What is the official website of Wheels Manufacturing, LLC ?

The official website of Wheels Manufacturing, LLC is http://www.wheelsmfg.com.

What is Wheels Manufacturing, LLC's cybersecurity risk score?

Wheels Manufacturing, LLC has an AI-generated cybersecurity risk score of 741 out of 1000, indicating a Moderate security posture according to Rankiteo's assessment.

How many security incidents has Wheels Manufacturing, LLC experienced?

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

Has Wheels Manufacturing, LLC been affected by any supply chain cyber incidents ?

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

Does Wheels Manufacturing, LLC have SOC 2 Type 1 certification ?

According to Rankiteo, Wheels Manufacturing, LLC is not certified under SOC 2 Type 1.

Does Wheels Manufacturing, LLC have SOC 2 Type 2 certification ?

According to Rankiteo, Wheels Manufacturing, LLC does not hold a SOC 2 Type 2 certification.

Does Wheels Manufacturing, LLC comply with GDPR ?

According to Rankiteo, Wheels Manufacturing, LLC is not listed as GDPR compliant.

Does Wheels Manufacturing, LLC have PCI DSS certification ?

According to Rankiteo, Wheels Manufacturing, LLC does not currently maintain PCI DSS compliance.

Does Wheels Manufacturing, LLC comply with HIPAA ?

According to Rankiteo, Wheels Manufacturing, LLC is not compliant with HIPAA regulations.

Does Wheels Manufacturing, LLC have ISO 27001 certification ?

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

Which industry does Wheels Manufacturing, LLC operate in ?

Wheels Manufacturing, LLC operates primarily in the Wholesale industry.

How many employees does Wheels Manufacturing, LLC have ?

Wheels Manufacturing, LLC employs approximately 8 people worldwide.

Does Wheels Manufacturing, LLC own any subsidiaries ?

Wheels Manufacturing, LLC presently has no subsidiaries across any sectors.

How many LinkedIn followers does Wheels Manufacturing, LLC have ?

Wheels Manufacturing, LLC's official LinkedIn profile has approximately 378 followers.

What is the NAICS classification code for Wheels Manufacturing, LLC ?

Wheels Manufacturing, LLC is classified under the NAICS code 42, which corresponds to Wholesale Trade.

Does Wheels Manufacturing, LLC have a Crunchbase profile ?

No, Wheels Manufacturing, LLC does not have a profile on Crunchbase.

Does Wheels Manufacturing, LLC have a LinkedIn profile ?

Yes, Wheels Manufacturing, LLC maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/wheels-manufacturing.

How many cybersecurity incidents has Wheels Manufacturing, LLC experienced ?

As of June 23, 2026, Rankiteo reports that Wheels Manufacturing, LLC has experienced 1 cybersecurity incidents.

How many peer or competitor companies does Wheels Manufacturing, LLC have ?

Wheels Manufacturing, LLC has an estimated 6,655 peer or competitor companies worldwide.

What types of cybersecurity incidents has Wheels Manufacturing, LLC faced ?

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

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741

/1000

Moderate

766

/1000

Fair

Wheels Manufacturing, LLC Vendor Cyber Rating & Cyber Score

wheelsmfg.com

Add Your Compliance Badge

Welcome to Wheels Manufacturing, one of the world’s largest suppliers of high-quality bicycle derailleur hangers, small parts, repair parts, and specialty tools. We started in 1988 in Boulder, Colorado as a small machine shop producing cog removal tools and chain ring spacers, and quickly grew into a large-scale manufacturing facility producing a full line of small parts totaling over 1000 products. We are now located in Louisville, Colorado about 10 miles from our original location. At Wheels Manufacturing we offer a great place to work. We have about 20-30 hard working employees in our small business environment. We offer a great total rewards package including competitive benefits, generous PTO, 401(k) with company match, bike and bike

WML A.I CyberSecurity Scoring

Scoring History

WML

Wheels Manufacturing, LLC CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

Severity

Impact

Seen

Blog Details

Supply Chain Source

Incident Details

View

Wheels Manufacturin...

Cyber Attack

10/2024

WHE344072925

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A.I.

WML 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

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Underwriter Stats for WML

Incidents vs Wholesale Industry Avg (This Year)

No incidents recorded for Wheels Manufacturing, LLC in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for Wheels Manufacturing, LLC in 2026.

Incident Types WML vs Wholesale Industry Avg (This Year)

No incidents recorded for Wheels Manufacturing, LLC in 2026.

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

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SUBSIDIARY

Wheels Manufacturing, LLC Subsidiaries

Parent Company

WML

Wholesale

Website: http://www.wheelsmfg.com

Company Size: 8

No subsidiary data available for this company.

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NEWS

Wheels Manufacturing, LLC 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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