How To Cook The Fragmented Rug Pull?

• URL: http://arxiv.org/abs/2511.15463v1
• Date: Wed, 19 Nov 2025 14:22:07 GMT
• Title: How To Cook The Fragmented Rug Pull?
• Abstract summary: rug pull detectors assume a simple workflow: the deployer keeps liquidity pool (LP) tokens and performs one or a few large sells (within a day) that collapse the pool and cash out.<n>In practice, many real-world exits violate these assumptions by splitting the attack across both time and actor dimensions.<n>We formalize this family of attacks as the fragmented rug pull (FRP) and offer a compact recipe for a slow-stewed beef special.
• Score: 5.849850957491873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing rug pull detectors assume a simple workflow: the deployer keeps liquidity pool (LP) tokens and performs one or a few large sells (within a day) that collapse the pool and cash out. In practice, however, many real-world exits violate these assumptions by splitting the attack across both time and actor dimensions: attackers break total extraction into many low-impact trades and route proceeds through multiple non-owner addresses, producing low-visibility drains. We formalize this family of attacks as the fragmented rug pull (FRP) and offer a compact recipe for a slow-stewed beef special: (i) keep the lid on (to preserve LP control so on-chain extraction remains feasible), (ii) chop thin slices (to split the total exit volume into many low-impact micro-trades that individually fall below impact thresholds), and (iii) pass the ladle (to delegate sells across multiple wallets so that each participant takes a small share of the extraction). Technically, we define three atomic predicate groups and show that their orthogonal combinations yield evasive strategies overlooked by prior heuristics (USENIX Sec 19, USENIX Sec 23). We validate the model with large-scale measurements. Our corpus contains 303,614 LPs, among which 105,434 are labeled as FRP pools. The labeled subset includes 34,192,767 pool-related transactions and 401,838 inflated-seller wallets, involving 1,501,408 unique interacting addresses. Notably, owner-wallet participation in inflated selling among FRP-flagged LPs has declined substantially (33.1% of cases), indicating a shift in scam behavior: the liquidity drain is no longer held on the owner wallet. We also detected 127,252 wallets acting as serial scammers when repeatedly engaging in inflated selling across multiple FRP LPs. Our empirical findings demonstrate that the evasive strategies we define are widespread and operationally significant.

Related papers

• ZAPs: A Reward Attribution Framework for DeFi Ecosystems with Adversarial-Robust Scoring via Parallel Anomaly Ensemble Detection [0.5689746155812007]
  We present ZAPs, a reward attribution framework that combines economic contribution scoring with adversarial robustness.<n>A two-layer weighting mechanism combines protocol share within sector and sector share within the ecosystem.<n>ZAPs also introduces a four-layer defense stack consisting of transaction-level integrity checks, a parallel anomaly ensemble, post-distribution behavioral memory, and graph-based sybil clustering.
  arXiv  Detail & Related papers  (2026-07-30T08:36:26Z)
• Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks [45.528190718580724]
  Bit-Flip Attacks can serve as an attack vector for inducing decision-level hijacking.<n>CogBias is a cognitive bias injection framework for Large Language Models.
  arXiv  Detail & Related papers  (2026-07-28T02:59:32Z)
• Incentives and Market Structure in Intent-Based Exchanges: Evidence from a Solver-Reward Reform [2.5599656137521425]
  We measure how a change to protocol-designed reward rules reshapes who captures value.<n>CoW Protocol CIP-74 replaced a fixed solver-reward cap with one tied to protocol revenue and introduced an ad-valorem volume fee.<n>Using daily solver shares over 395 days, we find the reform reallocated trading value by order size.
  arXiv  Detail & Related papers  (2026-07-24T04:04:15Z)
• All-out Attack: Optimal Block Withholding Under Pay-Per-Share Scheme [45.776687601070705]
  We analyze Block Withholding attacks under Pay-Per-Share and Full-PPS schemes for Nakamoto-style blockchains.<n>Under PPS/FPPS, the optimal strategy for a BWH attacker is the All-out Attack (AoA)<n> advanced BWH variants such as Fork After Withholding (FAW) yield no additional profit under PPS/FPPS.
  arXiv  Detail & Related papers  (2026-07-01T17:46:37Z)
• Layer-Isolated Evaluation: Gating the Deterministic Scaffold of a Production LLM Agent with a No-LLM, Regression-Locked Test Harness [45.148328075418156]
  A deployed ordering agent is decomposed into a fixed taxonomy of layers.<n>The pure suite runs in CI on every change against a locked per-slice baseline.<n>We validate by controlled regression injection, degrading one layer at a time across seven non-safety layers.
  arXiv  Detail & Related papers  (2026-06-10T05:55:13Z)
• Training Language Models for Bilateral Trade with Private Information [47.25254982913256]
  Bilateral trade demands individual, strategic surplus rationality, and cooperation to realize gains from trade.<n>We develop a structured bargaining environment where large language models negotiate via tool calls within an event-driven simulator.<n>The environment serves two purposes: a benchmark for frontier models and as a training environment for open-weight models via reinforcement learning.
  arXiv  Detail & Related papers  (2026-04-10T03:04:20Z)
• TxRay: Agentic Postmortem of Live Blockchain Attacks [52.658018348998105]
  Within five years, the DeFi ecosystem has lost over 15.75B USD to reported exploits.<n>We present TxRay, a postmortem system that reconstructs live ACT attacks from limited evidence.<n>On 114 incidents from DeFiHackLabs, TxRay produces an expert-aligned root cause and an executable PoC for 105 incidents, achieving 92.11% end-to-end reproduction.
  arXiv  Detail & Related papers  (2026-02-01T16:17:33Z)
• How to Serve Your Sandwich? MEV Attacks in Private L2 Mempools [0.0]
  We study the feasibility, profitability, and prevalence of sandwich attacks on rollups with private mempools.<n>Our results suggest that sandwiching, while endemic and profitable on L1, is rare, unprofitable, and largely absent in rollups with private mempools.
  arXiv  Detail & Related papers  (2026-01-27T13:00:33Z)
• Bits Leaked per Query: Information-Theoretic Bounds on Adversarial Attacks against LLMs [47.12608115550359]
  Adrial attacks by malicious users that threaten the safety of large language models (LLMs) can be viewed as attempts to infer a target property $T$ that is unknown when an instruction is issued.<n>We show that achieving error $varepsilon$ requires at least $log (1/varepsilon)/I(Z;T)$ queries, scaling linearly with the inverse leak rate and only logarithmically with the desired accuracy.
  arXiv  Detail & Related papers  (2025-10-19T20:51:24Z)
• On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation [4.37137147573556]
  Billions of dollars are lost every year in DeFi platforms by transactions exploiting business logic or accounting vulnerabilities.<n>We present the first decentralized, fully on-chain learning framework that: (i) performs gas-prohibitive computation on Layer-2 to reduce cost, (ii) propagates verified model updates to Layer-1, and (iii) enables gas-bounded low-latency inference inside smart contracts.
  arXiv  Detail & Related papers  (2025-10-15T18:58:34Z)
• When Priority Fails: Revert-Based MEV on Fast-Finality Rollups [0.0]
  We study the economics of transaction reverts on rollups and show that they are not accidental failures but equilibrium outcomes of MEV strategies.<n>We find that over 80% of reverted transactions are swaps, with half targeting USDC-WETH pools on Uniswap v3, v4.<n>Our findings establish reverts as a structural feature of rollup MEV microstructure and highlight the need for protocol-level reforms to sequencing, fee markets, and revert protection.
  arXiv  Detail & Related papers  (2025-06-02T09:18:53Z)
• SolRPDS: A Dataset for Analyzing Rug Pulls in Solana Decentralized Finance [0.6367946001576646]
  Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi)<n>A rug pull occurs when developers exploit users' trust and drain liquidity from token pools on Decentralized Exchanges (DEXs)<n>We introduce SolRPDS, the first public rug pull dataset derived from Solana's transactions.
  arXiv  Detail & Related papers  (2025-04-06T11:36:48Z)
• The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training [51.84624027213658]
  We propose Blockwise Learning Rate (LR), a strategy that tailors the LR to each block's sharpness.<n>We demonstrate this acceleration across GPT-2 and LLaMA, with model sizes ranging from 0.12B to 2B.<n>We incorporate Blockwise LR into Adam-mini, a recently proposed memory-efficient variant of Adam, achieving a combined $2times$ speedup and $2times$ memory saving.
  arXiv  Detail & Related papers  (2025-02-26T10:06:37Z)
• Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning [49.48615590763914]
  We propose a black-box attack algorithm named LCBT, which uses the Monte Carlo tree search method for efficient action searching and manipulation. We conduct our proposed attack methods on three aggressive algorithms: DDPG, PPO, and TD3 in continuous settings, which show a promising attack performance.
  arXiv  Detail & Related papers  (2024-11-20T08:20:29Z)
• G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering [116.4277292854053]
  Federated Learning (FL) offers collaborative model training without data sharing. FL is vulnerable to backdoor attacks, where poisoned model weights lead to compromised system integrity. We present G$2$uardFL, a protective framework that reinterprets the identification of malicious clients as an attributed graph clustering problem.
  arXiv  Detail & Related papers  (2023-06-08T07:15:04Z)
• CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning [63.72975421109622]
  CleanCLIP is a finetuning framework that weakens the learned spurious associations introduced by backdoor attacks. CleanCLIP maintains model performance on benign examples while erasing a range of backdoor attacks on multimodal contrastive learning.
  arXiv  Detail & Related papers  (2023-03-06T17:48:32Z)
• Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond [57.10914865054868]
  We consider vertical logistic regression (VLR) trained with mini-batch descent gradient. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
  arXiv  Detail & Related papers  (2022-07-19T05:47:30Z)
• Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation [122.83280749890078]
  We propose an improved certified defense against general poisoning attacks, namely Finite Aggregation. In contrast to DPA, which directly splits the training set into disjoint subsets, our method first splits the training set into smaller disjoint subsets. We offer an alternative view of our method, bridging the designs of deterministic and aggregation-based certified defenses.
  arXiv  Detail & Related papers  (2022-02-05T20:08:58Z)
• Strategic Liquidity Provision in Uniswap v3 [13.436603092715247]
  A liquidity provider (LP) allocates liquidity to one or more closed intervals of the price of an asset. We formalize the dynamic liquidity provision problem and focus on a general class of strategies for which we provide a neural network-based optimization framework.
  arXiv  Detail & Related papers  (2021-06-22T19:48:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.