What is the official website of Yıldız Holding ?

The official website of Yıldız Holding is http://www.yildizholding.com.tr.

What is Yıldız Holding's cybersecurity risk score?

Yıldız Holding has an AI-generated cybersecurity risk score of 796 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has Yıldız Holding experienced?

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

Has Yıldız Holding been affected by any supply chain cyber incidents ?

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

Does Yıldız Holding have SOC 2 Type 1 certification ?

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

Does Yıldız Holding have SOC 2 Type 2 certification ?

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

Does Yıldız Holding comply with GDPR ?

According to Rankiteo, Yıldız Holding is not listed as GDPR compliant.

Does Yıldız Holding have PCI DSS certification ?

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

Does Yıldız Holding comply with HIPAA ?

According to Rankiteo, Yıldız Holding is not compliant with HIPAA regulations.

Does Yıldız Holding have ISO 27001 certification ?

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

Which industry does Yıldız Holding operate in ?

Yıldız Holding operates primarily in the Manufacturing industry.

How many employees does Yıldız Holding have ?

Yıldız Holding employs approximately 19,803 people worldwide.

Does Yıldız Holding own any subsidiaries ?

Yıldız Holding presently has no subsidiaries across any sectors.

How many LinkedIn followers does Yıldız Holding have ?

Yıldız Holding's official LinkedIn profile has approximately 430,311 followers.

What is the NAICS classification code for Yıldız Holding ?

Yıldız Holding is classified under the NAICS code 30, which corresponds to Manufacturing.

Does Yıldız Holding have a Crunchbase profile ?

No, Yıldız Holding does not have a profile on Crunchbase.

Does Yıldız Holding have a LinkedIn profile ?

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

How many cybersecurity incidents has Yıldız Holding experienced ?

As of June 23, 2026, Rankiteo reports that Yıldız Holding has experienced 2 cybersecurity incidents.

How many peer or competitor companies does Yıldız Holding have ?

Yıldız Holding has an estimated 8,152 peer or competitor companies worldwide.

What types of cybersecurity incidents has Yıldız Holding faced ?

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

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Yıldız Holding

Boost Score +25 with badge (5 left)

796

/1000

Fair

821

/1000

Good

Yıldız Holding Vendor Cyber Rating & Cyber Score

yildizholding.com.tr

Add Your Compliance Badge

Yıldız Holding’in 1944 yılında İstanbul’da bisküvi üretimiyle başlayan hikâyesi, bugün 300’den fazla markayla 5 kıtada 4 milyar insana ulaştırdığımız gıda-atıştırmalık ürünlerimizle ve Türkiye’nin her noktasındaki perakende şirketlerimizle devam ediyor. Kökleri Türkiye’den beslenen, dalları ise dünyanın farklı coğrafyalarına uzanan global bir yapı içinde ülkemizi uluslararası arenada temsil ediyor, Türkiye’nin “Yıldız”ı olmanın mutluluğunu yaşıyoruz. Çoğunluğu Türkiye’de bulunan 80 bin çalışanımızla durmaksızın daha iyiye ulaşmayı amaçlıyor, 20’si yurt dışında olmak üzere toplam 45 fabrikamızda bisküviden çikolataya, dondurulmuş gıdadan ambalaja kadar geniş bir yelpazede üretim yapıyoruz. Ekonomik katkıda, istihdamda, ihracatta,

Yıldız Holding A.I CyberSecurity Scoring

Scoring History

Yıldız Holding

Yıldız Holding CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

Severity

Impact

Seen

Blog Details

Supply Chain Source

Incident Details

View

Yıldız Holding

Breach

10/2014

GOD857072825

Yıldız Holding

Breach

4/2013

GOD229080425

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A.I.

Yıldız Holding 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 Yıldız Holding

Incidents vs Manufacturing Industry Avg (This Year)

No incidents recorded for Yıldız Holding in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for Yıldız Holding in 2026.

Incident Types Yıldız Holding vs Manufacturing Industry Avg (This Year)

No incidents recorded for Yıldız Holding in 2026.

Incident History - Yıldız Holding (X = Date, Y = Severity)

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SUBSIDIARY

Yıldız Holding Subsidiaries

Yıldız Holding

Manufacturing

Website: http://www.yildizholding.com.tr

Company Size: 10,001+ employees

Godiva ChocolatierFood and Beverage Services

Godiva TürkiyeFood and Beverage Services

Şok MarketlerRetail

Bizim Toptan Satış MağazalarıRetail

Penta TeknolojiIT Services and IT Consulting

Autodesk by Penta TeknolojiTechnology, Information and Internet

Adobe by Penta TeknolojiInformation Services

Logitech Video Konferans (by Penta Teknoloji)Information Services

g2m Dağıtım Pazarlama ve Tic. A.Ş.Food and Beverage Services

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NEWS

Yıldız Holding CyberSecurity News

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

July 27, 2016 04:41 AM

Murat Ulker

Murat Ulker owns 63% of Yildiz Holding, which produces a wide range of food products and non-alcoholic beverages. Yildiz spent $850 million to buy Belgian...

Read Article

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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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