What is the official website of SARET Research Program ?

The official website of SARET Research Program is https://www.fire.nsw.gov.au/saret.

What is SARET Research Program's cybersecurity risk score?

SARET Research Program has an AI-generated cybersecurity risk score of 760 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has SARET Research Program experienced?

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

Has SARET Research Program been affected by any supply chain cyber incidents ?

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

Does SARET Research Program have SOC 2 Type 1 certification ?

According to Rankiteo, SARET Research Program is not certified under SOC 2 Type 1.

Does SARET Research Program have SOC 2 Type 2 certification ?

According to Rankiteo, SARET Research Program does not hold a SOC 2 Type 2 certification.

Does SARET Research Program comply with GDPR ?

According to Rankiteo, SARET Research Program is not listed as GDPR compliant.

Does SARET Research Program have PCI DSS certification ?

According to Rankiteo, SARET Research Program does not currently maintain PCI DSS compliance.

Does SARET Research Program comply with HIPAA ?

According to Rankiteo, SARET Research Program is not compliant with HIPAA regulations.

Does SARET Research Program have ISO 27001 certification ?

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

Which industry does SARET Research Program operate in ?

SARET Research Program operates primarily in the Public Safety industry.

How many employees does SARET Research Program have ?

SARET Research Program employs approximately None employees people worldwide.

Does SARET Research Program own any subsidiaries ?

SARET Research Program presently has no subsidiaries across any sectors.

How many LinkedIn followers does SARET Research Program have ?

SARET Research Program's official LinkedIn profile has approximately 695 followers.

What is the NAICS classification code for SARET Research Program ?

SARET Research Program is classified under the NAICS code 92219, which corresponds to Other Justice, Public Order, and Safety Activities.

Does SARET Research Program have a Crunchbase profile ?

No, SARET Research Program does not have a profile on Crunchbase.

Does SARET Research Program have a LinkedIn profile ?

Yes, SARET Research Program maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/frnsw-saret.

How many cybersecurity incidents has SARET Research Program experienced ?

As of June 23, 2026, Rankiteo reports that SARET Research Program has experienced 5 cybersecurity incidents.

How many peer or competitor companies does SARET Research Program have ?

SARET Research Program has an estimated 2,210 peer or competitor companies worldwide.

What types of cybersecurity incidents has SARET Research Program faced ?

Incident Types: The types of cybersecurity incidents that have occurred include Ransomware, Breach and Cyber Attack.

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SRP

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760

/1000

Fair

785

/1000

Fair

SARET Research Program Vendor Cyber Rating & Cyber Score

nsw.gov.au

Add Your Compliance Badge

The Safety of Alternative and Renewable Energy Technologies (SARET) Research Program is an initiative of Fire and Rescue NSW. The program aims to inform public policy, education and operational procedures for managing the fire and explosion risks related to new and emerging energy technologies.

SRP A.I CyberSecurity Scoring

Scoring History

SRP

SARET Research Program CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

Severity

Impact

Seen

Blog Details

Supply Chain Source

Incident Details

View

NSW Treasury

Breach

4/2026

NSW1776803465

NSW Police Force

Breach

6/2024

NSW1769571644

SARET Research Prog...

Breach

100

02/2022

NSW234711522

SARET Research Prog...

Cyber Attack

100

07/2021

NSW16403223

SARET Research Prog...

Ransomware

100

2/2021

TRA303092125

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A.I.

SRP 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 SRP

Incidents vs Public Safety Industry Avg (This Year)

No incidents recorded for SARET Research Program in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for SARET Research Program in 2026.

Incident Types SRP vs Public Safety Industry Avg (This Year)

No incidents recorded for SARET Research Program in 2026.

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

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SUBSIDIARY

SARET Research Program Subsidiaries

SRP

Public Safety

Website: https://www.fire.nsw.gov.au/saret

Company Size: 2-10 employees

I work for NSWGovernment Administration

NSW Department of Planning, Housing and InfrastructureGovernment Administration

NSW Small Business CommissionerGovernment Administration

NSW Department of EducationEducation Administration Programs

Information Technology DirectorateIT Services and IT Consulting

Transport for NSWTruck Transportation

Sydney MetroRail Transportation

Sydney TrainsTruck Transportation

NSW TrainLinkTransportation/Trucking/Railroad

NSW Police ForceLaw Enforcement

NSW TreasuryGovernment Administration

NSW Public Service CommissionGovernment Administration

NSW Environment Protection Authority (EPA)Environmental Services

TCorpFinancial Services

NSW Office of Local Government Government Administration

Fire and Rescue NSWPublic Safety

FRNSW Fire SafetyPublic Safety

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NEWS

SARET Research Program 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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