What is the official website of Bug Out Service, LLC ?

The official website of Bug Out Service, LLC is http://www.bugoutservice.com.

What is Bug Out Service, LLC's cybersecurity risk score?

Bug Out Service, LLC has an AI-generated cybersecurity risk score of 774 out of 1000, indicating a Fair security posture according to Rankiteo's assessment.

How many security incidents has Bug Out Service, LLC experienced?

According to Rankiteo, Bug Out Service, LLC currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.

Has Bug Out Service, LLC been affected by any supply chain cyber incidents ?

According to Rankiteo, Bug Out Service, LLC has not been affected by any supply chain cyber incidents, and no incident IDs are currently listed for the organization.

Does Bug Out Service, LLC have SOC 2 Type 1 certification ?

According to Rankiteo, Bug Out Service, LLC is not certified under SOC 2 Type 1.

Does Bug Out Service, LLC have SOC 2 Type 2 certification ?

According to Rankiteo, Bug Out Service, LLC does not hold a SOC 2 Type 2 certification.

Does Bug Out Service, LLC comply with GDPR ?

According to Rankiteo, Bug Out Service, LLC is not listed as GDPR compliant.

Does Bug Out Service, LLC have PCI DSS certification ?

According to Rankiteo, Bug Out Service, LLC does not currently maintain PCI DSS compliance.

Does Bug Out Service, LLC comply with HIPAA ?

According to Rankiteo, Bug Out Service, LLC is not compliant with HIPAA regulations.

Does Bug Out Service, LLC have ISO 27001 certification ?

According to Rankiteo,Bug Out Service, LLC is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.

Which industry does Bug Out Service, LLC operate in ?

Bug Out Service, LLC operates primarily in the Consumer Services industry.

How many employees does Bug Out Service, LLC have ?

Bug Out Service, LLC employs approximately 88 people worldwide.

Does Bug Out Service, LLC own any subsidiaries ?

Bug Out Service, LLC presently has no subsidiaries across any sectors.

How many LinkedIn followers does Bug Out Service, LLC have ?

Bug Out Service, LLC's official LinkedIn profile has approximately 640 followers.

What is the NAICS classification code for Bug Out Service, LLC ?

Bug Out Service, LLC is classified under the NAICS code 81, which corresponds to Other Services (except Public Administration).

Does Bug Out Service, LLC have a Crunchbase profile ?

No, Bug Out Service, LLC does not have a profile on Crunchbase.

Does Bug Out Service, LLC have a LinkedIn profile ?

Yes, Bug Out Service, LLC maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/bug-out-service-inc..

How many cybersecurity incidents has Bug Out Service, LLC experienced ?

As of June 23, 2026, Rankiteo reports that Bug Out Service, LLC has experienced 1 cybersecurity incidents.

How many peer or competitor companies does Bug Out Service, LLC have ?

Bug Out Service, LLC has an estimated 6,360 peer or competitor companies worldwide.

What types of cybersecurity incidents has Bug Out Service, LLC faced ?

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

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BSL

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774

/1000

Fair

799

/1000

Fair

Bug Out Service, LLC Vendor Cyber Rating & Cyber Score

bugoutservice.com

Add Your Compliance Badge

Founded by John F. Sessions in 1963, Bug Out Service is an industry leader in professional pest control. Bug Out was proud to announce the partnership with other premier companies (Arrow Environmental Services & Skyline Pest Solutions) in the expanded Florida-Georgia area in early 2014. Together these three platform companies operate under the corporate identity of EPS, Environmental Pest Service based in Tampa, Florida. Customers enjoy a superior service experience that can only be delivered by a "hometown" company, yet Bug Out continues to be on the cutting edge of pest control technology and performs services at superior level of professionalism. Bug Out Service, LLC. offers a wide variety of programs to provide sustainable

BSL A.I CyberSecurity Scoring

Scoring History

BSL

Bug Out Service, LLC CyberSecurity News & History

Past Incidents

Attack Types

Entity

Type

Severity

Impact

Seen

Blog Details

Supply Chain Source

Incident Details

View

Bug Out Service, LL...

Breach

9/2020

REN237072725

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A.I.

BSL 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 BSL

Incidents vs Consumer Services Industry Avg (This Year)

No incidents recorded for Bug Out Service, LLC in 2026.

Incidents vs All-Companies Average (This Year)

No incidents recorded for Bug Out Service, LLC in 2026.

Incident Types BSL vs Consumer Services Industry Avg (This Year)

No incidents recorded for Bug Out Service, LLC in 2026.

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

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SUBSIDIARY

Bug Out Service, LLC Subsidiaries

BSL

Consumer Services

Website: http://www.bugoutservice.com

Company Size: 51-200 employees

Rentokil Pest Control North AmericaConsumer Services

Rentokil TerminixConsumer Services

Gregory Pest SolutionsEnvironmental Services

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NEWS

Bug Out Service, LLC CyberSecurity News

Latest updates, reports, and threat intel affecting the global network.

April 03, 2023 07:00 AM

‘I’ve never seen anything like this:’ One of China’s most popular apps has the ability to spy on its users, say experts

Experts say e-commerce giant Pinduoduo has taken violations of privacy and data security to the next level.

Read Article

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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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