With the widespread adoption of IPv6, its vast address space presents significant challenges for network asset discovery. Traditional exhaustive scanning approaches are no longer practical, while strategies based on target generation algorithms are increasingly undermined by the existence of Fully Responsive Prefixes (FRPs)—prefixes in which all addresses appear responsive to probing. FRPs distort scanning results, introducing bias and inefficiency. Existing FRP probing techniques suffer from limited scalability, poor accuracy, and restricted applicability in large-scale IPv6 environments.To this end, we propose Gungnir, a multi-protocol unified FRP probing algorithm based on autoregressive semantic modeling. Gungnir captures the intricate relationships between FRP patterns and their influencing factors through a deep semantic learning architecture. It leverages prefix inference and a granularity correction mechanism to accurately predict and validate FRPs, while mitigating errors from incorrect prefix-length estimation. Extensive experiments demonstrate that Gungnir outperforms state-of-the-art techniques, achieving up to 27× higher efficiency, 4.2× wider address space coverage, and broader coverage of both autonomous systems and routing prefixes under the same probing budget. Beyond performance, we further analyze the service and port distributions of the discovered FRPs, uncovering operational patterns and potential security implications. These insights offer valuable guidance for IPv6 measurement, address discovery, and network defense.

The IPv6 routing infrastructure is an important part of the modern Internet, and the collection of its interface addresses is greatly significant in network security, performance optimization, and measurement analysis. However, existing methods suffer from two major problems: the lack of flexibility in budget allocation across probing rounds and the absence of a dynamic hop limit adjustment mechanism based on feedback. These problems lead to the low hit rate and inefficiency of existing methods for discovering router interfaces, which seriously hinders the comprehensive knowledge of IPv6 routing infrastructure.To this end, we propose Helixir, a feedback-based, high hit-rate, and efficient IPv6 router interface discovery system. Helixir’s core design includes a dynamic budget allocation mechanism across probing rounds, an inter-prefix budget allocation strategy that adequately trades off exploration and exploitation, and a hop limit selection method based on Thompson sampling. Real-world experiments show that with a 100M budget, Helixir achieves a hit rate 3.64× that of state-of-the-art methods on the BGP prefixes dataset, and Helixir successfully discovers over 31 million IPv6 router interface addresses in total within half an hour. In addition, a systematic security analysis of the discovered router interfaces shows that many devices open sensitive ports and expose hundreds of potential CVE vulnerabilities, highlighting the security risks in the IPv6 network.

The vastness of the IPv6 address space has led to the common practice of allocating prefixes to end users rather than individual addresses. Users can assign these prefixes as a single subnet or divide them into multiple subnets for different purposes. Allocation strategies vary significantly in terms of prefix granularity, and identifying the actual granularity of subnet assignments is crucial for improving measurement efficiency, accuracy, and for better IPv6 network management. However, no existing method can discover IPv6 subnets at an Internet-Wide scale.To this end, we propose SubRecon, an Internet-Wide IPv6 subnet discovery system. SubRecon consists of two key phases: subnet delimitation and target expansion. In the subnet delimitation phase, we perform a systematic scan across the entire IPv6 address space without relying on any existing seed dataset. This phase adopts a top-down approach, probing prefixes from the shortest to the longest in a hierarchical manner. At each level, we recursively refine prefixes and discard sub-prefixes that do not meet the convergence condition. This pruning strategy eliminates redundant probes in unallocated regions, significantly reducing the search space and improving probing efficiency. To further improve coverage, the target expansion phase leverages the active address dataset as an auxiliary input. It identifies active addresses not covered by previously discovered subnets, expands them into new candidate target prefixes, and performs another round of subnet delimitation. This helps enhance the completeness and coverage of the final discovered subnet set. Experimental results show that SubRecon discovers 8,381,974 IPv6 subnets across 14,147 autonomous systems, and the resulting subnet list can serve as high-quality input for topology discovery. Additionally, during the subnet discovery process, SubRecon identifies a large number of last-hop router interfaces, discovering approximately 10 million more than the current state-of-the-art methods.

With the large-scale deployment and application of IPv6, IPv6 network measurements will become increasingly important. However, a special type of IPv6 prefix called Fully Responsive Prefix (FRP) is having a significant impact on IPv6 measurement campaigns, which is defined as all addresses under a prefix responding to scans. Obviously, there cannot be a real responder behind each of these addresses. To reveal the current status and impact of Internet-wide IPv6 FRPs, we propose for the first time an active probing method for Internet-wide IPv6 FRPs, Luori, which transforms the active probing process under IPv6 huge prefix space (potential range of prefix presence) into a dynamic search process in a tree based on reinforcement learning, achieving efficient probing of arbitrary routing prefixes. The evaluation results show that Luori found 31.7K largest FRPs in a single Internet-wide probing with 11 M budget, covering$1.5 \times 10^{30}$address space, which is$10^{6} \times$that of existing methods. More importantly, after six months of Internet-wide probing, we have found 516 K largest FRPs, which covers$1.3 \times 10^{33}$address space and 795 ASes, making it the largest publicly known FRP list. Based on this list, we screen out$20 \%$of the addresses covered by FRPs from a well-known IPv6 active address dataset. Furthermore, we further analyze and find that the distribution of these FRPs is extensive and their implementation methods are diverse, which can provide beneficial references for the practical application of FRPs. We also make this list publicly available and maintain it long-term for use and study by relevant researchers.