Federated Learning (FL) is increasingly being adopted in military collaborations to develop Large Language Models (LLMs) while preserving data sovereignty. However, prompt injection attacks-malicious manipulations of input prompts-pose new threats that may undermine operational security, disrupt decision-making, and erode trust among allies. This perspective paper highlights four vulnerabilities in federated military LLMs: secret data leakage, free-rider exploitation, system disruption, and misinformation spread. To address these risks, we propose a human-AI collaborative framework with both technical and policy countermeasures. On the technical side, our framework uses red/blue team wargaming and quality assurance to detect and mitigate adversarial behaviors of shared LLM weights. On the policy side, it promotes joint AI-human policy development and verification of security protocols.

Regression testing for software product line (SPL) is challenging and can be expensive because it must ensure that all the products of a product family are correct whenever changes are made. SPL regression testing can be made efficient by selecting only the test cases that are relevant to the changes. In the past, some approaches for SPL test case selection have been proposed. However, they require requirements specification, architecture and/or traceabilities for test cases that are well managed for selecting test cases for a retest. In this paper, we propose an automated method of source code-based regression test selection for SPLs that selects regression tests, based on the commonality and variability of a product family while leaving out the test cases not affected by the changes to the source code. Preliminary evaluation of our method using five product lines shows that our method reduces without missing any fault-revealing test cases the number of test cases for a retest by 22.4%, 13.4% and 20.4%, on average, compared to, respectively, the retest-all method, the random selection method and the complete selection method.

Collocation preferences represent the commonly used expressions, idioms, and word pairings of a language. Because collocation preferences arise from consensus usage, rather than a set of well-defined rules, they must be learned on a case-by-case basis, making them particularly challenging for non-native speakers of a language. To assist non-native speakers with these parts of a language, we developed AwkChecker, the first end-user tool geared toward helping non-native speakers detect and correct collocation errors in their writing. As a user writes, AwkChecker automatically flags collocation errors and suggests replacement expressions that correspond more closely to consensus usage. These suggestions include example usage to help users choose the best candidate. We describe AwkChecker's interface, its novel methods for detecting collocation errors and suggesting alternatives, and an early study of its use by non-native English speakers at our institution. Collectively, these contributions advance the state of the art in writing aids for non-native speakers.