Comparison Overview
ScreenBeam Latinoamérica
ScreenBeam Latinoamérica
220 Devcon Dr, None, San Jose, California, US, 95112
Last Update: 20/05/2026
Crear áreas de trabajo más seguras, fáciles de usar e inteligentes son esenciales para las corporaciones y escuelas de todo el mundo, cuando consideran cómo volver a la oficina o al campus. ScreenBeam proporciona el único sistema de presentación inalámbrica sin aplica...
GFT Technologies
Schelmenwasenstr. 34, Stuttgart, 70567, DE
Last Update: 01/04/2026
GFT Technologies is an AI-centric global digital transformation company. We design advanced data and AI transformation solutions, modernize technology architectures and develop next-generation core systems for industry leaders in Banking, Insurance, Manufacturing and Ro...
Compliance Ranges Comparison
ScreenBeam Latinoamérica
GFT Technologies
Benchmark & Cyber Underwriting Signals
Incidents vs IT Services and IT Consulting Industry Avg (This Year)
No incidents recorded for ScreenBeam Latinoamérica in 2026.
Incidents vs IT Services and IT Consulting Industry Avg (This Year)
No incidents recorded for GFT Technologies in 2026.
Incident History - ScreenBeam Latinoamérica (X = Date, Y = Severity)
ScreenBeam Latinoamérica cyber incidents detection timeline including parent company and subsidiaries.
Incident History - GFT Technologies (X = Date, Y = Severity)
GFT Technologies cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents
ScreenBeam Latinoamérica
GFT Technologies
FAQ
Latest Global CVEs
vLLM through 0.29.0 fails to properly clean up decode-side metadata for rejected inference requests in prefill/decode disaggregated deployments. Remote attackers can submit requests with max_tokens=0 to exhaust decode-worker memory without bound until the worker restarts.
- https://github.com/vllm-project/vllm
- https://github.com/vllm-project/vllm/blob/v0.29.0/vllm/distributed/kv_transfer/kv_connector/v1/nixl/push_worker.py#L162-L181
- https://github.com/vllm-project/vllm/pull/55677
- https://www.vulncheck.com/advisories/vllm-through-0.29.0-memory-exhaustion-via-rejected-requests
redis-parser through 3.0.0 contains a denial of service vulnerability in the RESP protocol parser that allows malicious Redis endpoints to crash the client process through unbounded recursion on nested arrays. Attackers can send crafted RESP byte streams with repeated array headers that exhaust the V8 call stack, causing an uncaught RangeError that terminates the Node.js process without triggering error handling callbacks.
- https://github.com/NodeRedis/node-redis-parser
- https://github.com/NodeRedis/node-redis-parser/blob/701655430f5f7d9ca00892a02f7eefcbc1193a98/lib/parser.js#L204-L213
- https://github.com/NodeRedis/node-redis-parser/blob/701655430f5f7d9ca00892a02f7eefcbc1193a98/lib/parser.js#L291-L306
- https://github.com/redis/ioredis/issues/2108
- https://www.vulncheck.com/advisories/redis-parser-through-3.0.0-denial-of-service-via-unbounded-recursion
Improper neutralization of special elements in output used by a downstream component ('injection') in Azure Cosmos DB allows an authorized attacker to elevate privileges over a network.
Server-side request forgery (ssrf) in Azure AI Foundry allows an unauthorized attacker to elevate privileges over a network.
Missing authentication for critical function in Azure AI Foundry allows an unauthorized attacker to elevate privileges over a network.