CAERIC A.I CyberSecurity Scoring
14/12/2025
Access Monitoring Plan
Access Monitoring Plan
No incidents recorded for Corporate advisory e risorse interculturali - CARINT in 2026.
No incidents recorded for Corporate advisory e risorse interculturali - CARINT in 2026.
No incidents recorded for Corporate advisory e risorse interculturali - CARINT in 2026.
The Ministry of Education is committed to bring out the best in every child by providing a variety of learning opportunities, nurturing the whole child holistically. The broad-based education exposes each child to different possibilities and ensures a strong foundation for learning in a variety of domains. MOE has created a variegated education landscape with diverse pathways, aimed at helping our children cultivate qualities such as creativity, confidence, compassion and resilience – life skills essential in a rapidly changing world. They also learn values such as respect, responsibility, integrity, care, and harmony; all of which are important for safeguarding our cohesive and harmonious multi-racial and multi-cultural society. Collectively, these are the Education Ministry’s efforts to ensure that all our students acquire a broad and deep foundation for a lifelong journey of learning.
Founded in 1965 in Sweden, EF (Education First) is a global association of education companies that shares a common mission of opening the world through education, offering language, academic, cultural exchange and education travel programs. Some companies are in the business of technology. Others are in the business of finance, sports, or soft drinks. At EF, we’re in a different kind of business. One that’s a little less tangible, and a lot more important. We’re in the business of understanding. For 60 years we’ve been the leader in international educational programs and culturally rich travel experiences with the power to change how people think, feel and act. The programs we deliver open the world to students and travelers in ways that challenge biases, overcome barriers, and pave the way for a more understanding world. You’ll find us working across more than 50 countries, with offices in some of the world’s greatest cities—each one filled with smart, driven people who push each other to be better every day. And yes, we have technology, we have finance, we even have sports with our own professional cycling team. But it’s what we do with it—building greater understanding, breaking down barriers, and creating a better world that makes all the difference. A notice on recruitment scams EF (Education First) accepts job applicant applications, reviews resumes and will contact you directly if there is an interest in your resume. Only submit your resume through our official website, careers.ef.com. We never ask our applicants to pay a fee for any service whatsoever.
We are Cambridge University Press & Assessment. We are a world-leading academic publisher and assessment organisation, and part of the University of Cambridge. We’re driven by a simple mission – to contribute to society through the pursuit of education, learning, and research at the highest international levels of excellence. Our team is one connected, global community, pursuing potential and moving forward, together. We will keep exploring, collaborating, and innovating to find bold new ways to spread knowledge, spark enquiry, and aid understanding.
Aakash Educational Services Limited (AESL) is a leading test-prep company in India with a strong legacy of over 37 years, that provides comprehensive test preparatory services for students preparing for Medical (NEET) and Engineering Entrance Examinations (JEE), School/Board Exams & Competitive Exams such as NTSE, KVPY, and Olympiads. Founded in 1988, we have 300+ centres nationwide and growing. Over the last three decades, Aakash has helped transform the lives of lakhs of students by helping them fulfil their dreams of becoming a doctor or an engineer. With a strong selection track record of producing 85,000+ Rankers across NEET & JEE, Aakash commitment to a ‘Student First’ approach has been at the core of all initiatives. Whether it is a new product launch or tech innovations to make the student experience more engaging and meaningful, Aakash leaves no stone unturned to offer simply the best. At Aakash, we are on a mission to build an omni-channel learning platform that will catapult the test-prep experience to the next level and help build India’s largest education company. We intend to transform the test-prep sector through innovative & technology-driven Hybrid programs and digitally-enabled learning solutions to complement a robust and fast-growing national footprint of centres that will enable millions of students to access quality education beyond geographical barriers. We are excited to create amazing opportunities to work in an environment that encourages innovation, collaboration, engagement, peer-to-peer learning and continuous mentoring opportunities. At Aakash, we are always on the lookout for exceptional professionals who are agile, big thinkers, and are ready to challenge the status quo. Needless to say, the supportive, rewarding and flexible culture at Aakash offers plenty of opportunities and avenues for career development.
Latest updates, reports, and threat intel affecting the global network.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, the fix for CVE-2026-22778, which introduced a sanitize_message helper that strips object-repr memory addresses from error messages before they reach the client, is incomplete: several response paths echo str(exc) directly to clients without calling sanitize_message. The unsanitized sites include the Anthropic API router in vllm/entrypoints/anthropic/api_router.py (the POST /v1/messages and POST /v1/messages/count_tokens handlers), the Server-Sent Events streaming converter in vllm/entrypoints/anthropic/serving.py, and the realtime speech-to-text WebSocket in vllm/entrypoints/speech_to_text/realtime/connection.py. These paths catch the exception inside the route coroutine and construct the JSONResponse themselves, bypassing the sanitizing global FastAPI exception handler, and WebSocket frames do not traverse that handler chain at all. Using the same primitive as the parent issue, an unauthenticated attacker can send malformed image bytes through the Anthropic Messages API image content parts so that PIL.Image.open raises an UnidentifiedImageError whose message contains the BytesIO object repr, leaking the heap memory address verbatim in the error.message field of the response body. This vulnerability is fixed in 0.23.1rc0.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, ll temperature validation gates use comparison operators (<, >), which silently evaluate to False for NaN and for positive Infinity in Python's IEEE 754 float semantics. Both values pass every guard and propagate to GPU sampling kernels, where they produce undefined behavior or CUDA errors that can crash the inference worker. This vulnerability is fixed in 0.23.1rc0.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, vLLM's /v1/audio/transcriptions endpoint limits compressed upload size but not decoded PCM output. A 25MB OPUS file expands to ~14.9GB of float32 PCM at decode time. This vulnerability is fixed in 0.23.1rc0.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package. The package is installed from a custom index (flashinfer.ai/whl/) using --extra-index-url, but the package name was not registered on PyPI, and UV_INDEX_STRATEGY="unsafe-best-match" is set globally. An attacker who registers flashinfer-jit-cache on PyPI with version 0.6.11.post2 can execute arbitrary code as root during the Docker build and backdoor every resulting container image, enabling exfiltration of all user prompts, API credentials, and model data from production vLLM deployments This vulnerability is fixed in 0.22.1.
vLLM is an inference and serving engine for large language models (LLMs). From 0.5.5 until 0.23.1rc0, integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels (csrc/quantization/gguf/gguf_kernel.cu) causes partial tensor processing. The output tensor is allocated at full size via torch::empty (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users' inference requests, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0.
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.