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Malicious SIM Card Attacks: How A 1980s Flaw Still Threatens Industrial IoT

Researchers proved that malicious SIM card attacks can execute attacker code inside the cellular modems running EV chargers, industrial routers, and telematics units. Six of eight tested industrial modules accepted the command. No mandatory patch exists yet, only a scattered, vendor-by-vendor response.

Malicious SIM card attacks just moved from theory to demonstrated fact for industrial hardware. Cellular IoT connections are on pace to reach 5.4 billion worldwide in 2026, according to IoT Analytics, and the module makers behind a large share of that growth just received a public lesson in a 40-year-old blind spot.

The vulnerability traces back to Proactive SIM, a legitimate cellular specification that lets a SIM card send commands directly to a device’s modem instead of only answering its requests. One of those commands, RUN AT, tells the modem to execute an AT command, the same control language used to operate modems since the 1980s, according to researchers from the University of Birmingham and security firm Fuzzware who presented the findings at the 2026 USENIX WOOT Conference in Baltimore. Using a custom toolkit called CATana, the team tested 26 devices: 18 smartphones and 8 cellular IoT modules used in EV chargers, industrial equipment, and connected cars.

Overlooked plant proteins may boost gut health

Plant-based diets are good for you. They promote gut, immune, metabolic and cardiovascular health, in part by cultivating a diverse community of intestinal bacteria. These benefits have long been known to stem partly from the fiber in plant-based foods, which is processed by our commensal gut bacteria, and the often brightly colored “phytochemicals” plants produce to protect themselves from environmental threats. But much remains to be learned about how exactly the bugs in our bellies process the constituents of vegetables to such salubrious effect.

A pair of studies led by Ludwig Princeton’s Jenna AbuSalim and director Joshua Rabinowitz—one in the Proceedings of the National Academy of Sciences, the other published in Nature Metabolism—shed new light on the matter.

One study reveals that plant fibers and proteins reprogram microbial metabolism to boost production of a healthful class of metabolites while suppressing that of their harmful counterparts. The other demonstrates that many physiologically important metabolites generally attributed to intestinal microbes are, in fact, also produced abundantly by mammalian metabolism.

Unlocking spontaneous cognition in the age of data science

Spontaneous brain activity is experimentally unconstrained. Embracing this freedom unlocks rich opportunities beyond task-based and naturalistic paradigms, the traditional Herculean pillars of constrained cognition. Emerging data-driven approaches can extract cognitively meaningful and translationally relevant insights from spontaneous brain activity: across profoundly altered states, across the human lifespan and across species.

Abstract: 1 Division of Endocrinology, Diabetes and Metabolism and the Joan and Sanford I

1 Division of Endocrinology, Diabetes and Metabolism and the Joan and Sanford I. Weill Center for Metabolic Health, Weill Cornell Medicine, New York, New York, USA.

2Diabetes Division, UMASS chan medical school, worcester, massachusetts, USA.

3Ansary Stem Cell Institute, Division of Regenerative Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York, USA.

AI Agent Security Risks: 5 Costly Blind Spots No One Is Fixing In 2026

Enterprises are pouring $2.59 trillion into AI in 2026, but most of that budget assumes agents will behave. AI agent security risks now center on permissions and identity, not intelligence, and the average agent-linked breach costs $4.7 million. Procurement teams still aren’t asking the one question that would catch it before signing.

AI agent security risks have overtaken model accuracy as the top worry inside enterprise IT departments this year. Global AI spending is on track to hit $2.59 trillion in 2026, a 47% jump from 2025, according to Gartner’s May forecast. A growing share of that money is going toward systems that act on their own rather than just answer questions. The uncomfortable part: most of the AI agent security risks now showing up in production weren’t designed against. They were inherited from a rush to deploy.

Most agents ship with more access than the task requires, because narrow permissions slow deployment down. A support agent built to retrieve ticket history often keeps the ability to edit records or export data long after launch, according to enterprise security researchers at miniOrange. Nobody revokes it because nobody owns that job. This is where most AI agent security risks actually begin, quietly, at the setup stage, long before any attacker gets involved.

Focal Therapy for Prostate Cancer

This retrospective study used the National Cancer Database (NCDB) to identify patients 50 years or older with nonmetastatic prostate cancer diagnosed between 2010 and 2023 and seen at US centers with Commission on Cancer accreditation.9 Treatment categories were derived from NCDB first-course treatment variables. The study was approved by the University of Pittsburgh institutional review board (STUDY26020146) and followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.10

Inappropriate focal therapy use was defined as focal therapy in low-, high-, or very–high-risk disease.2 Intermediate-risk focal therapy was reported separately because the NCDB does not capture trial enrollment, registry participation, or lesion characteristics needed to classify appropriateness.

Temporal trends were assessed using Cochran-Armitage tests. Multivariable logistic regression evaluated patient-and facility-level characteristics associated with focal therapy vs radical prostatectomy. Adjusted predicted probabilities and average marginal effects were estimated using facility-clustered robust standard errors. The eMethods in Supplement 1 provide additional methodologic detail.

Navigating Challenges and Opportunities in MultiOmics Integration for Personalized Healthcare

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022–2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.

Multidisciplinary research priorities for artificial intelligence in mental health: a call to action

The use of artificial intelligence (AI) is anticipated to transform mental health care. However, the rapid research growth in this field has outpaced coordinated frameworks, leaving research efforts fragmented, standards inconsistent, and safeguards for safety and ethics largely absent. This Position Paper outlines a coordinated roadmap to guide the responsible evaluation and implementation of AI in mental health, structured across four overarching priority domains that define near-term actions and longer-term strategic goals.

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