What is the official website of Mohawk Builder + Multifamily ?

The official website of Mohawk Builder + Multifamily is https://www.mohawkbuild.com/.

What is Mohawk Builder + Multifamily's cybersecurity risk score?

Mohawk Builder + Multifamily has an AI-generated cybersecurity risk score of 756 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has Mohawk Builder + Multifamily experienced?

According to Rankiteo, Mohawk Builder + Multifamily currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has Mohawk Builder + Multifamily been affected by any supply chain cyber incidents ?

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

Does Mohawk Builder + Multifamily have SOC 2 Type 1 certification ?

According to Rankiteo, Mohawk Builder + Multifamily is not certified under SOC 2 Type 1.

Does Mohawk Builder + Multifamily have SOC 2 Type 2 certification ?

According to Rankiteo, Mohawk Builder + Multifamily does not hold a SOC 2 Type 2 certification.

Does Mohawk Builder + Multifamily comply with GDPR ?

According to Rankiteo, Mohawk Builder + Multifamily is not listed as GDPR compliant.

Does Mohawk Builder + Multifamily have PCI DSS certification ?

According to Rankiteo, Mohawk Builder + Multifamily does not currently maintain PCI DSS compliance.

Does Mohawk Builder + Multifamily comply with HIPAA ?

According to Rankiteo, Mohawk Builder + Multifamily is not compliant with HIPAA regulations.

Does Mohawk Builder + Multifamily have ISO 27001 certification ?

According to Rankiteo,Mohawk Builder + Multifamily is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

Which industry does Mohawk Builder + Multifamily operate in ?

Mohawk Builder + Multifamily operates primarily in the Building Materials industry.

How many employees does Mohawk Builder + Multifamily have ?

Mohawk Builder + Multifamily employs approximately None employees people worldwide.

Does Mohawk Builder + Multifamily own any subsidiaries ?

Mohawk Builder + Multifamily presently has no subsidiaries across any sectors.

How many LinkedIn followers does Mohawk Builder + Multifamily have ?

Mohawk Builder + Multifamily's official LinkedIn profile has approximately 2,356 followers.

Does Mohawk Builder + Multifamily have a Crunchbase profile ?

No, Mohawk Builder + Multifamily does not have a profile on Crunchbase.

Does Mohawk Builder + Multifamily have a LinkedIn profile ?

Yes, Mohawk Builder + Multifamily maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/mohawk-builder-multi-family.

How many cybersecurity incidents has Mohawk Builder + Multifamily experienced ?

As of June 23, 2026, Rankiteo reports that Mohawk Builder + Multifamily has not experienced any cybersecurity incidents.

How many peer or competitor companies does Mohawk Builder + Multifamily have ?

Mohawk Builder + Multifamily has an estimated 1,541 peer or competitor companies worldwide.

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MBM

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756

/1000

Fair

781

/1000

Fair

Mohawk Builder + Multifamily Vendor Cyber Rating & Cyber Score

mohawkbuild.com

Add Your Compliance Badge

As the world’s largest flooring manufacturer, Mohawk has the resources to create product specifically for the builder and multifamily segments. Mohawk offers high-quality broadloom, modular carpets, carpet pad, luxury vinyl flooring, resilient sheet, solid and engineered wood, laminate, tile and countertops all within key price points.

MBM A.I CyberSecurity Scoring

Scoring History

MBM

Mohawk Builder + Multifamily CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

Severity

Impact

Seen

Blog Details

Supply Chain Source

Incident Details

View

No data available

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A.I.

MBM 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 MBM

Incidents vs Building Materials Industry Avg (This Year)

No incidents recorded for Mohawk Builder + Multifamily in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for Mohawk Builder + Multifamily in 2026.

Incident Types MBM vs Building Materials Industry Avg (This Year)

No incidents recorded for Mohawk Builder + Multifamily in 2026.

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

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SUBSIDIARY

Mohawk Builder + Multifamily Subsidiaries

MBM

Building Materials

Website: https://www.mohawkbuild.com/

Company Size: 10,001+ employees

Mohawk IndustriesTextile Manufacturing

Eliane RevestimentosConstruction

Mohawk GroupWholesale Building Materials

Marazzi USAGlass, Ceramics and Concrete Manufacturing

Daltile Architects & DesignersTextile Manufacturing

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Mohawk Builder + Multifamily Similar Companies

ACC Limited

cb acclimited.com

ACC Limited (ACC) is a leading player in the Indian building materials space, with a pan-India operational and marketing presence. It is a member of the Adani Group - the largest and fastest-growing portfolio of diversified sustainable businesses. Synonymous with cement, ACC has established its reputation as a pioneer organisation that has consistently set new benchmarks with its innovative research and product development. With experience and expertise spanning over eight decades, ACC has actively contributed to India’s progress.

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NEWS

Mohawk Builder + Multifamily 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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