What is the official website of Sound Lab a Keywords Studio ?

The official website of Sound Lab a Keywords Studio is https://www.keywordsstudios.com/services/audio-services/.

What is Sound Lab a Keywords Studio's cybersecurity risk score?

Sound Lab a Keywords Studio has an AI-generated cybersecurity risk score of 761 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has Sound Lab a Keywords Studio experienced?

According to Rankiteo, Sound Lab a Keywords Studio currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has Sound Lab a Keywords Studio been affected by any supply chain cyber incidents ?

According to Rankiteo, Sound Lab a Keywords Studio has not been affected by any supply chain cyber incidents, and no incident IDs are currently listed for the organization.

Does Sound Lab a Keywords Studio have SOC 2 Type 1 certification ?

According to Rankiteo, Sound Lab a Keywords Studio is not certified under SOC 2 Type 1.

Does Sound Lab a Keywords Studio have SOC 2 Type 2 certification ?

According to Rankiteo, Sound Lab a Keywords Studio does not hold a SOC 2 Type 2 certification.

Does Sound Lab a Keywords Studio comply with GDPR ?

According to Rankiteo, Sound Lab a Keywords Studio is not listed as GDPR compliant.

Does Sound Lab a Keywords Studio have PCI DSS certification ?

According to Rankiteo, Sound Lab a Keywords Studio does not currently maintain PCI DSS compliance.

Does Sound Lab a Keywords Studio comply with HIPAA ?

According to Rankiteo, Sound Lab a Keywords Studio is not compliant with HIPAA regulations.

Does Sound Lab a Keywords Studio have ISO 27001 certification ?

According to Rankiteo,Sound Lab a Keywords Studio is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

Which industry does Sound Lab a Keywords Studio operate in ?

Sound Lab a Keywords Studio operates primarily in the Entertainment Providers industry.

How many employees does Sound Lab a Keywords Studio have ?

Sound Lab a Keywords Studio employs approximately 15 people worldwide.

Does Sound Lab a Keywords Studio own any subsidiaries ?

Sound Lab a Keywords Studio presently has no subsidiaries across any sectors.

How many LinkedIn followers does Sound Lab a Keywords Studio have ?

Sound Lab a Keywords Studio's official LinkedIn profile has approximately 1,041 followers.

Does Sound Lab a Keywords Studio have a Crunchbase profile ?

No, Sound Lab a Keywords Studio does not have a profile on Crunchbase.

Does Sound Lab a Keywords Studio have a LinkedIn profile ?

Yes, Sound Lab a Keywords Studio maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/the-sound-lab-keywords-studios.

How many cybersecurity incidents has Sound Lab a Keywords Studio experienced ?

As of June 23, 2026, Rankiteo reports that Sound Lab a Keywords Studio has not experienced any cybersecurity incidents.

How many peer or competitor companies does Sound Lab a Keywords Studio have ?

Sound Lab a Keywords Studio has an estimated 7,488 peer or competitor companies worldwide.

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SLKS

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761

/1000

Fair

786

/1000

Fair

Sound Lab a Keywords Studio Vendor Cyber Rating & Cyber Score

keywordsstudios.com

Add Your Compliance Badge

The Sound Lab is a team of 20 full time sound designers, implementors, and mixers with a custom facility in Burbank, CA with 9 design rooms and 1 design/mix room. We have taken audiences on immersive journeys for three decades on films such as Transformers One, Guillermo del Toro's Pinocchio, 'Nightcrawler', 'Pacific Rim', 'Hellboy 2','Star Trek' and 'American Beauty' and games like 'Doom' (2016), 'Epic Mickey', 'Gears of War', 'Resident Evil', 'Fable’ and many more. We have been recognized multiple times for our work by MPSE, GANG, NAMM Foundation, BAFTA, DICE, and The Academy of Motion Picture Arts and Sciences.

SLKS A.I CyberSecurity Scoring

Scoring History

SLKS

Sound Lab a Keywords Studio 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.

SLKS 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 SLKS

Incidents vs Entertainment Providers Industry Avg (This Year)

No incidents recorded for Sound Lab a Keywords Studio in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for Sound Lab a Keywords Studio in 2026.

Incident Types SLKS vs Entertainment Providers Industry Avg (This Year)

No incidents recorded for Sound Lab a Keywords Studio in 2026.

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

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SUBSIDIARY

Sound Lab a Keywords Studio Subsidiaries

SLKS

Entertainment Providers

Website: https://www.keywordsstudios.com/services/audio-services/

Company Size: 15

Keywords StudiosComputer Games

Lakshya DigitalComputer Games

Helpshift, a Keywords TechnologySoftware Development

VOLTAEntertainment Providers

Studio GoboComputer Games

Liquid DevelopmentComputer Games

SperasoftComputer Games

Smoking Gun Interactive Inc.Computer Games

d3tComputer Games

Forgotten EmpiresComputer Games

Wicked WorkshopComputer Games

AMC StudioComputer Games

Heavy Iron Studios, Inc.Computer Games

Waste CreativeAdvertising Services

Fire Without SmokeAdvertising Services

Tantalus MediaComputer Games

Hardsuit LabsComputer Games

WizcorpComputer Games

Player ResearchComputer Games

Binari SonoriTranslation and Localization

Mighty Games GroupComputer Games

Coconut LizardSoftware Development

ICHI WorldwideAdvertising Services

Indigo Pearl UKPublic Relations and Communications Services

Syllabes - Studio & AcademyBroadcast Media Production and Distribution

JinglebellMedia Production

LacedEntertainment Providers

Keywords Studios Germany GmbHComputer Games

gnet agencyAdvertising Services

asrec - a Keywords studioTranslation and Localization

PXN DigitalPublic Relations and Communications Services

XLOC, INC.Computer Games

Keywords Studios - MontréalComputer Games

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NEWS

Sound Lab a Keywords Studio 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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