What is the official website of Four Corners Brewing Co. ?

The official website of Four Corners Brewing Co. is http://www.fcbrewing.com.

What is Four Corners Brewing Co.'s cybersecurity risk score?

Four Corners Brewing Co. has an AI-generated cybersecurity risk score of 760 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has Four Corners Brewing Co. experienced?

According to Rankiteo, Four Corners Brewing Co. currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has Four Corners Brewing Co. been affected by any supply chain cyber incidents ?

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

Does Four Corners Brewing Co. have SOC 2 Type 1 certification ?

According to Rankiteo, Four Corners Brewing Co. is not certified under SOC 2 Type 1.

Does Four Corners Brewing Co. have SOC 2 Type 2 certification ?

According to Rankiteo, Four Corners Brewing Co. does not hold a SOC 2 Type 2 certification.

Does Four Corners Brewing Co. comply with GDPR ?

According to Rankiteo, Four Corners Brewing Co. is not listed as GDPR compliant.

Does Four Corners Brewing Co. have PCI DSS certification ?

According to Rankiteo, Four Corners Brewing Co. does not currently maintain PCI DSS compliance.

Does Four Corners Brewing Co. comply with HIPAA ?

According to Rankiteo, Four Corners Brewing Co. is not compliant with HIPAA regulations.

Does Four Corners Brewing Co. have ISO 27001 certification ?

According to Rankiteo,Four Corners Brewing Co. is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

Which industry does Four Corners Brewing Co. operate in ?

Four Corners Brewing Co. operates primarily in the Food and Beverage Services industry.

How many employees does Four Corners Brewing Co. have ?

Four Corners Brewing Co. employs approximately 16 people worldwide.

Does Four Corners Brewing Co. own any subsidiaries ?

Four Corners Brewing Co. presently has no subsidiaries across any sectors.

How many LinkedIn followers does Four Corners Brewing Co. have ?

Four Corners Brewing Co.'s official LinkedIn profile has approximately 1,106 followers.

What is the NAICS classification code for Four Corners Brewing Co. ?

Four Corners Brewing Co. is classified under the NAICS code 722, which corresponds to Food Services and Drinking Places.

Does Four Corners Brewing Co. have a Crunchbase profile ?

No, Four Corners Brewing Co. does not have a profile on Crunchbase.

Does Four Corners Brewing Co. have a LinkedIn profile ?

Yes, Four Corners Brewing Co. maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/four-corners-brewing-co-.

How many cybersecurity incidents has Four Corners Brewing Co. experienced ?

As of June 23, 2026, Rankiteo reports that Four Corners Brewing Co. has not experienced any cybersecurity incidents.

How many peer or competitor companies does Four Corners Brewing Co. have ?

Four Corners Brewing Co. has an estimated 8,737 peer or competitor companies worldwide.

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Four Corners Brewing Co. Vendor Cyber Rating & Cyber Score

fcbrewing.com

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Perched just south of downtown Dallas is Four Corners Brewing Co. The brewery was founded with the common goal of elevating the beer culture in our hometown. We love craft beer, and we love our city. Four Corners is a place where local flavor and craft brewing come together. We’re inspired by the vibrant soul of Dallas and express ourselves through our brews, design, and experiences. Four Corners is a place for celebration. We’re proud to offer a unique place where friends and families can come together and share our passion for brewing. Saludos!

FCBC A.I CyberSecurity Scoring

Scoring History

FCBC

Four Corners Brewing Co. CyberSecurity News & History

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A.I.

FCBC Company Scoring based on AI Models

Cyber Incidents Likelihood 3 - 6 - 9 months

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Prediction

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

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Underwriter Stats for FCBC

Incidents vs Food and Beverage Services Industry Avg (This Year)

No incidents recorded for Four Corners Brewing Co. in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for Four Corners Brewing Co. in 2026.

Incident Types FCBC vs Food and Beverage Services Industry Avg (This Year)

No incidents recorded for Four Corners Brewing Co. in 2026.

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

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SUBSIDIARY

Four Corners Brewing Co. Subsidiaries

FCBC

Food and Beverage Services

Website: http://www.fcbrewing.com

Company Size: 16

Constellation BrandsFood and Beverage Services

Ruffino 1877Wineries

Constellation Brands New Zealand & AustraliaBeverage Manufacturing

High West DistilleryManufacturing

Crown Imports, LLCBeverage Manufacturing

Funky Buddha BreweryFood and Beverage Services

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NEWS

Four Corners Brewing Co. 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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