US Customs and Border Protection (CBP) agents have reportedly misused electronic databases to spy on family members, try to get dates and even provide intelligence to suspected drug traffickers, according to freedom of information (FOIA) files seen by Wired. Officers allegedly abused databases that can draw from sources like license plate readers, facial recognition and smartphone searches to violate the privacy of numerous individuals. A CBP officer is alleged to have used a government database to contact a flight attendant, and another was accused of using data from trusted-traveler applications to ask people out. One employee provided border-crossing data to someone involved in a divorce, and another abused internal policies by tracking coworkers cellphones with ad-tech-derived location data. Of 300 incidents tracked by Wired, 138 were referred to CBP management and 78 assigned to criminal investigators, while 43 others weren't investigated.

Records obtained by WIRED detail hundreds of allegations of Customs and Border Protection workers misusing internal tools to look up romantic interests and track colleagues' cell phones. Internal records obtained by WIRED reveal how, for years, United States Customs and Border Protection employees and contractors were accused of abusing sensitive government databases for reasons that had nothing to do with their jobs. The records contain hundreds of allegations of misuse of law enforcement databases, including federal agents querying data to look up romantic interests, monitor family members, expose various personal information and, in some cases, provide intelligence to suspected smugglers or drug-trafficking organizations. Acquired through Freedom of Information Act requests to CBP's Office of Professional Responsibility and the Department of Homeland Security's Office of Inspector General, the records reveal the breadth of alleged database abuse by CBP employees spanning more than a decade. As immigration and border authorities expand their surveillance through facial recognition, license plate readers, mobile-device searches, and commercially purchased location information generated by ordinary apps, the sheer range of these records, which date from 2009 through 2022, highlights how US residents can be--and have been--targeted by federal government employees with access to highly sensitive data and powerful tools.

New Scotland Yard, the headquarters of the Metropolitan police whose pilot with Palantir focuses on detecting misconduct by officers. New Scotland Yard, the headquarters of the Metropolitan police whose pilot with Palantir focuses on detecting misconduct by officers. Mayor's office grants extra 12 months to run pilot while London force procures long-term supplier Wed 24 Jun 2026 18.14 EDTLast modified on Wed 24 Jun 2026 18.50 EDT The Metropolitan police have been granted a 12-month extension to a pilot project with the spy-tech firm Palantir while the force carries out a procurement process. The development comes weeks after the mayor of London, Sadiq Khan, blocked a £50m deal between the Met and the US company to automate intelligence analysis in criminal investigations. Last month the mayor's office said there had been a "clear and serious breach" of procurement rules and said police had seriously considered only one supplier. Palantir's lawyers subsequently wrote to the Mayor's Office for Policing and Crime (Mopac) saying they intended to challenge the decision in court, the Times reported.

The Met said corruption was the most consistent offence detected, with misconduct related to'abuse of the IT system that rosters shifts by police officers for personal or financial gain'. The Met said corruption was the most consistent offence detected, with misconduct related to'abuse of the IT system that rosters shifts by police officers for personal or financial gain'. Sat 25 Apr 2026 11.34 EDTFirst published on Sat 25 Apr 2026 11.31 EDT The Metropolitan police have launched investigations into hundreds of officers after using an AI tool built by the controversial tech company Palantir to root out rogue cops. The software was deployed by the Met over the course of a week, surveilling staff members using data the force has ready access to, unearthing rule-breaking ranging from work-from-home violations to suspected corruption and even criminal allegations such as rape. The Met said as a result of the software, evidence had been found tying a small number of officers to serious cases of misconduct and criminality, resulting in the arrest of three officers for offences including abuse of authority for sexual purposes, fraud, sexual assault, misconduct in public office and misuse of police systems.

Leaders at Mira Murati's startup believe Barret Zoph engaged in an incident of "serious misconduct." The details are now coming to light. Leaders at Mira Murati's Thinking Machines Lab confronted the startup's cofounder and former CTO, Barret Zoph, over an alleged relationship with another employee last summer, WIRED has learned. That relationship was likely the alleged "misconduct" that has been mentioned in prior reporting, including by WIRED . To protect the privacy of the individuals involved, WIRED is not naming the employee in question.

ALI-Agent operates through two principal stages: Emulation and Refinement. During the Emulation stage, ALI-Agent automates the generation of realistic test scenarios.

Modern moderation systems increasingly support multiple languages, but often fail to address localisation and low-resource variants - creating safety gaps in real-world deployments. Small models offer a potential alternative to large LLMs, yet still demand considerable data and compute. We present LionGuard 2, a lightweight, multilingual moderation classifier tailored to the Singapore context, supporting English, Chinese, Malay, and partial Tamil. Built on pre-trained OpenAI embeddings and a multi-head ordinal classifier, LionGuard 2 outperforms several commercial and open-source systems across 17 benchmarks, including both Singapore-specific and public English datasets. The system is actively deployed within the Singapore Government, demonstrating practical efficacy at scale. Our findings show that high-quality local data and robust multilingual embeddings can achieve strong moderation performance, without fine-tuning large models. We release our model weights and part of our training data to support future work on LLM safety.

Academic misconduct detection in biomedical research remains challenging due to algorithmic narrowness in existing methods and fragmented analytical pipelines. We present BMDetect, a multimodal deep learning framework that integrates journal metadata (SJR, institutional data), semantic embeddings (PubMedBERT), and GPT-4o-mined textual attributes (methodological statistics, data anomalies) for holistic manuscript evaluation. Key innovations include: (1) multimodal fusion of domain-specific features to reduce detection bias; (2) quantitative evaluation of feature importance, identifying journal authority metrics (e.g., SJR-index) and textual anomalies (e.g., statistical outliers) as dominant predictors; and (3) the BioMCD dataset, a large-scale benchmark with 13,160 retracted articles and 53,411 controls. BMDetect achieves 74.33% AUC, outperforming single-modality baselines by 8.6%, and demonstrates transferability across biomedical subfields. This work advances scalable, interpretable tools for safeguarding research integrity.

Large language models (LLMs) and their safety classifiers often perform poorly on low-resource languages due to limited training data and evaluation benchmarks. This paper introduces RabakBench, a new multilingual safety benchmark localized to Singapore's unique linguistic context, covering Singlish, Chinese, Malay, and Tamil. RabakBench is constructed through a scalable three-stage pipeline: (i) Generate - adversarial example generation by augmenting real Singlish web content with LLM-driven red teaming; (ii) Label - semi-automated multi-label safety annotation using majority-voted LLM labelers aligned with human judgments; and (iii) Translate - high-fidelity translation preserving linguistic nuance and toxicity across languages. The final dataset comprises over 5,000 safety-labeled examples across four languages and six fine-grained safety categories with severity levels. Evaluations of 11 popular open-source and closed-source guardrail classifiers reveal significant performance degradation. RabakBench not only enables robust safety evaluation in Southeast Asian multilingual settings but also offers a reproducible framework for building localized safety datasets in low-resource environments. The benchmark dataset, including the human-verified translations, and evaluation code are publicly available.