Jing Chen

LLM-based multi-agent systems (MASs) have reshaped the digital landscape with their emergent coordination and problem-solving capabilities. However, current security evaluations of MASs are still confined to limited attack scenarios, leaving their security issues unclear and likely underestimated. To fill this gap, we propose TOMA, a topology-aware multi-hop attack scheme targeting MASs. By optimizing the propagation of contamination within the MAS topology and controlling the multi-hop diffusion of adversarial payloads originating from the environment, TOMA unveils new and effective attack vectors without requiring privileged access or direct agent manipulation. Experiments demonstrate attack success rates ranging from 40% to 78% across three state-of-the-art MAS architectures: \textsc{Magentic-One}, \textsc{LangManus}, and \textsc{OWL}, and five representative topologies, revealing intrinsic MAS vulnerabilities that may be overlooked by existing research. Inspired by these findings, we propose a conceptual defense framework based on topology trust, and prototype experiments show its effectiveness in blocking 94.8% of adaptive and composite attacks.

Large Reasoning Models (LRMs) employ reasoning to address complex tasks. Such explicit reasoning requires extended context lengths, resulting in substantially higher resource consumption. Prior work has shown that adversarially crafted inputs can trigger redundant reasoning processes, exposing LRMs to resource-exhaustion vulnerabilities. However, the reasoning process itself, especially its reflective component, has received limited attention, even though it can lead to over-reflection and consume excessive computing power. In this paper, we introduce Recursive Entropy to quantify the risk of resource consumption in reflection, thereby revealing the safety issues inherent in inference itself. Based on Recursive Entropy, we introduce RECUR, a resource exhaustion attack via Recursive Entropy guided Counterfactual Utilization and Reflection. It constructs counterfactual questions to verify the inherent flaws and risks of LRMs. Extensive experiments demonstrate that, under benign inference, recursive entropy exhibits a pronounced decreasing trend. RECUR disrupts this trend, increasing the output length by up to 11x and decreasing throughput by 90%. Our work provides a new perspective on robust reasoning.