Comparison Overview
FPT Industrial
FPT Industrial
Via Puglia 15, Turin, 10156, IT
Last Update: 11/04/2026
Powering the Future of Industry! For over 100 years, we’ve been shaping sustainable and efficient solutions. With over 8,000 people working across nearly 100 different countries, we shape engines and electric solutions for on-road, off-road, marine and power generati...
Rencol Components Ltd.
Unit 2, Avonbridge Trading Estate, Bristol, BS11 9QD, GB
Last Update: 08/09/2026
Following 100 years of successful design and manufacturing experience in plastic and metals, Rencol Components has built a truly global supply chain - rapidly expanding our range of high-quality, competitively-priced industrial components. Rencol has developed a mature...
Compliance Ranges Comparison
FPT Industrial
Rencol Components Ltd.
Benchmark & Cyber Underwriting Signals
Incidents vs Industrial Machinery Manufacturing Industry Avg (This Year)
No incidents recorded for FPT Industrial in 2026.
Incidents vs Industrial Machinery Manufacturing Industry Avg (This Year)
No incidents recorded for Rencol Components Ltd. in 2026.
Incident History - FPT Industrial (X = Date, Y = Severity)
FPT Industrial cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Rencol Components Ltd. (X = Date, Y = Severity)
Rencol Components Ltd. cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents
FPT Industrial
Rencol Components Ltd.
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.