What is the official website of Rolls-Royce Hungary ?

The official website of Rolls-Royce Hungary is https://www.rolls-royce.com/country-sites/hungary.aspx.

What is Rolls-Royce Hungary's cybersecurity risk score?

Rolls-Royce Hungary has an AI-generated cybersecurity risk score of 747 out of 1000, indicating a Moderate security posture according to Rankiteo's assessment.

How many security incidents has Rolls-Royce Hungary experienced?

According to Rankiteo, Rolls-Royce Hungary currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has Rolls-Royce Hungary been affected by any supply chain cyber incidents ?

According to Rankiteo, Rolls-Royce Hungary has been affected by a supply chain cyber incident involving Defused, with the incident ID ERIDEFJOHROLVID1772180734.

Does Rolls-Royce Hungary have SOC 2 Type 1 certification ?

According to Rankiteo, Rolls-Royce Hungary is not certified under SOC 2 Type 1.

Does Rolls-Royce Hungary have SOC 2 Type 2 certification ?

According to Rankiteo, Rolls-Royce Hungary does not hold a SOC 2 Type 2 certification.

Does Rolls-Royce Hungary comply with GDPR ?

According to Rankiteo, Rolls-Royce Hungary is not listed as GDPR compliant.

Does Rolls-Royce Hungary have PCI DSS certification ?

According to Rankiteo, Rolls-Royce Hungary does not currently maintain PCI DSS compliance.

Does Rolls-Royce Hungary comply with HIPAA ?

According to Rankiteo, Rolls-Royce Hungary is not compliant with HIPAA regulations.

Does Rolls-Royce Hungary have ISO 27001 certification ?

According to Rankiteo,Rolls-Royce Hungary is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

Which industry does Rolls-Royce Hungary operate in ?

Rolls-Royce Hungary operates primarily in the Aviation and Aerospace Component Manufacturing industry.

How many employees does Rolls-Royce Hungary have ?

Rolls-Royce Hungary employs approximately 0 people worldwide.

Does Rolls-Royce Hungary own any subsidiaries ?

Rolls-Royce Hungary presently has no subsidiaries across any sectors.

How many LinkedIn followers does Rolls-Royce Hungary have ?

Rolls-Royce Hungary's official LinkedIn profile has approximately 575 followers.

What is the NAICS classification code for Rolls-Royce Hungary ?

Rolls-Royce Hungary is classified under the NAICS code 3364, which corresponds to Aerospace Product and Parts Manufacturing.

Does Rolls-Royce Hungary have a Crunchbase profile ?

No, Rolls-Royce Hungary does not have a profile on Crunchbase.

Does Rolls-Royce Hungary have a LinkedIn profile ?

Yes, Rolls-Royce Hungary maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/rolls-royce-hungary.

How many cybersecurity incidents has Rolls-Royce Hungary experienced ?

As of June 23, 2026, Rankiteo reports that Rolls-Royce Hungary has experienced 2 cybersecurity incidents.

How many peer or competitor companies does Rolls-Royce Hungary have ?

Rolls-Royce Hungary has an estimated 2,817 peer or competitor companies worldwide.

What types of cybersecurity incidents has Rolls-Royce Hungary faced ?

Incident Types: The types of cybersecurity incidents that have occurred include Breach and Cyber Attack.

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Rolls-Royce Hungary

Boost Score +25 with badge (5 left)

747

/1000

Moderate

772

/1000

Fair

Rolls-Royce Hungary Vendor Cyber Rating & Cyber Score

rolls-royce.com

Add Your Compliance Badge

At Rolls-Royce, we pioneer the power that matters. Our Hungarian team, located in Budapest, is specialized in Electrical technology. From batteries, via power electronics and controls to electric machines, we develop the solutions that will power aviation, transport and energy generation applications of tomorrow in efficient and clean ways.

Rolls-Royce Hungary A.I CyberSecurity Scoring

Scoring History

Rolls-Royce Hungary

Rolls-Royce Hungary CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

Severity

Impact

Seen

Blog Details

Supply Chain Source

Incident Details

View

Rolls-Royce

Breach

2/2026

JOHROLERI177220...

Rolls-Royce

Cyber Attack

2/2026

Defused

ERIDEFJOHROLVID...

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A.I.

Rolls-Royce Hungary Company Scoring based on AI Models

Cyber Incidents Likelihood 3 - 6 - 9 months

Actual

Prediction

Incident Predictions locked

Access Monitoring Plan

A.I Risk Score Likelihood 3 - 6 - 9 months

Actual

Prediction A

Prediction B

Prediction C

Prediction D

A.I. Risk Score Predictions locked

Access Monitoring Plan

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Underwriter Stats for Rolls-Royce Hungary

Incidents vs Aviation and Aerospace Component Manufacturing Industry Avg (This Year)

No incidents recorded for Rolls-Royce Hungary in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for Rolls-Royce Hungary in 2026.

Incident Types Rolls-Royce Hungary vs Aviation and Aerospace Component Manufacturing Industry Avg (This Year)

No incidents recorded for Rolls-Royce Hungary in 2026.

Incident History - Rolls-Royce Hungary (X = Date, Y = Severity)

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SUBSIDIARY

Rolls-Royce Hungary Subsidiaries

Rolls-Royce Hungary

Aviation and Aerospace Component Manufacturing

Website: https://www.rolls-royce.com/country-sites/hungary.aspx

Company Size: 0

Rolls-RoyceIndustrial Machinery Manufacturing

YocovaAirlines and Aviation

Rolls-Royce Japan機械・産業エンジニアリング

Rolls-Royce IndiaIndustrial Machinery Manufacturing

Rolls-Royce Singapore HubIndustrial Machinery Manufacturing

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NEWS

Rolls-Royce Hungary CyberSecurity News

Latest updates, reports, and threat intel affecting the global network.

June 19, 2019 07:00 AM

Rolls-Royce Buys Siemens’ eAircraft Business to Jolt Electrification Efforts

Rolls-Royce has agreed to purchase Siemens' electric and hybrid-electric aerospace propulsion activities.

Read Article

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CVE

Latest

Global CVEs (Not Company-Specific)

CVE-2026-54236

SUMMARY

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.

Published

Date2026-06-22

Updated

Date2026-06-22

RISK INFORMATION (Score: 5.3)

cvss3

Base Score: 5.3

Complexity: LOW

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N

Impact Score

1.4

Exploitability

3.9

REFERENCES

CVE-2026-54235

SUMMARY

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.

Published

Date2026-06-22

Updated

Date2026-06-22

RISK INFORMATION (Score: )

cvss4

Base Score: 6.9

Complexity: LOW

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

Impact Score

Exploitability

REFERENCES

CVE-2026-54233

SUMMARY

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.

Published

Date2026-06-22

Updated

Date2026-06-22

RISK INFORMATION (Score: 6.5)

cvss3

Base Score: 6.5

Complexity: LOW

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Impact Score

3.6

Exploitability

2.8

REFERENCES

CVE-2026-54232

SUMMARY

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.

Published

Date2026-06-22

Updated

Date2026-06-22

RISK INFORMATION (Score: 8.8)

cvss3

Base Score: 8.8

Complexity: LOW

CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Impact Score

5.9

Exploitability

2.8

REFERENCES

https://github.com/vllm-project/vllm/security/advisories/GHSA-jrf6-vqxq-pjv2

CVE-2026-53923

SUMMARY

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.

Published

Date2026-06-22

Updated

Date2026-06-22

RISK INFORMATION (Score: )

cvss4

Base Score: 5.3

Complexity: LOW

CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:L/VI:L/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

Impact Score

Exploitability

REFERENCES

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