What is the official website of ~Pourri ?

The official website of ~Pourri is http://www.pourri.com.

What is ~Pourri's cybersecurity risk score?

~Pourri has an AI-generated cybersecurity risk score of 755 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has ~Pourri experienced?

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

Has ~Pourri been affected by any supply chain cyber incidents ?

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

Does ~Pourri have SOC 2 Type 1 certification ?

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

Does ~Pourri have SOC 2 Type 2 certification ?

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

Does ~Pourri comply with GDPR ?

According to Rankiteo, ~Pourri is not listed as GDPR compliant.

Does ~Pourri have PCI DSS certification ?

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

Does ~Pourri comply with HIPAA ?

According to Rankiteo, ~Pourri is not compliant with HIPAA regulations.

Does ~Pourri have ISO 27001 certification ?

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

Which industry does ~Pourri operate in ?

~Pourri operates primarily in the Consumer Goods industry.

How many employees does ~Pourri have ?

~Pourri employs approximately 111 people worldwide.

Does ~Pourri own any subsidiaries ?

~Pourri presently has no subsidiaries across any sectors.

How many LinkedIn followers does ~Pourri have ?

~Pourri's official LinkedIn profile has approximately 16,769 followers.

Does ~Pourri have a Crunchbase profile ?

No, ~Pourri does not have a profile on Crunchbase.

Does ~Pourri have a LinkedIn profile ?

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

How many cybersecurity incidents has ~Pourri experienced ?

As of June 23, 2026, Rankiteo reports that ~Pourri has not experienced any cybersecurity incidents.

How many peer or competitor companies does ~Pourri have ?

~Pourri has an estimated 1,648 peer or competitor companies worldwide.

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~Pourri

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755

/1000

Fair

780

/1000

Fair

~Pourri Vendor Cyber Rating & Cyber Score

pourri.com

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Founded by entrepreneur Suzy Batiz in 2007 under the name Poo~Pourri, ~Pourri is a company that transforms life's stinkiest moments and turns them into something magically fresh. From your home, bathroom and beyond, ~Pourri ensures all aspects of life smell amazing and clean—without the funky ingredients or stigmas. All ~Pourri products are formulated with essential oils, plant-based ingredients, and our Proprietary Funk Lock™ Technology, and made to be household, earth, pet, and family-friendly.

~Pourri A.I CyberSecurity Scoring

Scoring History

~Pourri

~Pourri 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.

~Pourri 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 ~Pourri

Incidents vs Consumer Goods Industry Avg (This Year)

No incidents recorded for ~Pourri in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for ~Pourri in 2026.

Incident Types ~Pourri vs Consumer Goods Industry Avg (This Year)

No incidents recorded for ~Pourri in 2026.

Incident History - ~Pourri (X = Date, Y = Severity)

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SUBSIDIARY

~Pourri Subsidiaries

Parent Company

~Pourri

Consumer Goods

Website: http://www.pourri.com

Company Size: 51-200 employees

No subsidiary data available for this company.

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~Pourri Similar Companies

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NEWS

~Pourri CyberSecurity News

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

October 20, 2022 07:00 AM

Grinding Off Ryzen 7000 IHS Seemingly Lowers Temps By 10 Degrees Celsius

A bit less risky method is to reduce the heat spreader's thickness by grinding it down or, in other words, lapping the CPU.

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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