This paper addresses a critical challenge in cybersecurity: the gap between vulnerability information represented by Common Vulnerabilities and Exposures (CVEs) and the resulting cyberattack actions. CVEs provide insights into vulnerabilities, but often lack details on potential threat actions (tactics, techniques, and procedures, or TTPs) within the ATT&CK framework. This gap hinders accurate CVE categorization and proactive countermeasure initiation. The paper introduces the TTPpredictor tool, which uses innovative techniques to analyze CVE descriptions and infer plausible TTP attacks resulting from CVE exploitation. TTPpredictor overcomes challenges posed by limited labeled data and semantic disparities between CVE and TTP descriptions. It initially extracts threat actions from unstructured cyber threat reports using Semantic Role Labeling (SRL) techniques. These actions, along with their contextual attributes, are correlated with MITRE's attack functionality classes. This automated correlation facilitates the creation of labeled data, essential for categorizing novel threat actions into threat functionality classes and TTPs. The paper presents an empirical assessment, demonstrating TTPpredictor's effectiveness with accuracy rates of approximately 98% and F1-scores ranging from 95% to 98% in precise CVE classification to ATT&CK techniques. TTPpredictor outperforms state-of-the-art language model tools like ChatGPT. Overall, this paper offers a robust solution for linking CVEs to potential attack techniques, enhancing cybersecurity practitioners' ability to proactively identify and mitigate threats.

Explicit antisymmetrization of a neural network is a potential candidate for a universal function approximator for generic antisymmetric functions, which are ubiquitous in quantum physics. However, this procedure is a priori factorially costly to implement, making it impractical for large numbers of particles. The strategy also suffers from a sign problem. Namely, due to near-exact cancellation of positive and negative contributions, the magnitude of the antisymmetrized function may be significantly smaller than before anti-symmetrization. We show that the anti-symmetric projection of a two-layer neural network can be evaluated efficiently, opening the door to using a generic antisymmetric layer as a building block in anti-symmetric neural network Ansatzes. This approximation is effective when the sign problem is controlled, and we show that this property depends crucially the choice of activation function under standard Xavier/He initialization methods. As a consequence, using a smooth activation function requires re-scaling of the neural network weights compared to standard initializations.

Numerous companies have started offering services based on large language models (LLM), such as ChatGPT, which inevitably raises privacy concerns as users' prompts are exposed to the model provider. Previous research on secure reasoning using multi-party computation (MPC) has proven to be impractical for LLM applications due to its time-consuming and communication-intensive nature. While lightweight anonymization techniques can protect private information in prompts through substitution or masking, they fail to recover sensitive data replaced in the LLM-generated results. In this paper, we expand the application scenarios of anonymization techniques by training a small local model to de-anonymize the LLM's returned results with minimal computational overhead. We introduce the HaS framework, where "H(ide)" and "S(eek)" represent its two core processes: hiding private entities for anonymization and seeking private entities for de-anonymization, respectively. To quantitatively assess HaS's privacy protection performance, we propose both black-box and white-box adversarial models. Furthermore, we conduct experiments to evaluate HaS's usability in translation and classification tasks. The experimental findings demonstrate that the HaS framework achieves an optimal balance between privacy protection and utility.

Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation praovided by these algorithms enables transparency and explainability, which is necessary for decision making in critical real-world problems. Yet, causal ML has had limited impact in practice compared to associational ML. This paper investigates the challenges of causal ML with application to COVID-19 UK pandemic data. We collate data from various public sources and investigate what the various structure learning algorithms learn from these data. We explore the impact of different data formats on algorithms spanning different classes of learning, and assess the results produced by each algorithm, and groups of algorithms, in terms of graphical structure, model dimensionality, sensitivity analysis, confounding variables, predictive and interventional inference. We use these results to highlight open problems in causal structure learning and directions for future research. To facilitate future work, we make all graphs, models, data sets, and source code publicly available online.

Adversarial examples, inputs designed to induce worst-case behavior in machine learning models, have been extensively studied over the past decade. Yet, our understanding of this phenomenon stems from a rather fragmented pool of knowledge; at present, there are a handful of attacks, each with disparate assumptions in threat models and incomparable definitions of optimality. In this paper, we propose a systematic approach to characterize worst-case (i.e., optimal) adversaries. We first introduce an extensible decomposition of attacks in adversarial machine learning by atomizing attack components into surfaces and travelers. With our decomposition, we enumerate over components to create 576 attacks (568 of which were previously unexplored). Next, we propose the Pareto Ensemble Attack (PEA): a theoretical attack that upper-bounds attack performance. With our new attacks, we measure performance relative to the PEA on: both robust and non-robust models, seven datasets, and three extended lp-based threat models incorporating compute costs, formalizing the Space of Adversarial Strategies. From our evaluation we find that attack performance to be highly contextual: the domain, model robustness, and threat model can have a profound influence on attack efficacy. Our investigation suggests that future studies measuring the security of machine learning should: (1) be contextualized to the domain & threat models, and (2) go beyond the handful of known attacks used today.

The attorneys general from all 50 states have banned together and sent an open letter to Congress, asking for increased protective measures against AI-enhanced child sexual abuse images, as originally reported by AP. The letter calls on lawmakers to "establish an expert commission to study the means and methods of AI that can be used to exploit children specifically." The letter sent to Republican and Democratic leaders of the House and Senate also urges politicians to expand existing restrictions on child sexual abuse materials to specifically cover AI-generated images and videos. This technology is extremely new and, as such, there's nothing on the books yet that explicitly places AI-generated images in the same category as other types of child sexual abuse materials. "We are engaged in a race against time to protect the children of our country from the dangers of AI," the prosecutors wrote in the letter.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The top prosecutors in all 50 states are urging Congress to study how artificial intelligence can be used to exploit children through pornography, and come up with legislation to further guard against it. In a letter sent Tuesday to Republican and Democratic leaders of the House and Senate, the attorneys general from across the country call on federal lawmakers to "establish an expert commission to study the means and methods of AI that can be used to exploit children specifically" and expand existing restrictions on child sexual abuse materials specifically to cover AI-generated images. "We are engaged in a race against time to protect the children of our country from the dangers of AI," the prosecutors wrote in the letter, shared ahead of time with The Associated Press.

Michael Gove's plans to lift the onshore wind ban are finally here, after what campaigners have termed a "Tory obsession" with blocking the form of renewable energy. Since 2015, it has been almost impossible to erect wind turbines in England because the planning system was changed so even one objection from a local resident could derail an entire project – an incredibly stringent interpretation of community consent. Just 20 turbines have been approved since 2014 as a result. It has taken years of campaigning – and squabbling – within the government and the backbenches to get to this point, which could make it slightly easier to build renewables. Though what the housing secretary has proposed is better than the previous situation, it still leaves onshore wind at a disadvantage compared with any other infrastructure project and campaigners say developers will still not invest in building windfarms if their proposals could be squashed by local councillors.

As the last days of summer set, one is wistful of the time spent with loved ones sitting on the beach, traveling on the road, or just sharing a refreshing ice cream cone. However, for many Americans such emotional connections are rare, leading to high suicide rates and physical illness. In a recent study by the Surgeon General, more than half of the adults in the USA experience loneliness, with only 39% reporting feeling "very connected to others." As Dr. Vivek H. Murthy states: "Loneliness is far more than just a bad feeling--it harms both individual and societal health. It is associated with a greater risk of cardiovascular disease, dementia, stroke, depression, anxiety, and premature death. The mortality impact of being socially disconnected is similar to that caused by smoking up to 15 cigarettes a day and even greater than that associated with obesity and physical inactivity."

X, the platform formerly known as Twitter, announced it will allow political advertising back on the platform – reversing a global ban on political ads since 2019. The move is the latest to stoke concerns about the ability of big tech to police online misinformation ahead of the 2024 elections – and X is not the only platform being scrutinised. Social media firms' handlings of misinformation and divisive speech reached a breaking point in the 2020 US presidential elections when Donald Trump used online platforms to rile up his base, culminating in the storming of the Capitol building on 6 January 2021. But in the time since, companies have not strengthened their policies to prevent such crises, instead slowly stripping protections away. This erosion of safeguards, coupled with the rise of artificial intelligence, could create a perfect storm for 2024, experts warn.