What is the official website of UMS Banking, Payment Processing Services ?

The official website of UMS Banking, Payment Processing Services is http://www.umsbanking.com.

What is UMS Banking, Payment Processing Services's cybersecurity risk score?

UMS Banking, Payment Processing Services has an AI-generated cybersecurity risk score of 753 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has UMS Banking, Payment Processing Services experienced?

According to Rankiteo, UMS Banking, Payment Processing Services currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has UMS Banking, Payment Processing Services been affected by any supply chain cyber incidents ?

According to Rankiteo, UMS Banking, Payment Processing Services has not been affected by any supply chain cyber incidents, and no incident IDs are currently listed for the organization.

Does UMS Banking, Payment Processing Services have SOC 2 Type 1 certification ?

According to Rankiteo, UMS Banking, Payment Processing Services is not certified under SOC 2 Type 1.

Does UMS Banking, Payment Processing Services have SOC 2 Type 2 certification ?

According to Rankiteo, UMS Banking, Payment Processing Services does not hold a SOC 2 Type 2 certification.

Does UMS Banking, Payment Processing Services comply with GDPR ?

According to Rankiteo, UMS Banking, Payment Processing Services is not listed as GDPR compliant.

Does UMS Banking, Payment Processing Services have PCI DSS certification ?

According to Rankiteo, UMS Banking, Payment Processing Services does not currently maintain PCI DSS compliance.

Does UMS Banking, Payment Processing Services comply with HIPAA ?

According to Rankiteo, UMS Banking, Payment Processing Services is not compliant with HIPAA regulations.

Does UMS Banking, Payment Processing Services have ISO 27001 certification ?

According to Rankiteo,UMS Banking, Payment Processing Services is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

Which industry does UMS Banking, Payment Processing Services operate in ?

UMS Banking, Payment Processing Services operates primarily in the Financial Services industry.

How many employees does UMS Banking, Payment Processing Services have ?

UMS Banking, Payment Processing Services employs approximately 16 people worldwide.

Does UMS Banking, Payment Processing Services own any subsidiaries ?

UMS Banking, Payment Processing Services presently has no subsidiaries across any sectors.

How many LinkedIn followers does UMS Banking, Payment Processing Services have ?

UMS Banking, Payment Processing Services's official LinkedIn profile has approximately 481 followers.

Does UMS Banking, Payment Processing Services have a Crunchbase profile ?

No, UMS Banking, Payment Processing Services does not have a profile on Crunchbase.

Does UMS Banking, Payment Processing Services have a LinkedIn profile ?

Yes, UMS Banking, Payment Processing Services maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/ums-banking.

How many cybersecurity incidents has UMS Banking, Payment Processing Services experienced ?

As of June 23, 2026, Rankiteo reports that UMS Banking, Payment Processing Services has not experienced any cybersecurity incidents.

How many peer or competitor companies does UMS Banking, Payment Processing Services have ?

UMS Banking, Payment Processing Services has an estimated 32,126 peer or competitor companies worldwide.

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753

/1000

Fair

778

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Fair

UMS Banking, Payment Processing Services Vendor Cyber Rating & Cyber Score

umsbanking.com

Add Your Compliance Badge

UMS Banking has been partnering with Financial Institutions and other strategic partners all across the US to provide the latest payments technology and products for over 30 years. We understand the high level of service required by Community Banks and work to be an extension of the bank in order to provide the best solution to their customers. With our partners we drive integrated financial services, software solutions, business management tools, and intelligent dashboards to help business clients succeed in today's market. UMS Banking has offices in the Los Angeles, Dallas, and Nashville areas along with talented employees all across the US in order to best service our partners and clients alike. Contact us to learn more about how UMS

UBPPS A.I CyberSecurity Scoring

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UBPPS

UMS Banking, Payment Processing Services CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

Severity

Impact

Seen

Blog Details

Supply Chain Source

Incident Details

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No data available

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A.I.

UBPPS 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 UBPPS

Incidents vs Financial Services Industry Avg (This Year)

No incidents recorded for UMS Banking, Payment Processing Services in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for UMS Banking, Payment Processing Services in 2026.

Incident Types UBPPS vs Financial Services Industry Avg (This Year)

No incidents recorded for UMS Banking, Payment Processing Services in 2026.

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

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SUBSIDIARY

UMS Banking, Payment Processing Services Subsidiaries

Parent Company

UBPPS

Financial Services

Website: http://www.umsbanking.com

Company Size: 16

No subsidiary data available for this company.

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NEWS

UMS Banking, Payment Processing Services CyberSecurity News

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

February 12, 2019 08:00 AM

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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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