This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content that has been modified using techniques designed to evade detection by these tools (n=805). The results demonstrate that the detectors' already low accuracy rates (39.5%) show major reductions in accuracy (17.4%) when faced with manipulated content, with some techniques proving more effective than others in evading detection. The accuracy limitations and the potential for false accusations demonstrate that these tools cannot currently be recommended for determining whether violations of academic integrity have occurred, underscoring the challenges educators face in maintaining inclusive and fair assessment practices. However, they may have a role in supporting student learning and maintaining academic integrity when used in a non-punitive manner. These results underscore the need for a combined approach to addressing the challenges posed by GenAI in academia to promote the responsible and equitable use of these emerging technologies. The study concludes that the current limitations of AI text detectors require a critical approach for any possible implementation in HE and highlight possible alternatives to AI assessment strategies.

The abundance of cyber-physical components in modern day power grid with their diverse hardware and software vulnerabilities has made it difficult to protect them from advanced persistent threats (APTs). An attack graph depicting the propagation of potential cyber-attack sequences from the initial access point to the end objective is vital to identify critical weaknesses of any cyber-physical system. A cyber security personnel can accordingly plan preventive mitigation measures for the identified weaknesses addressing the cyber-attack sequences. However, limitations on available cybersecurity budget restrict the choice of mitigation measures. We address this aspect through our framework, which solves the following problem: given potential cyber-attack sequences for a cyber-physical component in the power grid, find the optimal manner to allocate an available budget to implement necessary preventive mitigation measures. We formulate the problem as a mixed integer linear program (MILP) to identify the optimal budget partition and set of mitigation measures which minimize the vulnerability of cyber-physical components to potential attack sequences. We assume that the allocation of budget affects the efficacy of the mitigation measures. We show how altering the budget allocation for tasks such as asset management, cybersecurity infrastructure improvement, incident response planning and employee training affects the choice of the optimal set of preventive mitigation measures and modifies the associated cybersecurity risk. The proposed framework can be used by cyber policymakers and system owners to allocate optimal budgets for various tasks required to improve the overall security of a cyber-physical system.

A team of researchers with members from MIT, UC Berkely and FAR AI has created a computer program to target vulnerabilities in the KataGo program that allow it to beat the AI-based system. They have published a paper describing their efforts on the arXiv preprint server. In 2016, a computer program created by the DeepMind project succeeded in beating human champion Go players for the first time. The program used a deep-learning neural network to learn how the game works and then how to play at increasingly higher levels by simply playing against itself. More recently, a similar open-source program called KataGo was released to the public--it can also beat the best human players.

Through the media, this conversation may appear to sit in a cloud of worry about speculative future-bots that will wipe out humanity. However, real inklings of how we can easily lose mastery over our AI creations are observed in practical problems related to unintended behaviors from poorly designed machine learning systems. Among these potential "AI accidents" is the case of adversarial techniques. This approach takes, for instance, a trained classifier model that performs well with identifying inputs compared to how a person would classify. Then, a new input comes along that includes subtle yet maliciously crafted data that causes the model to behave very poorly. What is troublesome is that the type of poor behavior is not a reduction in the statistical performance of the model.