Penetration Testing (PT), a key network security assessment technique that simulates real cyber attacks to identify vulnerabilities, is traditionally manual and expert-dependent, leading to low efficiency and high costs. Automating and intelligentizing PT has thus become a critical research focus, yet current technologies face two core challenges: lack of standardized, reusable simulated network scenarios (hindering unified experiments and result comparison) and intelligent models' failure to integrate historical decision experience or utilize attack chain temporal correlations (restricting adaptability). To address these, this study proposes an intelligent PT method integrating knowledge graph-driven automated scenario construction and historical decision enhancement. Two innovations are introduced: a network knowledge graph-based mechanism to generate standardized, real-characteristic testing environments; and a historical decision enhancement scheme with a collaborative state temporal processing and action filtering architecture. Experimental results show the method reduces average iterations by 69%, eliminates redundant executions, and enhances decision rationality, offering a new path for automated PT advancement.

As cyberspace continues to expand, identifying the organization or individual behind a website has become increasingly vital in security incident response, phishing website detection, and other cybersecurity subfields. An existing solution for it involves analyzing webpage content and extracting owner names using named entity recognition techniques. However, since these techniques operate on a sentence-by-sentence basis, they struggle to identify the true owner when multiple individual or organizational names appear on a webpage. Moreover, they often perform poorly on non-English websites. To address these limitations, we propose OwnerHunter, a novel multilingual framework powered by large language models, which formulates website owner identification as a multilingual document-level information extraction task and utilizes global information from webpages to identify the owner. In OwnerHunter, we first craft prompts that fully leverage the capabilities of large language models to effectively recognize potential owners on webpages in different languages with minimal examples. To enhance the comprehensiveness and accuracy of recognition, we further design a multimodal augmentation strategy, an example pool strategy, and a self-verification strategy. Then, we devise a semantic and string similarity aggregation-based entity disambiguation technique to eliminate ambiguities among multiple potential owners recognized by large language models and a position-based hybrid ranking technique to exactly select the true owner. To evaluate OwnerHunter, we refine the publicly available English dataset ONER and construct the Chinese dataset WOI-cn with 16,036 real websites. Experimental results show that OwnerHunter achieves F1 scores of 0.9505 on ONER and 0.9621 on WOI-cn, setting new state-of-the-art performance on both datasets.

Website owner identification aims to link websites to their real-world owners, which is crucial for credibility assessment and information provenance in information retrieval and vital for applications in cybersecurity, Internet governance, and digital regulation. Existing approaches for website owner identification primarily rely on querying infrastructure registration records or analyzing webpage content. However, these methods often fail due to incomplete or outdated registration records and sparse webpage content. We observe that inter-website relationships, derived from shared infrastructure data such as primary domains, IP blocks, and geolocations, can provide valuable but underutilized ownership cues. To exploit this insight, we propose MetaRAG, a meta-path-guided dynamic graph retrieval-augmented generation framework that performs reasoning using large language models over ownership-relevant paths in a website-centric knowledge graph. MetaRAG consists of three components: (1) a knowledge graph construction module that integrates infrastructure data and crawled webpage content into a unified representation; (2) a meta-path-guided dynamic reasoning module that constrains retrieval to ownership-relevant meta-paths and adaptively decides whether to retrieve more information or perform inference based on evidence completeness; and (3) a multi-path evidence refinement module that aggregates and scores retrieved paths to suppress noise and distill high-confidence ownership signals. We evaluate MetaRAG on two constructed real-world datasets, achieving up to 6.82% improvement over strong baselines. The results demonstrate the effectiveness of our approach in combining structured web knowledge with large language model-based reasoning for more accurate website owner identification.

Hypervisor is the core technology of virtualization for emulating independent hardware resources for each virtual machine. Virtual devices serve as the main interface of the hypervisor, making the security of virtual devices crucial, as any vulnerabilities can impact the entire virtualization environment and pose a threat to the host machine's security. Direct Memory Access (DMA) is the interface of virtual devices, enabling communication with the host machine. Recently, many efforts have focused on fuzzing against DMA to discover the hypervisor's vulnerabilities. However, the lack of sensitivity to the DMA state causes these efforts to be hindered in efficiency during fuzzing. Specifically, there are two main issues: the uncertain interaction moment and the unclear interaction depth. In this paper, we introduce InSVDF, a DMA interface stateaware fuzzing engine. InSVDF first models the intra-interface state of the DMA interface and incorporates an asynchronyaware state snapshot mechanism along with a depth-aware seed preservation mechanism. To validate our approach, we compare InSVDF with a state-of-the-art fuzzer. The results demonstrate that InSVDF significantly enhances vulnerability discovery speed, with improvements of up to 24.2 x in the best case. Furthermore, InSVDF has identified 2 new vulnerabilities, one of which has been assigned a CVE ID.

Website owner identification aims to recognize the organization or individual who owns a given website that is served on the web. It is a crucial step for cyberspace surveying and mapping, playing a significant role in cyberspace administration and governance. Existing widely employed solutions for website owner identification mainly fall into two paradigms: (1) querying the public information databases such as WHOIS, which store the Internet resource’s registered users or assignees; and (2) directly extracting the organization or individual name of the website owner from the webpage using the technique of named entity recognition. However, the former is less reliable due to the incomplete, encrypted, and outdated records in the public information databases. Meanwhile, the latter requires that the webpages explicitly and precisely present their owner names without ambiguity, which is often hard to guarantee in practice. To address these limitations, we propose to formulate website owner identification as a problem of webpage representation learning, thereby introducing a novel representation learning framework empowered by large language model-based text Rewriting and Multi-level contrastive learning, named ReMon. First, we devise a prompt to rewrite the webpages using large language models, which effectively filters out noise from the original webpages. Second, we model website–website, website–owner, and owner–owner interactions through multi-level contrastive learning, fully utilizing the self-supervision signals on long-tail items to learn the multi-level constraints. Third, we design a retrieval-based prediction framework and a clustering-based framework to apply websites’ and owners’ representations for different scenarios of the website owner identification task. To evaluate ReMon under our formulation, we construct two datasets based on real-world data. Compared to existing approaches, our ReMon can address the challenging scenarios when valid information cannot be found in public information databases and the owner’s name does not appear on the webpage. Meanwhile, the experimental results show that ReMon outperforms all representation learning-based baselines and significantly enhances training efficiency. The code is available at https://github.com/tuchen9/ReMon .