What is the official website of Nippon Paint India ?

The official website of Nippon Paint India is https://www.nipponpaint.co.in/.

What is Nippon Paint India's cybersecurity risk score?

Nippon Paint India has an AI-generated cybersecurity risk score of 765 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has Nippon Paint India experienced?

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

Has Nippon Paint India been affected by any supply chain cyber incidents ?

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

Does Nippon Paint India have SOC 2 Type 1 certification ?

According to Rankiteo, Nippon Paint India is not certified under SOC 2 Type 1.

Does Nippon Paint India have SOC 2 Type 2 certification ?

According to Rankiteo, Nippon Paint India does not hold a SOC 2 Type 2 certification.

Does Nippon Paint India comply with GDPR ?

According to Rankiteo, Nippon Paint India is not listed as GDPR compliant.

Does Nippon Paint India have PCI DSS certification ?

According to Rankiteo, Nippon Paint India does not currently maintain PCI DSS compliance.

Does Nippon Paint India comply with HIPAA ?

According to Rankiteo, Nippon Paint India is not compliant with HIPAA regulations.

Does Nippon Paint India have ISO 27001 certification ?

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

Which industry does Nippon Paint India operate in ?

Nippon Paint India operates primarily in the Chemical Manufacturing industry.

How many employees does Nippon Paint India have ?

Nippon Paint India employs approximately 0 people worldwide.

Does Nippon Paint India own any subsidiaries ?

Nippon Paint India presently has no subsidiaries across any sectors.

How many LinkedIn followers does Nippon Paint India have ?

Nippon Paint India's official LinkedIn profile has approximately 72,800 followers.

Does Nippon Paint India have a Crunchbase profile ?

No, Nippon Paint India does not have a profile on Crunchbase.

Does Nippon Paint India have a LinkedIn profile ?

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

How many cybersecurity incidents has Nippon Paint India experienced ?

As of June 23, 2026, Rankiteo reports that Nippon Paint India has not experienced any cybersecurity incidents.

How many peer or competitor companies does Nippon Paint India have ?

Nippon Paint India has an estimated 4,390 peer or competitor companies worldwide.

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765

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Fair

790

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Nippon Paint India Vendor Cyber Rating & Cyber Score

nipponpaint.co.in

Add Your Compliance Badge

Welcome to the official LinkedIn page of Nippon Paint (India) Private Limited. Nippon Paint, founded in Japan has over 143 years of experience and is the No.1 paint manufacturer in Asia Pacific, and among the leading paint manufacturers of the world. Nippon Paint produces high-quality paints and coats for decorative, industrial and automotive sectors. Over the years, Nippon Paint has perfected its products by means of breakthrough paint technology, with an emphasis on innovation and eco-friendliness. Nippon Paint’s mission is to create advanced products that boost quality of life while protecting the environment. Through innovative technology, Nippon Paint benefits its customers and society. The company and its subsidiaries has presence

NPI A.I CyberSecurity Scoring

Scoring History

NPI

Nippon Paint India CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

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Impact

Seen

Blog Details

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Incident Details

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A.I.

NPI 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

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Prediction B

Prediction C

Prediction D

A.I. Risk Score Predictions locked

Access Monitoring Plan

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Underwriter Stats for NPI

Incidents vs Chemical Manufacturing Industry Avg (This Year)

No incidents recorded for Nippon Paint India in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for Nippon Paint India in 2026.

Incident Types NPI vs Chemical Manufacturing Industry Avg (This Year)

No incidents recorded for Nippon Paint India in 2026.

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

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SUBSIDIARY

Nippon Paint India Subsidiaries

NPI

Chemical Manufacturing

Website: https://www.nipponpaint.co.in/

Company Size: 0

NIPSEA Group I Nippon PaintChemical Manufacturing

Betek Boya ve Kimya Sanayi A.Ş. (Nippon Paint- Filli Boya - Fawori - Tempo - Dalmaçyalı)Chemical Manufacturing

Nippon Paint Industrial GroupChemical Manufacturing

Nippon Paint MalaysiaConstruction

Nippon Paint MarineChemical Manufacturing

Nippon Paint Marine | Europe, Turkey & Middle EastChemical Manufacturing

PT.Nipsea paint and ChemicalsChemical Manufacturing

Nippon Paint Coatings PhilippinesChemical Manufacturing

Nippon Paint IndonesiaChemical Manufacturing

Nippon Paint BangladeshChemical Manufacturing

Nippon Paint VietnamChemical Manufacturing

V-tech MalaysiaChemical Manufacturing

Nippon Paint MyanmarChemical Manufacturing

Nippon Paint TaiwanWholesale Building Materials

Nippon Paint Hong KongManufacturing

Nippon Paint Czech Republic | AutomotiveChemical Manufacturing

Nippon Paint SingaporeConsumer Goods

AYDA International AwardsDesign Services

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NEWS

Nippon Paint India 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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