Large Reasoning Models (LRMs) have demonstrated promising performance in complex tasks. However, the resource-consuming reasoning processes may be exploited by attackers to maliciously occupy the resources of the servers, leading to a crash, like the DDoS attack in cyber. To this end, we propose a novel attack method on LRMs termed ExtendAttack to maliciously occupy the resources of servers by stealthily extending the reasoning processes of LRMs. Concretely, we systematically obfuscate characters within a benign prompt, transforming them into a complex, poly-base ASCII representation. This compels the model to perform a series of computationally intensive decoding sub-tasks that are deeply embedded within the semantic structure of the query itself. Extensive experiments demonstrate the effectiveness of our proposed ExtendAttack. Remarkably, it significantly increases response length and latency, with the former increasing by over 2.7 times for the o3 model on the HumanEval benchmark. Besides, it preserves the original meaning of the query and achieves comparable answer accuracy, showing the stealthiness.

Prototype pollution vulnerability often has further consequences—such as Cross-site Scripting (XSS) and cookie manipulation—that are achieved via so-called gadgets, i.e., code snippets that change the control- or data-flow of a victim program for malicious purposes. Prior works face challenges in finding prototype pollution gadgets for such consequences because the control- or data-flow change sometimes needs the injection of complex property values to replace existing undefined ones through prototype pollution, which may not be seen before or cannot be solved by existing constraint solvers. In this paper, we design a dynamic analysis framework, called Gala, to automatically detect client-side prototype pollution gadgets among real-world websites, and implement an open-source version of Gala. Our key insight is to borrow existing defined values on non-vulnerable websites to victim ones where such values are undefined, thus guiding the property injection to flow to the sinks in gadgets. Our evaluation of Gala against one-million websites reveals 133 zero-day gadgets that are not found by prior works. For example, one gadget was from Meta's software and another from the Vue framework. Both have acknowledged and fixed it, with Meta rewarding us a bug bounty and Vue assigning CVE-2024-6783. Our evaluation also shows that 23 websites with prototype pollution vulnerabilities—which do not have further consequences as reported by prior works—have consequences due to gadgets found by Gala. In addition to the Meta and Vue gadgets, we also responsibly disclosed all the zero-day gadgets and those newly-discovered prototype pollution consequences to their developers.