What is the official website of Fuse - Edital de Inovação Rumo ?

The official website of Fuse - Edital de Inovação Rumo is http://projetofuse.com/.

What is Fuse - Edital de Inovação Rumo's cybersecurity risk score?

Fuse - Edital de Inovação Rumo has an AI-generated cybersecurity risk score of 766 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has Fuse - Edital de Inovação Rumo experienced?

According to Rankiteo, Fuse - Edital de Inovação Rumo currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has Fuse - Edital de Inovação Rumo been affected by any supply chain cyber incidents ?

According to Rankiteo, Fuse - Edital de Inovação Rumo has not been affected by any supply chain cyber incidents, and no incident IDs are currently listed for the organization.

Does Fuse - Edital de Inovação Rumo have SOC 2 Type 1 certification ?

According to Rankiteo, Fuse - Edital de Inovação Rumo is not certified under SOC 2 Type 1.

Does Fuse - Edital de Inovação Rumo have SOC 2 Type 2 certification ?

According to Rankiteo, Fuse - Edital de Inovação Rumo does not hold a SOC 2 Type 2 certification.

Does Fuse - Edital de Inovação Rumo comply with GDPR ?

According to Rankiteo, Fuse - Edital de Inovação Rumo is not listed as GDPR compliant.

Does Fuse - Edital de Inovação Rumo have PCI DSS certification ?

According to Rankiteo, Fuse - Edital de Inovação Rumo does not currently maintain PCI DSS compliance.

Does Fuse - Edital de Inovação Rumo comply with HIPAA ?

According to Rankiteo, Fuse - Edital de Inovação Rumo is not compliant with HIPAA regulations.

Does Fuse - Edital de Inovação Rumo have ISO 27001 certification ?

According to Rankiteo,Fuse - Edital de Inovação Rumo is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

Which industry does Fuse - Edital de Inovação Rumo operate in ?

Fuse - Edital de Inovação Rumo operates primarily in the Truck Transportation industry.

How many employees does Fuse - Edital de Inovação Rumo have ?

Fuse - Edital de Inovação Rumo employs approximately None employees people worldwide.

Does Fuse - Edital de Inovação Rumo own any subsidiaries ?

Fuse - Edital de Inovação Rumo presently has no subsidiaries across any sectors.

How many LinkedIn followers does Fuse - Edital de Inovação Rumo have ?

Fuse - Edital de Inovação Rumo's official LinkedIn profile has approximately followers.

What is the NAICS classification code for Fuse - Edital de Inovação Rumo ?

Fuse - Edital de Inovação Rumo is classified under the NAICS code 484, which corresponds to Truck Transportation.

Does Fuse - Edital de Inovação Rumo have a Crunchbase profile ?

No, Fuse - Edital de Inovação Rumo does not have a profile on Crunchbase.

Does Fuse - Edital de Inovação Rumo have a LinkedIn profile ?

Yes, Fuse - Edital de Inovação Rumo maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/projetofuse.

How many cybersecurity incidents has Fuse - Edital de Inovação Rumo experienced ?

As of June 23, 2026, Rankiteo reports that Fuse - Edital de Inovação Rumo has not experienced any cybersecurity incidents.

How many peer or competitor companies does Fuse - Edital de Inovação Rumo have ?

Fuse - Edital de Inovação Rumo has an estimated 5,672 peer or competitor companies worldwide.

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766

/1000

Fair

791

/1000

Fair

Fuse - Edital de Inovação Rumo Vendor Cyber Rating & Cyber Score

projetofuse.com

Add Your Compliance Badge

O desafio global da logística, a cada dia, vem encontrando na sociedade exemplos de soluções que transformam a vida das pessoas. Dentro deste cenário, o Fuse, um edital de aceleração de ideias, está abrindo espaço para você se tornar o agente dessa transformação. Recebendo suporte da Rumo, maior operadora do segmento ferroviário da América Latina, o Fuse busca soluções inovadoras para a segurança ferroviária, principal valor da Companhia.

FEDIR A.I CyberSecurity Scoring

Scoring History

FEDIR

Fuse - Edital de Inovação Rumo 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.

FEDIR 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 FEDIR

Incidents vs Truck Transportation Industry Avg (This Year)

No incidents recorded for Fuse - Edital de Inovação Rumo in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for Fuse - Edital de Inovação Rumo in 2026.

Incident Types FEDIR vs Truck Transportation Industry Avg (This Year)

No incidents recorded for Fuse - Edital de Inovação Rumo in 2026.

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

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SUBSIDIARY

Fuse - Edital de Inovação Rumo Subsidiaries

FEDIR

Truck Transportation

Website: http://projetofuse.com/

Company Size: 5,001-10,000 employees

RumoTransportation, Logistics, Supply Chain and Storage

CosanInvestment Management

RaízenRetail Gasoline

Raízen PowerServiços para energia renovável

ComgasOil and Gas

MooveChemical Manufacturing

Mobil by MooveOil and Gas

TirrenoChemical Manufacturing

PetroChoice - Lubrication SolutionsOil and Gas

Moove EuropeOil and Gas

Moove UKOil and Gas

Comma OilOil and Gas

Moove FranceWholesale

Moove PortugalOil and Gas

WP GroupOil and Gas

Moove EspañaOil and Gas

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NEWS

Fuse - Edital de Inovação Rumo 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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