Toptal Designers A.I CyberSecurity Scoring
06/03/2026
Access Monitoring Plan
Access Monitoring Plan
No incidents recorded for Toptal Designers in 2026.
No incidents recorded for Toptal Designers in 2026.
No incidents recorded for Toptal Designers in 2026.
HDR is an employee-owned design firm specializing in engineering, architecture, environmental and construction services. We’re ranked No. 6 among the world’s design firms and we’re the largest healthcare design firm. Led by the strength of our values and a culture shaped by employee ownership, we network with each other, build on each other’s contributions, and collaborate together to make great things possible. When you join HDR, we give you license to do the same. We help you take charge of your career, giving you multiple growth opportunities along the way. So, what are you waiting for? Come grow with us.
Dar is one of the world’s leading consultancies, providing design, planning, engineering, sustainability consulting, digital solutions and services, project management, and facilities management for buildings, cities, transportation, civil infrastructure, water, and the environment. We are a global community of talented and innovative engineers, planners, economists, architects, sustainability specialists, digital experts, designers, project management specialists, construction management professionals, and multidisciplinary experts. Together, we take on the world’s most exciting and ambitious projects in order to nurture sustainable development, empower and connect communities, create more and better opportunities, and enhance lives. Dar has over 10,000 professionals in 60 offices across the Middle East, Africa, Asia, UK, and Europe. Collectively, our teams have delivered over 4,500 projects, worth more than US$540 billion, for more than 950 clients around the world. Dar is the founding member of Sidara, a global collective of the world’s brightest and best planners, designers, engineers, and consultants who have come together on a shared mission: to advocate for the world as we would for our own home. For us, that means taking on projects with courage and resilience, bravely setting up a home where others won’t go, intertwining ourselves with the communities we create for, and navigating immense complexity and ambiguity to empower our clients, craft solutions for the world’s most critical challenges, and deliver projects that elevate how people live, connect communities, and inspire global impact, all while showcasing and honoring the unique cultures we serve. Dar and its sister companies under the Sidara Collaborative can offer a broad and integrated range of highly specialised and world-class services—from architecture and design, engineering, and project management to digital strategy, sustainability consulting, and energy innovation - under one umbrella.
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'
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