A Lifecycle-Oriented Detection and Defense Framework for Price Manipulation Attacks in DeFi

• URL: http://arxiv.org/abs/2608.15518v1
• Date: Sun, 16 Aug 2026 04:01:42 GMT
• Title: A Lifecycle-Oriented Detection and Defense Framework for Price Manipulation Attacks in DeFi
• Abstract summary: This paper investigates attack patterns, vulnerability types, risk assessment, automated detection, and defense strategies.<n>Based on Oracle lifecycle theory, a three-layer attack tree is constructed to analyze the attack chain.<n>Experiments show that the proposed approach reduces attack-induced price deviation from 55.56% to below 5%.
• Score: 4.805862074245663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aiming at frequent oracle price manipulation attacks in Decentralized Finance (DeFi), this paper investigates attack patterns, vulnerability types, risk assessment, automated detection, and defense strategies. Based on Oracle lifecycle theory, a three-layer attack tree covering the physical, protocol, and application layers is constructed to analyze the attack chain and identify the data election stage as a key intrusion point. Four major contract-level vulnerabilities are summarized: insufficient validation of price data flow, oracle call risks, uncontrolled cross-contract calls, and defects in AMM price reading logic. Fuzzy-AHP and Value-at-Risk (VaR) models are combined to quantify risk factors and construct a risk matrix covering technology, market, governance, and contract risks. An automated detection tool based on an extended Slither framework is developed using taint tracking and pattern matching. Finally, a multi-layer defense strategy is proposed, integrating trusted execution environments at the data source layer, TWAP smoothing and adaptive circuit breakers at the smart contract layer, and optimized kernel network configurations at the operating system layer. Experiments show that the proposed approach reduces attack-induced price deviation from 55.56% to below 5%. The detection tool achieves 94.38% accuracy and 92.31% recall, outperforming existing open-source tools.

Related papers

• 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)
• WMAttack: Automated Attack Search for Adversarial Evaluation of World-Model Agents [53.84430233754606]
  We introduce WMAttack, an automated attack-search framework for adversarial evaluation of world-model agents.<n> WMAttack formulates robustness evaluation as a finite-budget search over attack configurations.<n>We show that WMAttack consistently discovers stronger attacks than the evaluated baselines.
  arXiv  Detail & Related papers  (2026-05-22T04:31:09Z)
• Exploiting LLM Agent Supply Chains via Payload-less Skills [10.141577783380281]
  We introduce Semantic Compliance Hijacking (SCH), a payload-less supply chain attack targeting autonomous coding environments.<n>The SCH approach translates malicious goals into unstructured natural language instructions formatted as necessary compliance rules, leading the agent to generate and execute unauthorized code.<n>The findings demonstrate the pervasive nature of this threat, with SCH achieving peak success rates of up to 77.67% for confidentiality breaches.
  arXiv  Detail & Related papers  (2026-05-14T06:55:47Z)
• From Risk to Rescue: An Agentic Survival Analysis Framework for Liquidation Prevention [46.90931293070464]
  We propose an autonomous agent that perceives risk, simulates counterfactual futures, and executes protocol-faithful interventions to proactively prevent liquidations.<n>We validate our approach using a high-fidelity, protocol-faithful Aave v3 simulator on a cohort of 4,882 high-risk user profiles.
  arXiv  Detail & Related papers  (2026-04-16T03:22:05Z)
• Reward Under Attack: Analyzing the Robustness and Hackability of Process Reward Models [68.45272703833209]
  We show that state-of-the-art PRMs are systematically exploitable under adversarial optimization pressure.<n>We introduce a three-tiered diagnostic framework that applies increasing adversarial pressure to quantify these vulnerabilities.<n>We release PRM-BiasBench and a diagnostic toolkit to enable robustness evaluation before deployment.
  arXiv  Detail & Related papers  (2026-02-20T23:38:03Z)
• Conformal Thinking: Risk Control for Reasoning on a Compute Budget [60.65072883773352]
  Reasoning Large Language Models (LLMs) enable test-time scaling, with dataset-level accuracy improving as the token budget increases.<n>We re-frame the budget setting problem as risk control, limiting the error rate while minimizing compute.<n>Our framework introduces an upper threshold that stops reasoning when the model is confident and a novel lower threshold that preemptively stops unsolvable instances.
  arXiv  Detail & Related papers  (2026-02-03T18:17:22Z)
• Quantifying Return on Security Controls in LLM Systems [0.0]
  This paper introduces a decision-oriented framework to quantify residual risk.<n>It converts adversarial probe outcomes into financial risk estimates and return-on-control metrics.
  arXiv  Detail & Related papers  (2025-12-17T04:58:09Z)
• LLM-Powered Detection of Price Manipulation in DeFi [12.59175486585742]
  Decentralized Finance (DeFi) smart contracts manage billions of dollars, making them a prime target for exploits.<n>Price manipulation vulnerabilities, often via flash loans, are a devastating class of attacks causing significant financial losses.<n>We propose PMDetector, a hybrid framework combining static analysis with Large Language Model (LLM)-based reasoning.
  arXiv  Detail & Related papers  (2025-10-24T09:13:30Z)
• VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection [55.957275374847484]
  VulAgent is a multi-agent vulnerability detection framework based on hypothesis validation.<n>It implements a semantics-sensitive, multi-view detection pipeline, each aligned to a specific analysis perspective.<n>On average, VulAgent improves overall accuracy by 6.6%, increases the correct identification rate of vulnerable--fixed code pairs by up to 450%, and reduces the false positive rate by about 36%.
  arXiv  Detail & Related papers  (2025-09-15T02:25:38Z)
• LLM-BSCVM: An LLM-Based Blockchain Smart Contract Vulnerability Management Framework [5.88442044992561]
  LLM-BSCVM is a Large Language Model-based smart contract vulnerability management framework.<n>It provides end-to-end vulnerability detection, analysis, repair, and evaluation capabilities for Web 3.0 ecosystem.<n>It achieves a vulnerability detection accuracy and F1 score exceeding 91% on benchmark datasets.
  arXiv  Detail & Related papers  (2025-05-23T03:05:09Z)
• AI-Based Vulnerability Analysis of NFT Smart Contracts [6.378351117969227]
  This study proposes an AI-driven approach to detect vulnerabilities in NFT smart contracts.<n>We collected 16,527 public smart contract codes, classifying them into five vulnerability categories: Risky Mutable Proxy, ERC-721 Reentrancy, Unlimited Minting, Missing Requirements, and Public Burn.<n>A random forest model was implemented to improve robustness through random data/feature sampling and multitree integration.
  arXiv  Detail & Related papers  (2025-04-18T08:55:31Z)
• Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks [47.18575262588692]
  We propose a novel Multi-Space Prototypical Learning framework tailored for few-shot attack detection.<n>By leveraging Polyak-averaged prototype generation, the framework stabilizes the learning process and effectively adapts to rare and zero-day attacks.<n> Experimental results on benchmark datasets demonstrate that MSPL outperforms traditional approaches in detecting low-profile and novel attack types.
  arXiv  Detail & Related papers  (2024-12-28T00:09:46Z)

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.