The emergence of Large Language Model-enhanced Search Engines (LLMSEs) has revolutionized information retrieval by integrating web-scale search capabilities with AI-powered summarization. While these systems demonstrate improved efficiency over traditional search engines, their security implications against well-established black-hat Search Engine Optimization (SEO) attacks remain unexplored. In this paper, we present the first systematic study of SEO attacks targeting LLMSEs. Specifically, we examine ten representative LLMSE products (e.g., ChatGPT, Gemini) and construct SEO-Bench, a benchmark comprising 1,000 real-world black-hat SEO websites, to evaluate both open- and closed-source LLMSEs. Our measurements show that LLMSEs mitigate over 99.78% of traditional SEO attacks, with the phase of retrieval serving as the primary filter, intercepting the vast majority of malicious queries. We further propose and evaluate seven LLMSEO attack strategies, demonstrating that off-the-shelf LLMSEs are vulnerable to LLMSEO attacks, i.e., rewritten-query stuffing and segmented texts double the manipulation rate compared to the baseline. This work offers the first in-depth security analysis of the LLMSE ecosystem, providing practical insights for building more resilient AI-driven search systems. We have responsibly reported the identified issues to major vendors.

Distinct from traditional malicious packages, this paper uncovers a novel attack vector named “blackhat Search Engine Optimization through REPositories (RepSEO)”. In this approach, attackers carefully craft packages to manipulate search engine results, exploiting the credibility of software repositories to promote illicit websites. Our research presents a systematic analysis of the underground ecosystem of RepSEO, identifying key players such as account providers, advertisers, and publishers. We developed an effective detection tool, applied to a ten-year large-scale dataset of npm, Docker Hub, and NuGet software repositories. This investigation led to the startling discovery of 3,801,682 abusive packages, highlighting the widespread nature of this attack. Our study also delves into the supply chain tactics of these attacks, revealing strategies like the use of self-hosted email services for account registration, redirection methods to obscure landing pages, and rapid deployment techniques by aggressive attackers. Additionally, we explore the profit motives behind these attacks, identifying two primary types of advertisers: survey-based advertisers and malware distribution advertisers. We reported npm, NuGet, and Docker Hub about the RepSEO packages and the related supply chain vulnerabilities of Google, and received their acknowledgments. Software repositories have started removing the abusive packages as of this paper's submission. We also opensource our code and data to facilitate future research.

As a new type of underground ecosystem, the exploitation of Abused IHMs as MalIcious sErvices (AIMIEs) is becoming increasingly prevalent among miscreants to host illegal images and propagate harmful content. However, there has been little effort to understand this new menace, in terms of its magnitude, impact, and techniques, not to mention any serious effort to detect vulnerable image hosting modules on a large scale. To fulfill this gap, this paper presents the first measurement study of AIMIEs. By collecting and analyzing 89 open-sourced AIMIEs, we reveal the landscape of AIMIEs, report the evolution and evasiveness of abused image hosting APIs from reputable companies such as Alibaba, Tencent, and Bytedance, and identify real-world abused images uploaded through those AIMIEs. In addition, we propose a tool, called Viola, to detect vulnerable image hosting modules (IHMs) in the wild. We find 477 vulnerable IHM upload APIs associated with 338 web services, which integrated vulnerable IHMs, and 207 victim FQDNs. The highest-ranked domain with vulnerable web service is baidu.com, followed by bilibili.com and 163.com. We have reported abused and vulnerable IHM upload APIs and received acknowledgments from 69 of them by the time of paper submission.

In this article, the event-triggered multistep model predictive control for the discrete-time nonlinear system over communication networks under the influence of packet dropouts and cyber attacks is studied. First, the interval type-2 Takagi-Sugeno fuzzy model is applied to express the discrete-time nonlinear system and an event-triggered mode, which is capable of determining whether the sampled signal ought to be delivered into the unreliable network, is designed to economize communication resources. Second, two Bernoulli processes are introduced to represent the randomly happening packet dropouts in the unreliable network and the randomly occurring deception attacks on the actuator side from the adversaries. Third, under the assumption that the system states are unmeasurable, a multistep parameter-dependent model predictive controller is synthesized via optimizing one series of feedback laws for a given period of time, which leads to improved control performance than that of the one-step approach. Moreover, the results on the recursive feasibility and closed-loop stability related to the networked system are achieved, which explicitly consider the external disturbance and input constraint. Finally, simulation experiments on the mass-spring-damping system are carried out to illustrate the rationality and effectiveness of the provided control strategy.