EDP Renewables A.I CyberSecurity Scoring
30/03/2026
Access Monitoring Plan
Access Monitoring Plan
No incidents recorded for EDP Renewables in 2026.
No incidents recorded for EDP Renewables in 2026.
No incidents recorded for EDP Renewables in 2026.
What if your next career adventure started with ENGIE? Joining ENGIE means being part of a company at the forefront of the energy transition, committed to accelerating the shift to a carbon-neutral economy. With 98,000 employees across 30 countries, ENGIE's operations cover the entire energy value chain, from production to infrastructures and sales. ENGIE works across a range of complementary areas: renewable electricity and green gas production, flexibility assets (notably batteries), gas and electricity transmission and distribution networks, local energy infrastructures (heating and cooling networks) and energy supply to individuals, local authorities and businesses. #WithENGIE
Siemens Energy is one of the world’s leading energy technology companies. The company works with its customers and partners on energy systems for the future, thus supporting the transition to a more sustainable world. With its portfolio of products, solutions and services, Siemens Energy covers almost the entire energy value chain – from power generation and transmission to storage. The portfolio includes conventional and renewable energy technology, such as gas and steam turbines, hybrid power plants operated with hydrogen, and power generators and transformers. A majority stake in the wind power subsidiary Siemens Gamesa Renewable Energy (SGRE) makes Siemens Energy a global market leader for renewable energies. An estimated one-sixth of the electricity generated worldwide is based on technologies from Siemens Energy. Siemens Energy employs 103,000 people worldwide in more than 90 countries and generated revenue of around €39.1 billion in fiscal year 2025. Siemens Energy is a trademark licensed by Siemens AG
Hydro-Québec produit, transporte et distribue de l'électricité. Elle exploite essentiellement des énergies renouvelables, plus particulièrement l'hydroélectricité. Elle fait aussi de la recherche dans les domaines reliés à l'énergie et s'intéresse activement à l'efficacité énergétique. En outre, elle développe les technologies issues de ses recherches. Son unique actionnaire est le gouvernement du Québec. En vertu de la loi, le producteur fournit au distributeur un volume annuel d'électricité patrimoniale au-delà duquel le distributeur s'approvisionne sur les marchés dans un contexte de libre concurrence. Les activités de transport et de distribution sont réglementées. L'entreprise comprend quatre divisions : Hydro-Québec Production Hydro-Québec Production produit de l'électricité et la commercialise sur les marchés de gros au Québec et hors Québec. Hydro-Québec TransÉnergie Hydro-Québec TransÉnergie exploite le plus vaste réseau de transport d'électricité de l'Amérique du Nord pour le bénéfice de ses clients au Québec et hors Québec. Hydro-Québec Distribution Hydro-Québec Distribution assure aux Québécois un approvisionnement fiable en énergie. Au-delà du volume annuel d'électricité patrimoniale fourni par Hydro-Québec Production, elle s'approvisionne sur les marchés. Elle s'emploie à ce que ses clientèles fassent une utilisation efficace de l'électricité. Hydro-Québec Équipement et la Société d'énergie de la Baie James, filiale d'Hydro-Québec, sont les maîtres d'oeuvre des projets de construction d'Hydro-Québec Production et d'Hydro-Québec TransÉnergie.
Latest updates, reports, and threat intel affecting the global network.
According to EDP's Articles of Association, EDP's General and Supervisory Board is primarily responsible for (i) overseeing and supervising the activity of...
Microsoft has deepened its commitment to clean energy by securing a long-term virtual power purchase agreement with EDP Renewables North America.
Electricity grids are becoming increasingly complex. Digitalisation or digital transformation allows for balance and efficiency.
Energy giant EDP Renewables North America (EDPR NA) has confirmed a ransomware attack that affected information systems at parent company Energias de Portugal...
The president of EDP Redes Spain closes the seminar “An impulse towards energy transition: new technologies” in Menéndez Pelayo...
The manager will lead EDP's Digital Global Unit, the area responsible for Digital Transformation and for managing the Group's Information...
Renewable energy company EDP Renewables notified its landowners of a ransomware attack that it suffered in the spring of 2020.
EDP Renewables North America LLC has confirmed that it was targeted in a ransomware attack, with the company advising that those behind the...
EDP Distribuição Director Aurélio Blanquet, the newly elected Chair of the ENCS Assembly Committee, talked to Innovation News Network about his new role and...
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'
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