We introduce HackSynth, a novel Large Language Model (LLM)-based agent capable of autonomous penetration testing. HackSynth's dual-module architecture includes a Planner and a Summarizer, which enable it to generate commands and process feedback iteratively. To benchmark HackSynth, we propose two new Capture The Flag (CTF)-based benchmark sets utilizing the popular platforms PicoCTF and OverTheWire. These benchmarks include two hundred challenges across diverse domains and difficulties, providing a standardized framework for evaluating LLM-based penetration testing agents. Based on these benchmarks, extensive experiments are presented, analyzing the core parameters of HackSynth, including creativity (temperature and top-p) and token utilization. Multiple open source and proprietary LLMs were used to measure the agent's capabilities. The experiments show that the agent performed best with the GPT-4o model, better than what the GPT-4o's system card suggests. We also discuss the safety and predictability of HackSynth's actions. Our findings indicate the potential of LLM-based agents in advancing autonomous penetration testing and the importance of robust safeguards. HackSynth and the benchmarks are publicly available to foster research on autonomous cybersecurity solutions.

Radio spectrum is a limited national resource whose efficient utilization is of strategic importance. With the rapid advancement of wireless technologies, maintaining spectrum cleanliness and enabling fast and reliable anomaly detection have become critical challenges. Artificial intelligence (AI)-based approaches have recently shown great promise in addressing these issues; however, their effectiveness strongly depends on the availability of high-quality, representative, and annotated datasets. Generating such datasets is a complex task, further complicated by environmental conditions such as weather. Hungary’s nationwide spectrum monitoring network enables continuous observation of frequency bands, thereby providing the opportunity to construct large-scale and sustainable datasets. This study introduces a measurement methodology designed for the FM sound broadcasting in the VHF band (87.5–108 MHz), presents the resulting dataset, and details the annotation process. The published, openly accessible dataset is expected to serve not only as a valuable reference point but also as a benchmark for the international research community, facilitating the development, validation, and objective comparison of AI-driven spectrum monitoring solutions.

Large Language Models (LLMs), such as GPT-4 and Llama 2, show remarkable proficiency in a wide range of natural language processing (NLP) tasks. Despite their effectiveness, the high costs associated with their use pose a challenge. We present LlamBERT, a hybrid approach that leverages LLMs to annotate a small subset of large, unlabeled databases and uses the results for fine-tuning transformer encoders like BERT and RoBERTa. This strategy is evaluated on two diverse datasets: the IMDb review dataset and the UMLS Meta-Thesaurus. Our results indicate that the LlamBERT approach slightly compromises on accuracy while offering much greater cost-effectiveness.

Long Short-Term Memory (LSTM) neural networks have proven their excellence in basin-level discharge prediction, provided there is an adequate amount of high-quality time series data available for training, including meteorological forcings and streamflow gauge measurements. Such data-driven black-box models can successfully learn the complex behavior of delayed hydraulic responses; however, they cannot yet be easily applied in water management practice, and model transfer attempts to ungauged catchments have not been entirely successful.In our previous work, we explored an approach to characterizing near-surface flow regimes, starting from a full catchment model and then applying a single LSTM network layer within a semi-distributed subbasin setup reflecting downstream topography. Application to the Tarna River catchment area in Hungary (2,116 km2) showed that transfer learning from the full catchment model (achieving an NSE of 0.91 on the training set and 0.66 on an independent test set) to a downstream chain of gauged Hydrological Response Units (HRUs) is a powerful tool for investigating a semi-distributed HRU network. The entire setup, however, involves a much higher level of complexity, and the available detailed meteorological data and gauge measurements in only two-thirds of the subbasins did not provide sufficient information for the single LSTM model to fully predict the HRU network processes.Because these models apply “virtual water amounts” stored in the hidden cells of the LSTM network for discharge estimation, their internal variables lack direct physical interpretability. In the present research, we investigate how data fusion during calibration with Gravity Recovery and Climate Experiment (GRACE) data, downscaled using soil water content and evapotranspiration products from the ECMWF Reanalysis (ERA5) database, can improve predictive performance, and help to verify our working hypothesis regarding the theoretical connection between Near Surface Water Content (NSWC) and LSTM cell states. These results can also validate interpretations derived from our model concerning baseflow contributions and recharge-discharge classification of subbasins, while promising realistic transferability of the pre-trained lumped catchment model to all subbasins and broader general applicability of the proposed method. We hypothesize that the daily change dynamics of Terrestrial Water Storage (TWS) and NSWC – the latter playing a decisive role in gravitational flows within the Critical Zone – are strongly correlated.Accordingly, we propose using downscaled TSW estimates to (1) introduce a new term into the loss function based on our working hypothesis relating median LSTM cell state values to the normalized dynamics of NSWC, and (2) add a new input dimension approximating total runoff as precipitation minus evapotranspiration and infiltration.Furthermore, the current model extension, still based on 0.1 ° gridded input data, prepares the ground for future developments that incorporate high-spatial-resolution satellite remote sensing data, such as Sentinel-2 NDWI, to support local-scale hydrological applications efficiently. Integrating satellite data products with different temporal and spatial resolutions is not a straightforward calibration step for rainfall-runoff models, as pixel-wise normalization of measurements requires complex physically based geostatistical methods compatible with model logic to avoid performance deterioration.

We present a purely deep neural network-based approach for estimating long memory parameters of time series models that incorporate the phenomenon of long-range dependence. Parameters, such as the Hurst exponent, are critical in characterizing the long-range dependence, roughness, and self-similarity of stochastic processes. The accurate and fast estimation of these parameters holds significant importance across various scientific disciplines, including finance, physics, and engineering. We harnessed efficient process generators to provide high-quality synthetic training data, enabling the training of scale-invariant 1D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models. Our neural models outperform conventional statistical methods, even those augmented with neural networks. The precision, speed, consistency, and robustness of our estimators are demonstrated through experiments involving fractional Brownian motion (fBm), the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process, and the fractional Ornstein-Uhlenbeck (fOU) process. We believe that our work will inspire further research in the field of stochastic process modeling and parameter estimation using deep learning techniques.