What is the official website of HEKUMA GmbH ?

The official website of HEKUMA GmbH is http://www.hekuma.com.

What is HEKUMA GmbH's cybersecurity risk score?

HEKUMA GmbH has an AI-generated cybersecurity risk score of 745 out of 1000, indicating a Moderate security posture according to Rankiteo's assessment.

How many security incidents has HEKUMA GmbH experienced?

According to Rankiteo, HEKUMA GmbH currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has HEKUMA GmbH been affected by any supply chain cyber incidents ?

According to Rankiteo, HEKUMA GmbH has not been affected by any supply chain cyber incidents, and no incident IDs are currently listed for the organization.

Does HEKUMA GmbH have SOC 2 Type 1 certification ?

According to Rankiteo, HEKUMA GmbH is not certified under SOC 2 Type 1.

Does HEKUMA GmbH have SOC 2 Type 2 certification ?

According to Rankiteo, HEKUMA GmbH does not hold a SOC 2 Type 2 certification.

Does HEKUMA GmbH comply with GDPR ?

According to Rankiteo, HEKUMA GmbH is not listed as GDPR compliant.

Does HEKUMA GmbH have PCI DSS certification ?

According to Rankiteo, HEKUMA GmbH does not currently maintain PCI DSS compliance.

Does HEKUMA GmbH comply with HIPAA ?

According to Rankiteo, HEKUMA GmbH is not compliant with HIPAA regulations.

Does HEKUMA GmbH have ISO 27001 certification ?

According to Rankiteo,HEKUMA GmbH is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

Which industry does HEKUMA GmbH operate in ?

HEKUMA GmbH operates primarily in the Automation Machinery Manufacturing industry.

How many employees does HEKUMA GmbH have ?

HEKUMA GmbH employs approximately 133 people worldwide.

Does HEKUMA GmbH own any subsidiaries ?

HEKUMA GmbH presently has no subsidiaries across any sectors.

How many LinkedIn followers does HEKUMA GmbH have ?

HEKUMA GmbH's official LinkedIn profile has approximately 2,442 followers.

What is the NAICS classification code for HEKUMA GmbH ?

HEKUMA GmbH is classified under the NAICS code 33325, which corresponds to Others.

Does HEKUMA GmbH have a Crunchbase profile ?

No, HEKUMA GmbH does not have a profile on Crunchbase.

Does HEKUMA GmbH have a LinkedIn profile ?

Yes, HEKUMA GmbH maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/hekuma-gmbh.

How many cybersecurity incidents has HEKUMA GmbH experienced ?

As of June 23, 2026, Rankiteo reports that HEKUMA GmbH has not experienced any cybersecurity incidents.

How many peer or competitor companies does HEKUMA GmbH have ?

HEKUMA GmbH has an estimated 3,388 peer or competitor companies worldwide.

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HEKUMA GmbH

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745

/1000

Moderate

770

/1000

Fair

HEKUMA GmbH Vendor Cyber Rating & Cyber Score

hekuma.com

Add Your Compliance Badge

HEKUMA – perfecting your performance We at HEKUMA specialize in automation at the highest level and cover a wide range of injection molding and assembly processes for the plastics industry. Especially where high product quality, complex manufacturing requirements and large production output have to be combined, we are the right partner! Since 2023, the BBS, Kahle, teamtechnik and HEKUMA brands have, together, formed the "Production Automation" Business Unit in the Dürr Group, with 2,500 employees at 20 sites world-wide. To bring this cooperation to the fore, we will operate under a single, uniform brand, “BBS Automation”, from June 2025. Together, we offer a comprehensive portfolio for a wide-range of sectors, from insertion and

HEKUMA GmbH A.I CyberSecurity Scoring

Scoring History

HEKUMA GmbH

HEKUMA GmbH CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

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Impact

Seen

Blog Details

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A.I.

HEKUMA GmbH 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 HEKUMA GmbH

Incidents vs Automation Machinery Manufacturing Industry Avg (This Year)

No incidents recorded for HEKUMA GmbH in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for HEKUMA GmbH in 2026.

Incident Types HEKUMA GmbH vs Automation Machinery Manufacturing Industry Avg (This Year)

No incidents recorded for HEKUMA GmbH in 2026.

Incident History - HEKUMA GmbH (X = Date, Y = Severity)

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SUBSIDIARY

HEKUMA GmbH Subsidiaries

HEKUMA GmbH

Automation Machinery Manufacturing

Website: http://www.hekuma.com

Company Size: 133

teamtechnik GroupIndustrial Automation

Dürr GroupMachinery Manufacturing

DürrMachinery Manufacturing

CogiscanSoftware Development

iTAC Software AGSoftware Development

DUALIS GmbH IT SolutionIT Services and IT Consulting

Ingecal constructeurIndustrial Machinery Manufacturing

HOMAGMachinery Manufacturing

Stiles MachineryMachinery Manufacturing

HOMAG India Pvt. Ltd.Machinery Manufacturing

HOMAG CanadaMachinery Manufacturing

HOMAG Australia Pty LtdMachinery Manufacturing

HOMAG China Machinery Manufacturing

Stiles Machinery - MexicoMachinery Manufacturing

BBS AutomationMachinery Manufacturing

SchenckMachinery Manufacturing

Service@SchenckMaschinenbau

Schenck ServiceCommercial and Industrial Machinery Maintenance

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NEWS

HEKUMA GmbH CyberSecurity News

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

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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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