Despite Video Large Language Models (Video-LLMs) having rapidly advanced in recent years, perceptual hallucinations pose a substantial safety risk, which severely restricts their real-world applicability. While several methods for hallucination mitigation have been proposed, they often compromise the model’s capacity for video understanding and reasoning. In this work, we propose SmartSight, a pioneering step to address this issue in a training-free manner by leveraging the model’s own introspective capabilities. Specifically, SmartSight generates multiple candidate responses to uncover low-hallucinated outputs that are often obscured by standard greedy decoding. It assesses the hallucination of each response using the Temporal Attention Collapse score, which measures whether the model over-focuses on trivial temporal regions of the input video when generating the response. To improve efficiency, SmartSight identifies the Visual Attention Vanishing point, enabling more accurate hallucination estimation and early termination of hallucinated responses, leading to a substantial reduction in decoding cost. Experiments show that SmartSight substantially lowers hallucinations for QwenVL-2.5-7B by 10.59% on VRIPT-HAL, while simultaneously enhancing video understanding and reasoning, boosting performance on VideoMMMU by 8.86%. These results highlight SmartSight’s effectiveness in improving the reliability of open-source Video-LLMs.

Large Reasoning Models (LRMs) have demonstrated impressive performance in reasoningintensive tasks, but they remain vulnerable to harmful content generation, particularly in the mid-to-late steps of their reasoning processes.Current defense methods, however, depend on costly fine-tuning and additional expert knowledge, which limits their scalability.In this work, we propose ReasoningGuard, an inference-time safeguard for LRMs.It injects timely safety aha moments during the reasoning process to guide the model towards harmless yet helpful reasoning.Our approach leverages the internal attention mechanisms of the LRM to accurately identify key points in the reasoning path, triggering safety-oriented reflections.To safeguard both the subsequent reasoning steps and the final answers, we implement a scaling sampling strategy during decoding to select the optimal reasoning path.With minimal additional inference cost, Rea-soningGuard effectively mitigates four types of jailbreak attacks, including recent ones targeting the reasoning process of LRMs.Our approach outperforms nine existing safeguards, providing state-of-the-art defenses while avoiding common exaggerated safety issues.

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.

Serverless cloud functions transfer server management responsibilities to service providers, offering scalability and cost-efficiency. This convenience not only facilitates normal activities but also raises abuse concerns. So far, public understanding of real-world cloud functions remains limited. To fill this gap, we conducted an in-depth measurement study to uncover their practical usage and abuse. Through empirical analysis of nine leading providers (e.g., AWS, Tencent), we identified 531,089 function domains from a passive DNS dataset spanning April 2022 to March 2024. We first investigated the usage status of serverless cloud functions, showing the different practices between providers. Additionally, based on active requests to these functions, we pointed out privacy risks of unauthorized access and identified four abuse types, including covert C2 communication, hosting malicious websites, promoting illicit services, and abusing egress nodes as IP proxies. Alarmingly, 4.89% of cloud functions are being abused, with over 614k invocations recorded. Only four abused functions were flagged by existing threat intelligence systems, indicating critical gaps in security monitoring for serverless environments. Our work offers insights into the serverless cloud ecosystem and provides recommendations for better management. With responsible disclosure, we hope to raise awareness and improve protective measures against abuses among cloud function providers.

With the rise of generative large language models (LLMs) like LLaMA and ChatGPT, these models have significantly transformed daily life and work by providing advanced insights. However, as jailbreak attacks continue to circumvent built-in safety mechanisms, exploiting carefully crafted scenarios or tokens, the safety risks of LLMs have come into focus. While numerous defense strategies-such as prompt detection, modification, and model fine-tuning-have been proposed to counter these attacks, a critical question arises: do these defenses compromise the utility and usability of LLMs for legitimate users? Existing research predominantly focuses on the effectiveness of defense strategies without thoroughly examining their impact on performance, leaving a gap in understanding the trade-offs between LLM safety and performance.

Cryptocurrencies, while revolutionary, have become a magnet for malicious actors. With numerous reports underscoring cyberattacks and scams in this domain, our paper takes the lead in characterizing visual scams associated with cryptocurrency wallets---a fundamental component of Web3. Specifically, scammers capitalize on the omission of vital wallet interface details, such as token symbols, wallet addresses, and smart contract function names, to mislead users, potentially resulting in unintended financial losses. Analyzing Ethereum blockchain transactions from July 2022 to June 2023, we uncovered a total of 24,901,115 visual scam incidents, which include 3,585,493 counterfeit token attacks, 21,281,749 zero-transfer attacks, and 33,873 function name attacks, orchestrated by 6,768 distinct attackers. Shockingly, over 28,414 victims fell prey to these scams, with losses surpassing 27 million USD. This alarming data underscores the pressing need for robust protective measures. By profiling the typical victims and attackers, we are able to propose mitigation strategies informed by our findings.

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.

Cryptocurrency mining is a crucial operation in blockchains, and miners often join mining pools to increase their chances of earning rewards. However, the energy-intensive nature of PoW cryptocurrency mining has led to its ban in New York State of the United States, China, and India. As a result, mining pools, serving as a central hub for mining activities, have become prime targets for regulatory enforcement. Furthermore, cryptojacking malware refers to self-owned stealthy mining pools to evade detection techniques and conceal profit wallet addresses. However, no systematic research has been conducted to analyze it, largely due to a lack of full understanding of the protocol implementation, usage, and port distribution of the stealth mining pool.

With the growth of mobile computing techniques, mobile gambling scams have seen a rampant increase in the recent past. In mobile gambling scams, miscreants deliver scamming messages via mobile instant messaging, host scam gambling platforms on mobile apps, and adopt mobile payment channels. To date, there is little quantitative knowledge about how this trending cybercrime operates, despite causing daily fraud losses estimated at more than ${\$}$522,262 USD. This paper presents the first empirical study based on ground-truth data of mobile gambling scams, associated with 1,461 scam incident reports and 1,487 gambling scam apps, spanning from January 1, 2020 to December 31, 2020. The qualitative and quantitative analysis of this ground-truth data allows us to characterize the operational pipeline and full fraud kill chain of mobile gambling scams. In particular, we study the social engineering tricks used by scammers and reveal their effectiveness. Our work provides a systematic analysis of 1,068 confirmed Android and 419 iOS scam apps, including their development frameworks, declared permissions, compatibility, and backend network infrastructure. Perhaps surprisingly, our study unveils that public online app generators have been abused to develop gambling scam apps. Our analysis reveals several payment channels (ab)used by gambling scam app and uncovers a new type of money mule-based payment channel with the average daily gambling deposit of ${\$}$400,000 USD. Our findings enable a better understanding of the mobile gambling scam ecosystem, and suggest potential avenues to disrupt these scam activities.

As Android platform evolves in a fast pace, API-related compatibility issues become a significant challenge for developers. To handle an incompatible API invocation, developers mainly have two choices: merely performing sufficient checks to avoid invoking incompatible APIs on platforms that do not support them, or gracefully providing replacement implementations on those incompatible platforms. As providing more consistent app behaviors, the latter one is more recommended and more challenging to adopt. However, it is still unknown how these issues are handled in the real world, do developers meet difficulties and what can we do to help them.

As a new mechanism to monetize web content, cryptocurrency mining is becoming increasingly popular. The idea is simple: a webpage delivers extra workload (JavaScript) that consumes computational resources on the client machine to solve cryptographic puzzles, typically without notifying users or having explicit user consent. This new mechanism, often heavily abused and thus considered a threat termed "cryptojacking", is estimated to affect over 10 million web users every month; however, only a few anecdotal reports exist so far and little is known about its severeness, infrastructure, and technical characteristics behind the scene. This is likely due to the lack of effective approaches to detect cryptojacking at a large-scale (e.g., VirusTotal). In this paper, we take a first step towards an in-depth study over cryptojacking. By leveraging a set of inherent characteristics of cryptojacking scripts, we build CMTracker, a behavior-based detector with two runtime profilers for automatically tracking Cryptocurrency Mining scripts and their related domains. Surprisingly, our approach successfully discovered 2,770 unique cryptojacking samples from 853,936 popular web pages, including 868 among top 100K in Alexa list. Leveraging these samples, we gain a more comprehensive picture of the cryptojacking attacks, including their impact, distribution mechanisms, obfuscation, and attempts to evade detection. For instance, a diverse set of organizations benefit from cryptojacking based on the unique wallet ids. In addition, to stay under the radar, they frequently update their attack domains (fastflux) on the order of days. Many attackers also apply evasion techniques, including limiting the CPU usage, obfuscating the code, etc.

Android integrates an increasing number of features into system services to manage sensitive resources, such as location, medical and social network information. To prevent untrusted apps from abusing the services, Android implements a comprehensive set of access controls to ensure proper usage of sensitive resources. Unlike explicit permission-based access controls that are discussed extensively in the past, our paper focuses on the widespread yet undocumented input validation problem. As we show in the paper, there are in fact more input validations acting as security checks than permission checks, rendering them a critical foundation for Android framework. Unfortunately, these validations are unstructured, ill-defined, and fragmented, making it challenging to analyze. To this end, we design and implement a tool, called Invetter, that combines machine learning and static analysis to locate sensitive input validations that are problematic in system services. By applying Invetter to 4 different AOSP codebases and 4 vendor-customized images, we locate 103 candidate insecure validations. Among the true positives, we are able to confirm that at least 20 of them are truly exploitable vulnerabilities by constructing various attacks such as privilege escalation and private information leakage.