Security
Who Swept My ETH? Uncovering EIP-7702-Based Ether Redirection Attacks on Ethereum
arXiv cs.CR0 views
arXiv:2610.41153v1 Announce Type: new Abstract: EIP-7702 introduces a new transaction type that allows Externally Owned Accounts (EOAs) on major EVM-compatible chains to delegate execution to arbitrary contract codes temporarily. While enhancing functionality, this mechanism facilitates a novel threat: the \underline{E}IP-7702-Based \underline{E}ther \underline{R}edirection \underline{A}ttack (EERA). In such an attack, a victim is delegated to a malicious sweeper contract, which automatically redirects incoming ETH transfers to an address controlled by the attacker. As a nascent attack paradigm arising from protocol evolution, the underlying mechanisms and security implications of EERA remain systematically unexplored. To bridge this gap, we provide the first formal analysis of the EERA attack model and propose SweeperHunter, a framework that detects these attacks through transaction analysis. By evaluating transactions from the first ten months following the introduction of EIP-7702 on Ethereum mainnet, we have identified 4,668,209 malicious delegations, 20,443 attack transactions and 508 ETH sweeper contracts, forming the first EERA dataset. Finally, we analyze this attack from both attacker and victim perspectives, derive key insights, and propose corresponding mitigation strategies. We reveal the prevalence of EERA, as approximately 70\% of the observed delegations point toward ETH sweeper contracts. Furthermore, we observe a notable lack of effective responses from victims; specifically, over 600,000 accounts failed to revoke or update their malicious delegations promptly, thereby remaining in a state of vulnerability. Our research is expected to facilitate understanding and mitigation of this emerging threat posed by EIP-7702.
Source:https://arxiv.org/abs/2610.41153PreviousEvaluating Structural and SAT Attack Resilience in Cryptographic Dynamic Logic Locking via Keystream CappingNextPP-Mark: Provable and Publicly Verifiable Watermarking for Generative AI
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