Colibri Group A.I CyberSecurity Scoring
10/06/2026
Access Monitoring Plan
Access Monitoring Plan
No incidents recorded for Colibri Group in 2026.
No incidents recorded for Colibri Group in 2026.
No incidents recorded for Colibri Group in 2026.
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.
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 :-)
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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