LUCE A.I CyberSecurity Scoring
23/02/2026
Access Monitoring Plan
Access Monitoring Plan
No incidents recorded for Lund University Commissioned Education in 2026.
No incidents recorded for Lund University Commissioned Education in 2026.
No incidents recorded for Lund University Commissioned Education in 2026.
I created "My own company" in order to be able to invoice my clients, but I am now fully retired. I spend a fair share of my leisure time enhancing my "hobby" website (https://anglais-pratique.fr/), which is primarily intended for French speakers who wish to improve their English. I want to leave this group ("My own company..."), but can't find how, probably because I created it!!! So let it be :-)
L'AFPA, PREMIER ORGANISME DE FORMATION PROFESSIONNELLE DES ADULTES Avec plus de 140 000 personnes formées chaque année dans plus de 200 implantations partout en France, l’Afpa, devenue Agence nationale pour la formation professionnelle des adultes en janvier 2017, est depuis plus de 65 ans, le premier organisme de formation des actifs, salariés et demandeurs d’emploi. Sa dimension nationale en fait l'un des principaux acteurs de la politique de l'emploi et de la formation professionnelle. UNE FORMATION UTILE ET PERFORMANTE, AU SERVICE DES ENTREPRISES L’Afpa propose une large gamme de formations qualifiantes et certifiantes, immédiatement utiles sur le marché de l’emploi, dans une logique de formation tout au long de la vie : insertion, reconversion, professionnalisation. Elle forme prioritairement aux métiers qui recrutent, considérant que la formation doit être un investissement pour les entreprises, et une arme majeure de lutte contre le chômage. Six mois après une formation Afpa, 60% des stagiaires ont retrouvé un emploi. UNE OFFRE DE FORMATION RENOUVELEE, EN LIGNE AVEC LE CPF Parce qu’aujourd’hui la formation devient plus que jamais la responsabilité de tous, l’Afpa déploie une nouvelle offre plus souple, modulaire et compatible avec le Compte Personnel de Formation (CPF). 200 ingénieurs de formation assurent une veille permanente pour répondre au double impératif de l’efficience économique et de l’utilité sociale et être toujours plus proche des besoins des entreprises et de l'évolution des métiers.
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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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