What is the official website of MOH_Kenya ?

The official website of MOH_Kenya is https://www.health.go.ke/.

What is MOH_Kenya's cybersecurity risk score?

MOH_Kenya has an AI-generated cybersecurity risk score of 700 out of 1000, indicating a Moderate security posture according to Rankiteo's assessment.

How many security incidents has MOH_Kenya experienced?

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

Has MOH_Kenya been affected by any supply chain cyber incidents ?

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

Does MOH_Kenya have SOC 2 Type 1 certification ?

According to Rankiteo, MOH_Kenya is not certified under SOC 2 Type 1.

Does MOH_Kenya have SOC 2 Type 2 certification ?

According to Rankiteo, MOH_Kenya does not hold a SOC 2 Type 2 certification.

Does MOH_Kenya comply with GDPR ?

According to Rankiteo, MOH_Kenya is not listed as GDPR compliant.

Does MOH_Kenya have PCI DSS certification ?

According to Rankiteo, MOH_Kenya does not currently maintain PCI DSS compliance.

Does MOH_Kenya comply with HIPAA ?

According to Rankiteo, MOH_Kenya is not compliant with HIPAA regulations.

Does MOH_Kenya have ISO 27001 certification ?

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

Which industry does MOH_Kenya operate in ?

MOH_Kenya operates primarily in the Hospitals and Health Care industry.

How many employees does MOH_Kenya have ?

MOH_Kenya employs approximately 74 people worldwide.

Does MOH_Kenya own any subsidiaries ?

MOH_Kenya presently has no subsidiaries across any sectors.

How many LinkedIn followers does MOH_Kenya have ?

MOH_Kenya's official LinkedIn profile has approximately 2,678 followers.

What is the NAICS classification code for MOH_Kenya ?

MOH_Kenya is classified under the NAICS code 62, which corresponds to Health Care and Social Assistance.

Does MOH_Kenya have a Crunchbase profile ?

No, MOH_Kenya does not have a profile on Crunchbase.

Does MOH_Kenya have a LinkedIn profile ?

Yes, MOH_Kenya maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/moh-kenya.

How many cybersecurity incidents has MOH_Kenya experienced ?

As of June 23, 2026, Rankiteo reports that MOH_Kenya has experienced 1 cybersecurity incidents.

How many peer or competitor companies does MOH_Kenya have ?

MOH_Kenya has an estimated 33,111 peer or competitor companies worldwide.

What types of cybersecurity incidents has MOH_Kenya faced ?

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

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MOH_Kenya

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700

/1000

Moderate

725

/1000

Moderate

MOH_Kenya Vendor Cyber Rating & Cyber Score

go.ke

Add Your Compliance Badge

The Ministry of Health (MoH) is the government body whose key mandate is to build a progressive, responsive and sustainable healthcare system for accelerated attainment of the highest standard of health to all Kenyans as enshrined in the Constitution of Kenya 2010. The journey to realizing a healthy, productive and globally competitive nation began in post independent Kenya in 1963 with emphasis on preventive, curative and rehabilitative services. Over the years, the Ministry has embraced several policy changes aimed at achieving equitable, affordable, accessible and quality health care for all. This has been realized through increased investment in human resources for health, service delivery, quality assurance and standards, health

MOH_Kenya A.I CyberSecurity Scoring

Scoring History

MOH_Kenya

MOH_Kenya CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

Severity

Impact

Seen

Blog Details

Supply Chain Source

Incident Details

View

MOH_Kenya

Breach

12/2025

MOH1765541477

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A.I.

MOH_Kenya 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 MOH_Kenya

Incidents vs Hospitals and Health Care Industry Avg (This Year)

No incidents recorded for MOH_Kenya in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for MOH_Kenya in 2026.

Incident Types MOH_Kenya vs Hospitals and Health Care Industry Avg (This Year)

No incidents recorded for MOH_Kenya in 2026.

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

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SUBSIDIARY

MOH_Kenya Subsidiaries

Parent Company

MOH_Kenya

Hospitals and Health Care

Website: https://www.health.go.ke/

Company Size: 10,001+ employees

No subsidiary data available for this company.

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NEWS

MOH_Kenya 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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