinforces the benchmark’s emphasis on precision and reproducibility.

The schema is organized into **four conceptual categories** [2602.21529]:

| Category | Representative contents | Intended role |
|---|---|---|
| On-chain behavioral | token concentration metrics, holder distribution variance, transaction activity statistics, quarterly price indicators | structural manipulation and early market dynamics |
| Smart contract metadata | blockchain platform, consensus mechanism, token standard, contract deployment status | project and execution context |
| OSINT signals | Google Search hits/search visibility, Twitter/X activity/volume, social media activity indicators | hype, promotion, and off-chain precursors |
| Temporal anchors | project lifecycle timestamps, midpoint boundary, label provenance information | temporal alignment and provenance |

The paper does **not** provide a total feature count, a full field-by-field schema, or per-column descriptive statistics [2602.21529]. However, it highlights representative variables such as **token concentration ratio**, **holder variance among the top 1% holders**, and **quarterly price dynamics** on the on-chain side, and **Google Search hits** and **Twitter/X activity** on the OSINT side [2602.21529]. A central claim is that these modalities are not simply concatenated but **temporally aligned and capped at the midpoint**.

This multimodal design differentiates TM-RugPull from earlier code-only and transaction-only approaches. **CRPWarner** is a static-analysis system for **contract-related rug pulls** with strong code semantics but no OSINT layer [2403.01425]. **RPHunter** fuses contract risk and transaction behavior through graph learning, but it remains centered on code-and-transaction fusion rather than temporally aligned OSINT [2506.18398]. TM-RugPull occupies a complementary position: it is a dataset whose novelty lies in the combination of **cross-category token diversity**, **multimodal evidence including OSINT**, and **strict temporal hygiene** [2602.21529].

## 5. Empirical observations and illustrative benchmarking

The descriptive analysis reported for TM-RugPull identifies several characteristic differences between scam and legitimate projects. First, as noted above, **over 40% of scam cases are outside DeFi**, supporting the decision to include meme, NFT, and celebrity-themed tokens [2602.21529]. Second, scam tokens show **significantly higher token concentration** and **higher holder variance among top 1% holders** than legitimate projects [2602.21529]. In the conclusion, the paper specifies that scam tokens exhibit significantly higher token concentration with **\( p < 0.001 \)** under a **Mann–Whitney U test** [2602.21529].

Third, the temporal alignment analysis suggests that **OSINT activity peaks well before the midpoint while price remains stable**, which the authors interpret as evidence of an authentic early-warning window [2602.21529]. In the sample illustration, **Twitter volume and Google search interest surge during Q1–Q2** before overt on-chain collapse [2602.21529]. This suggests that off-chain hype may precede visible on-chain deterioration, providing a concrete rationale for multimodal early detection.

Benchmarking in the paper is explicitly **illustrative** rather than exhaustive [2602.21529]. The authors compare models trained on the **full benchmark** against models trained on a **DeFi-only subset**, while keeping **feature definitions, temporal constraints, labeling protocol, and preprocessing** fixed [2602.21529]. They use lightweight tabular classifiers, specifically **logistic regression** and **random forest**, not to establish model superiority but to isolate the contribution of cross-domain diversity [2602.21529].

The main result is qualitative: models trained on the **full benchmark** show **clearer separation** between scam and legitimate tokens, with less overlap near the decision boundary, whereas models trained on the **DeFi-only subset** show more overlap and greater uncertainty [2602.21529]. The paper interprets this as evidence that **heterogeneous token categories** and **temporally aligned multimodal signals** improve separability and reduce overfitting to DeFi-specific artifacts [2602.21529]. It further claims a **significant decrease in false positives**, though it does **not** provide standard benchmark metrics such as AUROC, F1, precision, recall, PR-AUC, or confusion matrices [2602.21529].

A later modeling paper operationalizes a closely related design philosophy at the detector level. The **LROO Rug Pull Detector** integrates on-chain and OSINT signals under strict temporal constraints and reports strong discriminative performance with **TabPFN**, including **0.982 accuracy**, **0.982 F1**, **0.997 ROC AUC**, and **0.997 PR AUC** on a **temporally isolated 20\% test set** [2603.11324]. Because that framework is described as built around the same design direction later referenced as TM-RugPull, it can be read as a modeling blueprint for how a leakage-resistant multimodal benchmark can be operationalized [2603.11324].

## 6. Position within the rug-pull literature

TM-RugPull sits at the intersection of several strands of rug-pull research. Earlier work such as **“Do not rug on me: Zero-dimensional Scam Detection”** built pre-rug classifiers for Uniswap V2 using token propagation, liquidity-pool, and smart-contract heuristics, but within a narrower Ethereum/Uniswap setting [2201.07220]. **“Token Spammers, Rug Pulls, and SniperBots”** provided a large-scale analysis of **1-day rug pulls** on Ethereum and BSC, emphasizing disposable-token campaigns, creator concentration, and LP-event structure [2206.08202]. NFT-focused work has instead emphasized creator networks, contract backdoors, or marketplace behavior, as in **“Understanding Rug Pulls: An In-Depth Behavioral Analysis of Fraudulent NFT Creators”** [2304.07598] and **“A Deep Dive into NFT Rug Pulls”** [2305.06108].

On the code-analysis side, **CRPWarner** formalized **contract-related rug pulls** through malicious functions such as **Hidden Mint Function**, **Limiting Sell Order**, and **Leaking Token**, evaluated via bytecode-level static analysis [2403.01425]. NFT contract-focused static analysis later catalogued hidden backdoors such as `selfdestruct`, `delegatecall`, unrestricted minting, and owner-only withdrawal patterns in verified NFT contracts [2506.07974]. More recent multimodal work, especially **RPHunter**, has fused semantic code graphs and transaction-behavior graphs to capture the interplay between malicious token logic and suspicious early transaction evolution [2506.18398].

TM-RugPull’s distinctiveness lies in benchmark design rather than detector architecture. The paper presents it as the first public dataset to combine **multi-chain coverage**, **cross-category token diversity**, **multimodal features including OSINT**, **strict temporal hygiene**, and **manually verified labels grounded in forensic evidence** [2602.21529]. In that sense, its main contribution is not only broader coverage but a more principled notion of what an early-warning benchmark should look like.

The dataset also highlights a tension in the literature between breadth and label sharpness. Systems like **SolRPDS** provide very large liquidity-centric datasets for Solana, but explicitly note that **inactivity is a signal for suspicious behavior and not definitive evidence for rug pull** [2504.07132]. TM-RugPull instead sacrifices scale to preserve stronger label semantics and human verification [2602.21529]. This suggests a methodological divide between large proxy-label corpora and smaller, forensic-grade benchmarks.

## 7. Limitations, naming issues, and future directions

The paper does not collect its limitations into a dedicated section, but several are clear. Coverage is restricted to **five blockchains**—Ethereum, BSC, Polygon, Arbitrum, and Fantom—so TM-RugPull is not comprehensive across all token ecosystems [2602.21529]. Because labels require strong forensic corroboration and long observation periods for legitimate tokens, the benchmark may underrepresent borderline or emerging scams [2602.21529]. The exclusion of **over 200 ambiguous candidates** improves precision but narrows ecological coverage [2602.21529].

The use of OSINT signals such as Google Search and Twitter/X introduces possible issues of platform noise, manipulation, and measurement instability, though the paper does not elaborate them in detail [2602.21529]. The legitimacy rule requiring **more than one year of sustained on-chain activity** may also induce a form of survivorship bias by making the negative class cleaner than what real-time deployment would face [2602.21529]. Finally, despite the emphasis on temporal soundness, the paper does not define a formal chronological benchmark protocol, leaving future work to specify calendar-based train/test generalization [2602.21529].

A mild naming inconsistency also remains unresolved. The title and body call the benchmark **TM-RugPull**, while the data-availability section names the release **RugPull1K** [2602.21529]. The paper appears to treat them as the same benchmark, but it does not explicitly explain the distinction.

Future work is strongly implied rather than fully specified. The most immediate extensions would be more formal temporal evaluation protocols, richer schema statistics, modality ablations, and broader chain coverage. A plausible implication is that TM-RugPull will be most influential not merely as a dataset to train on, but as a template for how rug-pull benchmarks should be constructed: temporally bounded, multimodal, forensically labeled, cross-ecosystem, and public.

Source: https://www.emergentmind.com/topics/tm-rugpull