Penetration testing is essential for ensuring Web security by identifying and mitigating vulnerabilities in advance, and the rapid progress of large language models (LLMs) shows great potential to revolutionize this process through intelligent, automated agents. In this work, we establish a comprehensive end-to-end penetration testing benchmark using a real-world penetration testing environment to explore the capabilities of LLM-based agents in this domain. Our results reveal that the agents are familiar to procedures of penetration testing tasks, but they still face limitations in generating accurate commands and executing complete processes. Accordingly, we summarize the current challenges, including the difficulty of maintaining the entire message history and the tendency for the agent to become stuck. Based on the above insights, we propose a Penetration testing State Machine (PSM) that utilizes the Finite State Machine (FSM) methodology to address these limitations. Then, we introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs, which utilizes the inherent inference ability of LLM and the constraint framework of state machines. Our evaluation results show that AutoPT outperforms the the ReAct-based baseline and improves the task completion rate from 22% to 41% on the benchmark target. Compared with the baseline and manual work, AutoPT also reduces time and economic costs further. In general, our AutoPT has facilitated the development of automated penetration testing and bring new findings and insights for both academia and industry.

The global polynomial synchronization (GPS) is investigated for memristive competitive neural networks (MCNNs) with proportional delays and uncertain parameters. First, via drawing support from differential inclusion theory and adopting the time-variant state feedback controllers, their error systems are synthesized into a vector form MCNNs. Then, a new proportional delay differential inequality (PDDI) is established through norm definition and Lipschitz condition for the vector form MCNNs mentioned-above. Several algebraic forms of synchronization criteria for the system proposed are achieved by applying the new PDDI. Each of these criteria is represented by only one inequality, rather than in the form of components, which facilitates validation. Compared to the commonly used method for constructing Lyapunov functionals in studying MCNNs, it is more concise. Ultimately, numerical examples are used to inspect the results acquired, and the GPS of one example is applied in image encryption and decryption. The empirical outcomes demonstrat the efficacy of the employed synchronization control approach, exhibiting robust resilience and heighten security in secure communication applications.