NPTPR A.I CyberSecurity Scoring
30/03/2026
Access Monitoring Plan
Access Monitoring Plan
No incidents recorded for Northwest Physical Therapy | Powered by RET in 2026.
No incidents recorded for Northwest Physical Therapy | Powered by RET in 2026.
No incidents recorded for Northwest Physical Therapy | Powered by RET in 2026.
Hamad Medical Corporation (HMC) is the main provider of secondary and tertiary healthcare in Qatar and one of the leading hospital providers in the Middle East. For more than three decades, HMC has been dedicated to delivering the safest, most effective and compassionate care to all its patients. HMC manages twelve hospitals – nine specialist hospitals and three community hospitals – as well as the National Ambulance Service and home and residential care services. In January 2016, HMC achieved the significant distinction of becoming the first healthcare system across the globe to have all its hospitals accredited by Joint Commission International under the Academic Medical Center accreditation program. Additionally, the National Ambulance Service, Home Healthcare Service, Stroke Service and Palliative Care, have all received this prestigious accreditation since 2011. To meet the needs of a rapidly growing population, HMC has announced ambitious plans to expand capacity across its network through to 2030. HMC is leading the development of the region’s first academic health system – combining innovative research, top-class education and excellent clinical care – and is committed to building a legacy of healthcare expertise in Qatar. HMC collaborates with key partners who are experts in Qatar and beyond, including Weill Cornell Medical College-Qatar, the Institute for Healthcare Improvement and Partners Healthcare, Boston. HMC is also the first hospital system in the Middle East to achieve institutional accreditation from the Accreditation Council of Graduate Medical Education – International (ACGME-I), which demonstrates excellence in the way medical graduates are trained through residency, internship and fellowship programs. For more information about working at HMC, please visit www.hmc.org.qa/en/employees_careers/employees_careers.aspx
Latest updates, reports, and threat intel affecting the global network.
Dify is an open-source LLM app development platform. Prior to 1.16.0, the PUT /console/api/apps/<app_id>/server endpoint in api/controllers/console/app/mcp_server.py used AppMCPServerController.put() to retrieve an AppMCPServer by the client-supplied server ID without verifying that the server belonged to the requested application and tenant. An authenticated workspace member could therefore change another application's MCP server status and parameters, potentially redirecting data or disabling the service. This issue is fixed in version 1.16.0.
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, a caller can use the request-level media_io_kwargs field to select the GLMGA video backend and supply large values for the fps and max_frames options without a strict work ceiling. GLMGA constructs and deduplicates an attacker-sized pre-decode frame-index list, allowing a compact request and tiny valid video to consume disproportionate CPU time and memory in the shared media-loading executor. This issue is fixed in version 0.30.0.
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the Rust frontend's track_http_metrics middleware records the raw HTTP method token as a Prometheus label for requests reaching registered routes. An unauthenticated attacker can send unique arbitrary method tokens to unguarded routes such as /tokenize, causing Prometheus's Family::get_or_create function to permanently create counter and histogram label sets. Those label sets increase process memory usage and enlarge the /metrics response until the service or monitoring path is exhausted. This issue is fixed in version 0.30.0.
vLLM is an inference and serving engine for large language models. From 0.24.0 until 0.30.0, the Qwen2VLVideoBackend and Qwen3VLVideoBackend classes accept request-level values for the media_io_kwargs.video.max_frames and media_io_kwargs.video.fps fields without enforcing server-side ceilings. An unauthenticated caller can submit these values to the /tokenize endpoint, causing the sampler to decode every frame selected from attacker-controlled video input, consume disproportionate frontend memory, and potentially terminate the API process before scheduling or admission control. The Rust frontend is not affected because it rejects the media_io_kwargs field. This issue is fixed in version 0.30.0.
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, structured-output request failures can escape request-scoped validation and reach the EngineCore fatal-error path. A per-request backend mismatch can re-raise a grammar compilation exception, padding produced by the ngram_gpu speculative-decoding mode can pass a negative token to guidance validation, and the Rust frontend can admit empty structured-output values that the Python frontend rejects, allowing ordinary constrained-generation requests to terminate the shared engine. This issue is fixed in version 0.30.0.
curl -i -X GET 'https://api.rankiteo.com/underwriter-getcompany-history?
linkedin_id=axa' -H 'apikey: YOUR_API_KEY_HERE'
Every week, Rankiteo analyzes billions of signals to give organizations a sharper, faster view of emerging risks. With deeper, more actionable intelligence at their fingertips, security teams can outpace threat actors, respond instantly to Zero-Day attacks, and dramatically shrink their risk exposure window.
Rankiteo is a unified scoring and risk platform that analyzes billions of signals weekly to help organizations gain faster, more actionable insights into emerging threats. Empowering teams to outpace adversaries and reduce exposure.