DTF A.I CyberSecurity Scoring
19/05/2026
Access Monitoring Plan
Access Monitoring Plan
No incidents recorded for Dahua Technology France in 2026.
No incidents recorded for Dahua Technology France in 2026.
No incidents recorded for Dahua Technology France in 2026.
Computer and Network Security
## Our core business We manage linux / unix server infrastructures and build the efficient and secure networking environments using hardware cutting edge technologies suited to the needs of the project and the client. We believe in quality, opposed to quantity. Our company consists of highly qualified, experienced people, who share a common passion of both server and network infrastructure management. ## Our principles We stated basic principles, that we see as crucial in successful delivery of a stable and secure network environment project: • we tend to give client what he needs, not what he wants • we believe in open communication with client • client is not our enemy • "strict" is for corporations, we believe in "flexible" • teamwork - share victory, share defeat ## What we do best We specialise in linux / unix server and large network environments management. We think, that every need deserves a custom solution, as in most of cases it is cheaper and easier, than a ready solution with after-integration pain. We deliver individual solutions, that best fulfill need of our clients. The impact is put always on high quality and technological innovation of the project. ## Technologies we use We use a set of technologies we think are best suitable for big projects and those we have experience with, although we do not limit ourselves to them only. We constantly seek for new horizons, new trends and test them, so when they become mature, we can make use of them in our work. Network Security Solutions is always in search of new possibilities. ## Our offer Linux / Unix servers and network Router / Switch management, from big complicated environments to smaller ones. We offer hosting on our servers as well as the ability to manage servers from our customers. We provide 24/7/365 monitoring for customers whose services we manage.
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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'
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