What is the official website of Eversheds Sutherland Governments and Infrastructure ?

The official website of Eversheds Sutherland Governments and Infrastructure is https://www.eversheds-sutherland.com/global/en/what/industries/governments-infrastructure/index.page.

What is Eversheds Sutherland Governments and Infrastructure's cybersecurity risk score?

Eversheds Sutherland Governments and Infrastructure has an AI-generated cybersecurity risk score of 773 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has Eversheds Sutherland Governments and Infrastructure experienced?

According to Rankiteo, Eversheds Sutherland Governments and Infrastructure currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has Eversheds Sutherland Governments and Infrastructure been affected by any supply chain cyber incidents ?

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

Does Eversheds Sutherland Governments and Infrastructure have SOC 2 Type 2 certification ?

According to Rankiteo, Eversheds Sutherland Governments and Infrastructure does not hold a SOC 2 Type 2 certification.

Does Eversheds Sutherland Governments and Infrastructure have PCI DSS certification ?

According to Rankiteo, Eversheds Sutherland Governments and Infrastructure does not currently maintain PCI DSS compliance.

Does Eversheds Sutherland Governments and Infrastructure comply with HIPAA ?

According to Rankiteo, Eversheds Sutherland Governments and Infrastructure is not compliant with HIPAA regulations.

Does Eversheds Sutherland Governments and Infrastructure have ISO 27001 certification ?

According to Rankiteo,Eversheds Sutherland Governments and Infrastructure is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

How many employees does Eversheds Sutherland Governments and Infrastructure have ?

Eversheds Sutherland Governments and Infrastructure employs approximately None employees people worldwide.

Does Eversheds Sutherland Governments and Infrastructure own any subsidiaries ?

Eversheds Sutherland Governments and Infrastructure presently has no subsidiaries across any sectors.

How many LinkedIn followers does Eversheds Sutherland Governments and Infrastructure have ?

Eversheds Sutherland Governments and Infrastructure's official LinkedIn profile has approximately 190,195 followers.

What is the NAICS classification code for Eversheds Sutherland Governments and Infrastructure ?

Eversheds Sutherland Governments and Infrastructure is classified under the NAICS code 5411, which corresponds to Legal Services.

Does Eversheds Sutherland Governments and Infrastructure have a Crunchbase profile ?

No, Eversheds Sutherland Governments and Infrastructure does not have a profile on Crunchbase.

Does Eversheds Sutherland Governments and Infrastructure have a LinkedIn profile ?

Yes, Eversheds Sutherland Governments and Infrastructure maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/es-govt-infrastructure.

How many cybersecurity incidents has Eversheds Sutherland Governments and Infrastructure experienced ?

As of June 23, 2026, Rankiteo reports that Eversheds Sutherland Governments and Infrastructure has not experienced any cybersecurity incidents.

How many peer or competitor companies does Eversheds Sutherland Governments and Infrastructure have ?

Eversheds Sutherland Governments and Infrastructure has an estimated 7,897 peer or competitor companies worldwide.

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ESGI

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773

/1000

Fair

798

/1000

Fair

Eversheds Sutherland Governments and Infrastructure Vendor Cyber Rating & Cyber Score

eversheds-sutherland.com

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Our global Governments and Infrastructure group advises on major projects that sustain economic growth. We have vast experience working closely with government bodies, major multinational corporates and infrastructure funds on some of the largest, transformational transport and infrastructure projects across the globe.

ESGI A.I CyberSecurity Scoring

Scoring History

ESGI

Eversheds Sutherland Governments and Infrastructure CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

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Impact

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A.I.

ESGI 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

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Prediction B

Prediction C

Prediction D

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Underwriter Stats for ESGI

Incidents vs Legal Services Industry Avg (This Year)

No incidents recorded for Eversheds Sutherland Governments and Infrastructure in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for Eversheds Sutherland Governments and Infrastructure in 2026.

Incident Types ESGI vs Legal Services Industry Avg (This Year)

No incidents recorded for Eversheds Sutherland Governments and Infrastructure in 2026.

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

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SUBSIDIARY

Eversheds Sutherland Governments and Infrastructure Subsidiaries

ESGI

Legal Services

Website: https://www.eversheds-sutherland.com/global/en/what/industries/governments-infrastructure/index.page

Company Size: None employees

Eversheds SutherlandLaw Practice

Eversheds Sutherland BitānsLegal Services

Eversheds Sutherland BelgiumLegal Services

Eversheds Sutherland Employment, Labor & Pensions Law TrainingLegal Services

Eversheds Sutherland Czech Republic & SlovakiaLegal Services

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NEWS

Eversheds Sutherland Governments and Infrastructure 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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