What is the official website of Aquasis ?

The official website of Aquasis is http://www.aquasis.pt.

What is Aquasis's cybersecurity risk score?

Aquasis has an AI-generated cybersecurity risk score of 750 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has Aquasis experienced?

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

Has Aquasis been affected by any supply chain cyber incidents ?

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

Does Aquasis have SOC 2 Type 1 certification ?

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

Does Aquasis have SOC 2 Type 2 certification ?

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

Does Aquasis comply with GDPR ?

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

Does Aquasis have PCI DSS certification ?

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

Does Aquasis comply with HIPAA ?

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

Does Aquasis have ISO 27001 certification ?

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

Which industry does Aquasis operate in ?

Aquasis operates primarily in the IT Services and IT Consulting industry.

How many employees does Aquasis have ?

Aquasis employs approximately 35 people worldwide.

Does Aquasis own any subsidiaries ?

Aquasis presently has no subsidiaries across any sectors.

How many LinkedIn followers does Aquasis have ?

Aquasis's official LinkedIn profile has approximately 2,506 followers.

What is the NAICS classification code for Aquasis ?

Aquasis is classified under the NAICS code 5415, which corresponds to Computer Systems Design and Related Services.

Does Aquasis have a Crunchbase profile ?

No, Aquasis does not have a profile on Crunchbase.

Does Aquasis have a LinkedIn profile ?

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

How many cybersecurity incidents has Aquasis experienced ?

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

How many peer or competitor companies does Aquasis have ?

Aquasis has an estimated 40,794 peer or competitor companies worldwide.

What types of cybersecurity incidents has Aquasis faced ?

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

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Aquasis

Boost Score +25 with badge (5 left)

750

/1000

Fair

775

/1000

Fair

Aquasis Vendor Cyber Rating & Cyber Score

aquasis.pt

Add Your Compliance Badge

A AQUASIS é a empresa líder, em Portugal, no desenvolvimento e implementação de Sistemas de Informação Geográfica (SIG) para o Sector do Saneamento Básico, desenvolvendo diversos projetos para as principais Entidades Gestoras do sector, empresas do Grupo Águas de Portugal, Concessões, Serviços Municipalizados, Autarquias e outras entidades. A AQUASIS tem por missão o desenvolvimento de tecnologias de informação que visam o apoio à gestão técnica, a exploração e a manutenção dos sistemas de abastecimento de água, de saneamento de águas residuais e de recolha, tratamento e valorização de resíduos sólidos urbanos, promovendo a melhoria da eficiência operacional. As principais características que diferenciam a AQUASIS de outras Empresas de

Aquasis A.I CyberSecurity Scoring

Scoring History

Aquasis

Aquasis CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

Severity

Impact

Seen

Blog Details

Supply Chain Source

Incident Details

View

Aquasis

Breach

11/2022

COL21151222

Aquasis

Breach

3/2022

COL420072725

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A.I.

Aquasis 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 Aquasis

Incidents vs IT Services and IT Consulting Industry Avg (This Year)

No incidents recorded for Aquasis in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for Aquasis in 2026.

Incident Types Aquasis vs IT Services and IT Consulting Industry Avg (This Year)

No incidents recorded for Aquasis in 2026.

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

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SUBSIDIARY

Aquasis Subsidiaries

Aquasis

IT Services and IT Consulting

Website: http://www.aquasis.pt

Company Size: 35

Marubeni CorporationInternational Trade and Development

Maynilad Water Services, Inc.Utilities

BJITOutsourcing and Offshoring Consulting

Marubeni-Komatsu LtdMachinery Manufacturing

Marubeni-Komatsu Used EquipmentConstruction

Komatsu Hybrid Excavator HB215LC-2Construction

SmartestEnergyUtilities

SmartestEnergy UKUtilities

SmartestEnergy AustraliaUtilities

SmartestEnergy BusinessUtilities

SmartestEnergy USUtilities

Orffa Animal Feed Manufacturing

Creekstone Farms Premium BeefFood and Beverage Manufacturing

DASIAviation and Aerospace Component Manufacturing

Zamine PerúMining

Marubeni EuropeInternational Trade and Development

Marubeni Europe Speciality ChemicalsChemical Manufacturing

Marubeni America CorporationFinancial Services

GasvalpoOil and Gas

Columbia Grain InternationalFood and Beverage Manufacturing

Viacore SolutionsMining

Marubeni Brasil S.A.International Trade and Development

Marubeni Citizen-Cincom Inc.Machinery Manufacturing

Magellan Aviation GroupAirlines and Aviation

Magellan ExpendablesAirlines and Aviation

Swissport Japan Ltd.Airlines and Aviation

Power Train Technologies Chile S.A.Mining

Eastern FishFood and Beverage Manufacturing

Marubeni Middle-East and Africa Power LimitedRenewable Energy Semiconductor Manufacturing

MAXIMA MACHINERIES, INC.Wholesale Machinery

Marubeni EuropowerFinancial Services

HyBont Green Hydrogen ProjectRenewable Energy Power Generation

KiwapowerRenewable Energy Semiconductor Manufacturing

Marubeni Power International, Inc.Renewables & Environment

Marubeni Green PowerSolar Electric Power Generation

Marubeni Ventures Inc.Financial Services

DTDS Technology Pte Ltd., Marubeni GroupSemiconductor Manufacturing

DOLBIX CONSULTING Inc.Business Consulting and Services

Aquaman IndustryAtividades dos serviços de tecnologia da informação

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NEWS

Aquasis 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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