Jinku Li

Zhen Cheng, Xinrui Hou, Runhuai Li et al.

We performed the first systematic study of a new attack on Ethereum that steals cryptocurrencies. The attack is due to the unprotected JSON-RPC endpoints existed in Ethereum nodes that could be exploited by attackers to transfer the Ether and ERC20 tokens to attackers-controlled accounts. This study aims to shed light on the attack, including malicious behaviors and profits of attackers. Specifically, we first designed and implemented a honeypot that could capture real attacks in the wild. We then deployed the honeypot and reported results of the collected data in a period of six months. In total, our system captured more than 308 million requests from 1,072 distinct IP addresses. We further grouped attackers into 36 groups with 59 distinct Ethereum accounts. Among them, attackers of 34 groups were stealing the Ether, while other 2 groups were targeting ERC20 tokens. The further behavior analysis showed that attackers were following a three-steps pattern to steal the Ether. Moreover, we observed an interesting type of transaction called zero gas transaction, which has been leveraged by attackers to steal ERC20 tokens. At last, we estimated the overall profits of attackers. To engage the whole community, the dataset of captured attacks is released on https://github.com/zjuicsr/eth-honey.

Rui Jiao, Yue Zhang, Jinku Li

We present a novel framework addressing a critical vulnerability in Large Language Models (LLMs): the prevalence of factual inaccuracies within intermediate reasoning steps despite correct final answers. This phenomenon poses substantial risks in high-stakes domains including healthcare, legal analysis, and scientific research, where erroneous yet confidently presented reasoning can mislead users into dangerous decisions. Our framework integrates three core components: (1) a specialized fact-checking classifier trained on counterfactually augmented data to detect subtle factual inconsistencies within reasoning chains; (2) an enhanced Group Relative Policy Optimization (GRPO) reinforcement learning approach that balances factuality, coherence, and structural correctness through multi-dimensional rewards; and (3) a mechanistic interpretability method examining how factuality improvements manifest in model activations during reasoning processes. Extensive evaluation across multi state-of-the-art models reveals concerning patterns: even leading models like Claude-3.7 and GPT-o1 demonstrate reasoning factual accuracy of only 81.93% and 82.57% respectively. Our approach significantly enhances factual robustness (up to 49.90% improvement) while maintaining or improving performance on challenging benchmarks including Math-500, AIME-2024, and GPQA. Furthermore, our neural activation-level analysis provides actionable insights into how factual enhancements reshape reasoning trajectories within model architectures, establishing foundations for future training methodologies that explicitly target factual robustness through activation-guided optimization.

Zhuo Chen, Lei Wu, Jing Cheng et al.

Mobile apps are extensively involved in cyber-crimes. Some apps are malware which compromise users' devices, while some others may lead to privacy leakage. Apart from them, there also exist apps which directly make profit from victims through deceiving, threatening or other criminal actions. We name these apps as CULPRITWARE. They have become emerging threats in recent years. However, the characteristics and the ecosystem of CULPRITWARE remain mysterious. This paper takes the first step towards systematically studying CULPRITWARE and its ecosystem. Specifically, we first characterize CULPRITWARE by categorizing and comparing them with benign apps and malware. The result shows that CULPRITWARE have unique features, e.g., the usage of app generators (25.27%) deviates from that of benign apps (5.08%) and malware (0.43%). Such a discrepancy can be used to distinguish CULPRITWARE from benign apps and malware. Then we understand the structure of the ecosystem by revealing the four participating entities (i.e., developer, agent, operator and reaper) and the workflow. After that, we further reveal the characteristics of the ecosystem by studying the participating entities. Our investigation shows that the majority of CULPRITWARE (at least 52.08%) are propagated through social media rather than the official app markets, and most CULPRITWARE (96%) indirectly rely on the covert fourth-party payment services to transfer the profits. Our findings shed light on the ecosystem, and can facilitate the community and law enforcement authorities to mitigate the threats. We will release the source code of our tools to engage the community.