What is the official website of HOMAG India Pvt. Ltd. ?

The official website of HOMAG India Pvt. Ltd. is https://indiashop.homag.com/.

What is HOMAG India Pvt. Ltd.'s cybersecurity risk score?

HOMAG India Pvt. Ltd. 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 HOMAG India Pvt. Ltd. experienced?

According to Rankiteo, HOMAG India Pvt. Ltd. currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has HOMAG India Pvt. Ltd. been affected by any supply chain cyber incidents ?

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

Does HOMAG India Pvt. Ltd. have SOC 2 Type 1 certification ?

According to Rankiteo, HOMAG India Pvt. Ltd. is not certified under SOC 2 Type 1.

Does HOMAG India Pvt. Ltd. have SOC 2 Type 2 certification ?

According to Rankiteo, HOMAG India Pvt. Ltd. does not hold a SOC 2 Type 2 certification.

Does HOMAG India Pvt. Ltd. comply with GDPR ?

According to Rankiteo, HOMAG India Pvt. Ltd. is not listed as GDPR compliant.

Does HOMAG India Pvt. Ltd. have PCI DSS certification ?

According to Rankiteo, HOMAG India Pvt. Ltd. does not currently maintain PCI DSS compliance.

Does HOMAG India Pvt. Ltd. comply with HIPAA ?

According to Rankiteo, HOMAG India Pvt. Ltd. is not compliant with HIPAA regulations.

Does HOMAG India Pvt. Ltd. have ISO 27001 certification ?

According to Rankiteo,HOMAG India Pvt. Ltd. is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

Which industry does HOMAG India Pvt. Ltd. operate in ?

HOMAG India Pvt. Ltd. operates primarily in the Machinery Manufacturing industry.

How many employees does HOMAG India Pvt. Ltd. have ?

HOMAG India Pvt. Ltd. employs approximately 112 people worldwide.

Does HOMAG India Pvt. Ltd. own any subsidiaries ?

HOMAG India Pvt. Ltd. presently has no subsidiaries across any sectors.

How many LinkedIn followers does HOMAG India Pvt. Ltd. have ?

HOMAG India Pvt. Ltd.'s official LinkedIn profile has approximately 5,876 followers.

What is the NAICS classification code for HOMAG India Pvt. Ltd. ?

HOMAG India Pvt. Ltd. is classified under the NAICS code 333, which corresponds to Machinery Manufacturing.

Does HOMAG India Pvt. Ltd. have a Crunchbase profile ?

No, HOMAG India Pvt. Ltd. does not have a profile on Crunchbase.

Does HOMAG India Pvt. Ltd. have a LinkedIn profile ?

Yes, HOMAG India Pvt. Ltd. maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/homag-india.

How many cybersecurity incidents has HOMAG India Pvt. Ltd. experienced ?

As of June 23, 2026, Rankiteo reports that HOMAG India Pvt. Ltd. has not experienced any cybersecurity incidents.

How many peer or competitor companies does HOMAG India Pvt. Ltd. have ?

HOMAG India Pvt. Ltd. has an estimated 8,428 peer or competitor companies worldwide.

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HIPL

Boost Score +25 with badge (5 left)

745

/1000

Moderate

770

/1000

Fair

HOMAG India Pvt. Ltd. Vendor Cyber Rating & Cyber Score

homag.com

Add Your Compliance Badge

Everything from a single source The HOMAG Group is the world's leading provider of integrated solutions for production in the woodworking industry and woodworking shops.

HIPL A.I CyberSecurity Scoring

Scoring History

HIPL

HOMAG India Pvt. Ltd. CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

Severity

Impact

Seen

Blog Details

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Incident Details

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No data available

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A.I.

HIPL 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 HIPL

Incidents vs Machinery Manufacturing Industry Avg (This Year)

No incidents recorded for HOMAG India Pvt. Ltd. in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for HOMAG India Pvt. Ltd. in 2026.

Incident Types HIPL vs Machinery Manufacturing Industry Avg (This Year)

No incidents recorded for HOMAG India Pvt. Ltd. in 2026.

Incident History - HIPL (X = Date, Y = Severity)

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SUBSIDIARY

HOMAG India Pvt. Ltd. Subsidiaries

HIPL

Machinery Manufacturing

Website: https://indiashop.homag.com/

Company Size: 112

HOMAGMachinery Manufacturing

Dürr GroupMachinery Manufacturing

DürrMachinery Manufacturing

iTAC Software AGSoftware Development

CogiscanSoftware Development

DUALIS GmbH IT SolutionIT Services and IT Consulting

Ingecal constructeurIndustrial Machinery Manufacturing

BBS AutomationMachinery Manufacturing

SchenckMachinery Manufacturing

Service@SchenckMaschinenbau

Schenck ServiceCommercial and Industrial Machinery Maintenance

teamtechnik GroupIndustrial Automation

HEKUMA GmbHAutomation Machinery Manufacturing

Stiles MachineryMachinery Manufacturing

HOMAG CanadaMachinery Manufacturing

HOMAG Australia Pty LtdMachinery Manufacturing

HOMAG China Machinery Manufacturing

Stiles Machinery - MexicoMachinery Manufacturing

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NEWS

HOMAG India Pvt. Ltd. CyberSecurity News

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

November 21, 2022 03:21 PM

Three Steps to Cyber Protection

How you can reach an effective cybersecurity - with a three-step plan developed by the Charter of Trust.

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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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