What is WAVIA Professional Services (Formerly Ignite Alliance)'s cybersecurity risk score?

WAVIA Professional Services (Formerly Ignite Alliance) has an AI-generated cybersecurity risk score of 749 out of 1000, indicating a Moderate security posture according to Rankiteo's assessment.

How many security incidents has WAVIA Professional Services (Formerly Ignite Alliance) experienced?

According to Rankiteo, WAVIA Professional Services (Formerly Ignite Alliance) currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has WAVIA Professional Services (Formerly Ignite Alliance) been affected by any supply chain cyber incidents ?

According to Rankiteo, WAVIA Professional Services (Formerly Ignite Alliance) has not been affected by any supply chain cyber incidents, and no incident IDs are currently listed for the organization.

Does WAVIA Professional Services (Formerly Ignite Alliance) have SOC 2 Type 2 certification ?

According to Rankiteo, WAVIA Professional Services (Formerly Ignite Alliance) does not hold a SOC 2 Type 2 certification.

Does WAVIA Professional Services (Formerly Ignite Alliance) have PCI DSS certification ?

According to Rankiteo, WAVIA Professional Services (Formerly Ignite Alliance) does not currently maintain PCI DSS compliance.

Does WAVIA Professional Services (Formerly Ignite Alliance) comply with HIPAA ?

According to Rankiteo, WAVIA Professional Services (Formerly Ignite Alliance) is not compliant with HIPAA regulations.

Does WAVIA Professional Services (Formerly Ignite Alliance) have ISO 27001 certification ?

According to Rankiteo,WAVIA Professional Services (Formerly Ignite Alliance) is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

How many employees does WAVIA Professional Services (Formerly Ignite Alliance) have ?

WAVIA Professional Services (Formerly Ignite Alliance) employs approximately 4 people worldwide.

Does WAVIA Professional Services (Formerly Ignite Alliance) own any subsidiaries ?

WAVIA Professional Services (Formerly Ignite Alliance) presently has no subsidiaries across any sectors.

How many LinkedIn followers does WAVIA Professional Services (Formerly Ignite Alliance) have ?

WAVIA Professional Services (Formerly Ignite Alliance)'s official LinkedIn profile has approximately 255 followers.

Does WAVIA Professional Services (Formerly Ignite Alliance) have a Crunchbase profile ?

No, WAVIA Professional Services (Formerly Ignite Alliance) does not have a profile on Crunchbase.

Does WAVIA Professional Services (Formerly Ignite Alliance) have a LinkedIn profile ?

Yes, WAVIA Professional Services (Formerly Ignite Alliance) maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/ignite-alliance.

How many cybersecurity incidents has WAVIA Professional Services (Formerly Ignite Alliance) experienced ?

As of June 23, 2026, Rankiteo reports that WAVIA Professional Services (Formerly Ignite Alliance) has not experienced any cybersecurity incidents.

How many peer or competitor companies does WAVIA Professional Services (Formerly Ignite Alliance) have ?

WAVIA Professional Services (Formerly Ignite Alliance) has an estimated 19,483 peer or competitor companies worldwide.

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749

/1000

Moderate

774

/1000

Fair

WAVIA Professional Services (Formerly Ignite Alliance) Vendor Cyber Rating & Cyber Score

ignitealliance.com.au

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We provide aspirational business owners with high-level advice, actionable tools and resources so they can do what they do best. How We Create Positive Impact: Ignite now has several business arms which operate by bringing a pre-formed management team to small and medium businesses. The services include high level consulting with strategy and implementation plans, finance team services (CFO, management accounting, and tax compliance), innovation services (R&D and EMDG), private wealth, mortgage broking, and an incubator support program that provides angel investment for exponential technologies that will have a positive impact on the world. We are also proudly the licence holder for the SingularityU Australia Global Impact Challenge and

WPS A.I CyberSecurity Scoring

Scoring History

WPS

WAVIA Professional Services (Formerly Ignite Alliance) CyberSecurity News & History

Past Incidents

Attack Types

Entity

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Impact

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A.I.

WPS 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 WPS

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

No incidents recorded for WAVIA Professional Services (Formerly Ignite Alliance) in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for WAVIA Professional Services (Formerly Ignite Alliance) in 2026.

Incident Types WPS vs Business Consulting and Services Industry Avg (This Year)

No incidents recorded for WAVIA Professional Services (Formerly Ignite Alliance) in 2026.

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

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SUBSIDIARY

WAVIA Professional Services (Formerly Ignite Alliance) Subsidiaries

Parent Company

WPS

Business Consulting and Services

Website: https://www.ignitealliance.com.au

Company Size: 4

No subsidiary data available for this company.

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NEWS

WAVIA Professional Services (Formerly Ignite Alliance) 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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