The rapid growth of Internet of Things (IoT) devices has enhanced convenience but introduced significant security risks. Due to limited visibility into device internals, black-box fuzzing has become the primary method for IoT vulnerability detection. However, it is often difficult to recognize the triggered states, which limits the ability to explore different regions of the state space and, as a result, hinders the discovery of vulnerabilities. Additionally, it lacks feedback, preventing the fuzzer from refining its test inputs based on the results and reducing its effectiveness in discovering vulnerabilities. To address these challenges, we propose SSFuzz, an automated black-box fuzzing framework leveraging large language models (LLMs) to extract state nodes from interaction messages, enabling a state-guided approach. Additionally, we design a cross-device feedback-sharing mechanism based on source code similarities, aiming to make more effective use of the limited feedback available. Evaluated against five leading tools on 18 IoT devices, SSFuzz identified 38 previously undisclosed vulnerabilities, significantly outperforming existing methods. SSFuzz discovered 38 previously unknown vulnerabilities, significantly outperforming Snipuzz (five vulnerabilities) and IoTHunter (one vulnerability).

Facing multidemand tasks and massive heterogeneous resources in an IoT edge computing environment, it is a challenge to obtain reliable and quick response service and allocate application tasks to resource nodes that meet task requirements and user preference. Since IoT edge computing is facing different types of severe attacks, such as message attacks, swing attacks, collusion attack, node attacks, etc., providing a reliable service environment, trust evaluation between edge nodes is necessary. Existing trust computing schemes, however, suffer from a long response period and low malicious detection rate in a dynamic environment. To alleviate these issues, we propose a reliable and efficient task offloading strategy based on the multifeedback trust mechanism (TOSMFTM). First, a reliable and efficient architecture of TOSMFTM is established, which can effectively improve the ability of trust computing and task offloading. Second, according to the broker’s dynamic monitoring of data, a multifeedback trust aggregation model based on time attenuation and interaction frequency is proposed to provide a trusted running environment. Third, a trust weight$k$-means (TWK-means) clustering algorithm is designed based on resource attributes to enhance the reliability of service, and quickly and accurately cluster out resource nodes required by the task. Finally, we construct a task offloading model based on trust clustering to ensure user experience quality and promote system efficiency. Different from existing task processing models, which only focus on task offloading, our method also carries out resource preprocessing, trust evaluation, and resource clustering before task processing. The experiment verifies the effectiveness and feasibility of our TOSMFTM scheme.