Web security has become a critical domain as modern applications increasingly rely on dynamic user-generated content, making them highly vulnerable to Cross-Site Scripting (XSS) attacks. Traditional detection systems struggle to cope with evolving payload patterns, limited generalisation across institutions, and strict privacy restrictions that prevent sharing of sensitive request logs. To address these challenges, this work proposes a Zero-Knowledge Federated Sequence Learning (ZK-FSL) framework that enables collaborative XSS detection without exposing raw data or intermediate gradients. The model integrates attention-based deep sequence learning with zero-knowledge proof validation, ensuring both strong predictive capability and verifiable trust among participating clients. Experimental evaluation demonstrates that ZK-FSL achieves superior performance compared to centralised and federated baselines, reaching 96.3% accuracy , 96.7% precision , 95.9% recall , 96.3% F1-score , and an AUC of 0.98 . These results confirm that the proposed framework effectively enhances privacy-preserving threat detection while maintaining high robustness against diverse and sophisticated XSS attack patterns.
Ethereum, one of the largest blockchain platforms, manages millions of dollars in digital assets daily and depends on consistent community standards and cohesive implementations to maintain network consensus. This study introduces a novel methodology for identifying and measuring temporal inconsistencies in opcode mappings across Ethereum clients and the Solidity compiler. Using this methodology, we uncover significant and previously undocumented discrepancies both among different components of Ethereum’s implementation and between theoretical specifications and practical execution behavior. We further demonstrate the potential consequences of these inconsistencies by illustrating their impact on the detection of vulnerabilities in Solidity smart contracts. Such discrepancies can substantially undermine the accuracy and reliability of vulnerability analysis, highlighting the critical need for strict consistency in Ethereum’s operational semantics. Ensuring this consistency is essential for preserving transaction security and the overall robustness of the platform.
Arman Kolozyan, Tom Sorger, Alexander Hicks, Stefanos Chaliasos
Zero-knowledge proofs (ZKPs) have become a core technology for privacy and verifiable computing. They are used to secure blockchains that handle billions of dollars and identity applications dealing with sensitive personal data. However, ZKP systems are complex, and subtle implementation errors can completely break their guarantees, letting attackers forge money or false proofs of identity. Researchers and practitioners have therefore developed a growing set of bug detection and formal verification methods to secure these systems. Yet their real-world effectiveness and adoption remain unclear. In this paper, we aim to shed light on the state of ZKP security tooling. We first systematize the landscape of these tools and observe that most target Circom, leaving newer DSLs and zkVMs with limited support. We then evaluate six tools across 70 real-world vulnerabilities and find that while the tools detect 45.7% of bugs on isolated targets, their effectiveness drops to 19.6% on full codebases, with important vulnerability classes left unaddressed. We also present the first systematic analysis of formal verification efforts, revealing that current work focuses primarily on constraint correctness and identifying key gaps and risks. Finally, we survey 48 practitioners, showing that development and security remain human-led, LLMs are widely used, and practitioners prioritize tools with clearer guarantees and lower integration effort. Overall, our results highlight the need for better integration of security tooling with the development and auditing process, and we provide actionable insights for researchers and practitioners.