Srivatsan Sridhar (Stanford University), Onur Ascigil (Lancaster University), Navin Keizer (University College London), François Genon (UCLouvain), Sébastien Pierre (UCLouvain), Yiannis Psaras (Protocol Labs), Etienne Riviere (UCLouvain), Michał Król (City, University of London)

The InterPlanetary File System (IPFS) is currently the largest decentralized storage solution in operation, with thousands of active participants and millions of daily content transfers. IPFS is used as remote data storage for numerous blockchain-based smart contracts, Non-Fungible Tokens (NFT), and decentralized applications.

We present a content censorship attack that can be executed with minimal effort and cost, and that prevents the retrieval of any chosen content in the IPFS network. The attack exploits a conceptual issue in a core component of IPFS, the Kademlia Distributed Hash Table (DHT), which is used to resolve content IDs to peer addresses. We provide efficient detection and mitigation mechanisms for this vulnerability. Our mechanisms achieve a 99.6% detection rate and mitigate 100% of the detected attacks with minimal signaling and computational overhead. We followed responsible disclosure procedures, and our countermeasures are scheduled for deployment in the future versions of IPFS.

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Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural...

Gorka Abad (Radboud University & Ikerlan Technology Research Centre), Oguzhan Ersoy (Radboud University), Stjepan Picek (Radboud University & Delft University of Technology), Aitor Urbieta (Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA))

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Large Language Model guided Protocol Fuzzing

Ruijie Meng (National University of Singapore, Singapore), Martin Mirchev (National University of Singapore), Marcel Böhme (MPI-SP, Germany and Monash University, Australia), Abhik Roychoudhury (National University of Singapore)

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MPCDiff: Testing and Repairing MPC-Hardened Deep Learning Models

Qi Pang (Carnegie Mellon University), Yuanyuan Yuan (HKUST), Shuai Wang (HKUST)

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Differentially Private Dataset Condensation

Tianhang Zheng (University of Missouri-Kansas City), Baochun Li (University of Toronto)

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