What is the official website of PwC US Tax ?

The official website of PwC US Tax is https://www.pwc.com/us/en/services/tax.html.

What is PwC US Tax's cybersecurity risk score?

PwC US Tax has an AI-generated cybersecurity risk score of 748 out of 1000, indicating a Moderate security posture according to Rankiteo's assessment.

How many security incidents has PwC US Tax experienced?

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

Has PwC US Tax been affected by any supply chain cyber incidents ?

According to Rankiteo, PwC US Tax has been affected by a supply chain cyber incident involving NA, with the incident ID OFCK&LAONPROKIRSHEPWCMOV1781843490.

Does PwC US Tax have SOC 2 Type 1 certification ?

According to Rankiteo, PwC US Tax is not certified under SOC 2 Type 1.

Does PwC US Tax have SOC 2 Type 2 certification ?

According to Rankiteo, PwC US Tax does not hold a SOC 2 Type 2 certification.

Does PwC US Tax comply with GDPR ?

According to Rankiteo, PwC US Tax is not listed as GDPR compliant.

Does PwC US Tax have PCI DSS certification ?

According to Rankiteo, PwC US Tax does not currently maintain PCI DSS compliance.

Does PwC US Tax comply with HIPAA ?

According to Rankiteo, PwC US Tax is not compliant with HIPAA regulations.

Does PwC US Tax have ISO 27001 certification ?

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

Which industry does PwC US Tax operate in ?

PwC US Tax operates primarily in the Accounting industry.

How many employees does PwC US Tax have ?

PwC US Tax employs approximately 7 people worldwide.

Does PwC US Tax own any subsidiaries ?

PwC US Tax presently has no subsidiaries across any sectors.

How many LinkedIn followers does PwC US Tax have ?

PwC US Tax's official LinkedIn profile has approximately 47,744 followers.

What is the NAICS classification code for PwC US Tax ?

PwC US Tax is classified under the NAICS code 5412, which corresponds to Accounting, Tax Preparation, Bookkeeping, and Payroll Services.

Does PwC US Tax have a Crunchbase profile ?

No, PwC US Tax does not have a profile on Crunchbase.

Does PwC US Tax have a LinkedIn profile ?

Yes, PwC US Tax maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/pwc-us-tax.

How many cybersecurity incidents has PwC US Tax experienced ?

As of June 23, 2026, Rankiteo reports that PwC US Tax has experienced 1 cybersecurity incidents.

How many peer or competitor companies does PwC US Tax have ?

PwC US Tax has an estimated 9,889 peer or competitor companies worldwide.

What types of cybersecurity incidents has PwC US Tax faced ?

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

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748

/1000

Moderate

773

/1000

Fair

PwC US Tax Vendor Cyber Rating & Cyber Score

pwc.com

Add Your Compliance Badge

Sophisticated insights for better business decisions. As the leading provider of tax services worldwide, that's the value PwC provides. Let us guide you through our digital transformation and share our vast historical and technical knowledge of tax laws and legislation, and we'll help change the way you think about tax.

PUT A.I CyberSecurity Scoring

Scoring History

PUT

PwC US Tax CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

Severity

Impact

Seen

Blog Details

Supply Chain Source

Incident Details

View

PwC

Ransomware

100

7/2023

OFCK&LAONPROKIR...

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A.I.

PUT 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 PUT

Incidents vs Accounting Industry Avg (This Year)

No incidents recorded for PwC US Tax in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for PwC US Tax in 2026.

Incident Types PUT vs Accounting Industry Avg (This Year)

No incidents recorded for PwC US Tax in 2026.

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

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SUBSIDIARY

PwC US Tax Subsidiaries

PUT

Accounting

Website: https://www.pwc.com/us/en/services/tax.html

Company Size: 7

PwCProfessional Services

PwC UKAccounting

PwC UK AlumniAccounting

Olivehorse ConsultingIT Services and IT Consulting

PwC IndiaBusiness Consulting and Services

PwC ItalyProfessional Services

PwC BrasilBusiness Consulting and Services

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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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