Paperjam Club A.I CyberSecurity Scoring
06/01/2026
Access Monitoring Plan
Access Monitoring Plan
No incidents recorded for Paperjam Club in 2026.
No incidents recorded for Paperjam Club in 2026.
No incidents recorded for Paperjam Club in 2026.
Events Services
Encore is your full-service event production partner with more than 80 years of experience. Each year, Encore delivers more than 350,000 events in 20 countries across North America, Europe, the Middle East, Australia and Asia Pacific. Through event technology, rigging infrastructure, production and creative services, our team brings a unique blend of technical expertise with a commitment to delivering excellent service for every event. Proud to be one of the Fortune 100 Best Companies to Work For® in 2025 and a Certified Great Place to Work™ for five years running. Encore: events that transform.
Latest updates, reports, and threat intel affecting the global network.
Designed for CISOs, CTOs, risk officers, compliance leaders, and key decision-makers in finance, industry, and tech, this event delivers sharp...
The Luxembourg chapter of the international tech professionals association Isaca will hold a session on boosting interpersonal communication skills.
Find out more about Micse's cybersecurity mentoring programme and learn about entry level jobs in the field of cybersecurity at this meetup aimed at women.
In today's digital era, the responsibility for overseeing cyber-risk management in modern organisations is increasingly falling on the shoulders of Boards...
The Paperjam Club welcomes a new member: H2Lux. Introduction in three questions.
As part of the 10×6 Leading CIOs' Challenges 2024, organised by Paperjam+Delano Business Club on Tuesday 26th of March 2024, Jacques Ruckert...
Arendt's Cybersecurity & Information Protection Team is organising a webinar scheduled for Thursday 5 October to discuss the Digital...
As part of the 10×6 Leading CIOs' challenges event organised by the Paperjam + Delano Business Club on Wednesday 25 January, Nataliia Iskra, director and...
The Paperjam + Delano Club welcomes a new member: The Blockhouse Technology. Introduction in three questions.
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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