Multi-agent architectures leveraging Large Language Models (LLMs) have significantly advanced the precision of Question Answering (QA) systems across diverse domains. However, existing frameworks remain vulnerable to adversarial manipulations, including poisoning, backdoor, and jailbreak at tacks, primarily due to their reliance on centralized orchestration. To mitigate these risks, we propose AgentChain, a framework that substitutes centralized control with a distributed semantic consensus process. By modeling the blockchain as an ideal functionality, AgentChain establishes a secure distributed layer to coordinate role allocation, answer proposal, evaluation and voting through a decentralized council. Specifically, we design Proof-of-Content-Quality (PoCQ) mechanism to ensure that the f inal answers reflect a robust semantic agreement among the majority of honest agents. Furthermore, we propose an incentive mechanism based on stake reassignment that penalizes malicious agents by reducing their rewards, ultimately phasing them out of the network. Comprehensive evaluations across eight datasets demonstrate that AgentChain achieves superior performance and resilience. AgentChain minimizes the impact of poisoning attacks on precision to less than 3% and reduces the success rate of backdoor and jailbreak attacks to less than 4%. These findings highlight the effectiveness and trustworthiness of AgentChain in mitigating security threats while maintaining high QA accuracy.

To reduce attack risks in Internet of Things (IoT), many security vendors conduct software security analysis on IoT devices all the time. However, how to build an ultralow-latency threat alerting strategy using software vulnerability information still faces challenges. First, existing terminal threat detection methods for IoT systems relying on Indicators of Compromise (IoC) threat intelligence can only cover limited software vulnerabilities so the alert validity rate is still very low. Second, most users lack security knowledge and cannot proactively distinguish high-risk vulnerabilities, resulting in untimely reporting. In this article, a novel hierarchical software execution state representation (HSESR) scheme is proposed for ultralow latency threat alerting over IoT systems based on Beyond 5G. In HSESR, function call graphs are recorded and delivered to edge servers for swiftly identifying suspicious threat behaviors based on deep graph representation, while corresponding instruction sequences are delivered to the cloud data center for further matching the vulnerability information via recurrent semantic representation. To improve the effectiveness of HSESR, the graph representation is also actively encapsulated into the corresponding semantic representation, together acting as an implicit threat behavior signature, which is essential to associate with a security patch. Moreover, to accelerate the detection of suspicious behaviors, we also propose a deep reinforcement learning-based graph searching (DRL-GS) strategy to crop the huge function call graph of the entire software to timely report high-risk threat behaviors with minimized resource consumption. By instancing 1-day attacks on a simulated beyond 5G IoT system, the performance of HSESR is trustfully competitive against existing baselines, and the efficiency of threat detection was increased by 21.63%.