What is the official website of Canyon Pipeline Construction, Inc. ?

The official website of Canyon Pipeline Construction, Inc. is http://www.CanyonPipeline.com.

What is Canyon Pipeline Construction, Inc.'s cybersecurity risk score?

Canyon Pipeline Construction, Inc. has an AI-generated cybersecurity risk score of 751 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has Canyon Pipeline Construction, Inc. experienced?

According to Rankiteo, Canyon Pipeline Construction, Inc. currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has Canyon Pipeline Construction, Inc. been affected by any supply chain cyber incidents ?

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

Does Canyon Pipeline Construction, Inc. have SOC 2 Type 1 certification ?

According to Rankiteo, Canyon Pipeline Construction, Inc. is not certified under SOC 2 Type 1.

Does Canyon Pipeline Construction, Inc. have SOC 2 Type 2 certification ?

According to Rankiteo, Canyon Pipeline Construction, Inc. does not hold a SOC 2 Type 2 certification.

Does Canyon Pipeline Construction, Inc. comply with GDPR ?

According to Rankiteo, Canyon Pipeline Construction, Inc. is not listed as GDPR compliant.

Does Canyon Pipeline Construction, Inc. have PCI DSS certification ?

According to Rankiteo, Canyon Pipeline Construction, Inc. does not currently maintain PCI DSS compliance.

Does Canyon Pipeline Construction, Inc. comply with HIPAA ?

According to Rankiteo, Canyon Pipeline Construction, Inc. is not compliant with HIPAA regulations.

Does Canyon Pipeline Construction, Inc. have ISO 27001 certification ?

According to Rankiteo,Canyon Pipeline Construction, Inc. is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

Which industry does Canyon Pipeline Construction, Inc. operate in ?

Canyon Pipeline Construction, Inc. operates primarily in the Construction industry.

How many employees does Canyon Pipeline Construction, Inc. have ?

Canyon Pipeline Construction, Inc. employs approximately 76 people worldwide.

Does Canyon Pipeline Construction, Inc. own any subsidiaries ?

Canyon Pipeline Construction, Inc. presently has no subsidiaries across any sectors.

How many LinkedIn followers does Canyon Pipeline Construction, Inc. have ?

Canyon Pipeline Construction, Inc.'s official LinkedIn profile has approximately 2,262 followers.

Does Canyon Pipeline Construction, Inc. have a Crunchbase profile ?

No, Canyon Pipeline Construction, Inc. does not have a profile on Crunchbase.

Does Canyon Pipeline Construction, Inc. have a LinkedIn profile ?

Yes, Canyon Pipeline Construction, Inc. maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/canyon-pipelines-construction-inc-.

How many cybersecurity incidents has Canyon Pipeline Construction, Inc. experienced ?

As of June 23, 2026, Rankiteo reports that Canyon Pipeline Construction, Inc. has not experienced any cybersecurity incidents.

How many peer or competitor companies does Canyon Pipeline Construction, Inc. have ?

Canyon Pipeline Construction, Inc. has an estimated 39,770 peer or competitor companies worldwide.

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CPCI

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751

/1000

Fair

776

/1000

Fair

Canyon Pipeline Construction, Inc. Vendor Cyber Rating & Cyber Score

canyonpipeline.com

Add Your Compliance Badge

At Canyon, we take pride in our service to our customers and our communities. We realize the work we do matters to those we serve. By providing safe gas distribution and infrastructure construction services we’re able to help strengthen communities and improve the lives of those around us. It’s a calling we take very seriously and we will always step-up to meet the challenges put before us in a safe, reliable, timely and cost effective manner.

CPCI A.I CyberSecurity Scoring

Scoring History

CPCI

Canyon Pipeline Construction, Inc. CyberSecurity News & History

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A.I.

CPCI Company Scoring based on AI Models

Cyber Incidents Likelihood 3 - 6 - 9 months

Actual

Prediction

Incident Predictions locked

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

Actual

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Prediction D

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Underwriter Stats for CPCI

Incidents vs Construction Industry Avg (This Year)

No incidents recorded for Canyon Pipeline Construction, Inc. in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for Canyon Pipeline Construction, Inc. in 2026.

Incident Types CPCI vs Construction Industry Avg (This Year)

No incidents recorded for Canyon Pipeline Construction, Inc. in 2026.

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

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SUBSIDIARY

Canyon Pipeline Construction, Inc. Subsidiaries

CPCI

Construction

Website: http://www.CanyonPipeline.com

Company Size: 76

Centuri Group, Inc.Construction

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VINCI Construction

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Canyon Pipeline Construction, Inc. 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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