Benchmarking Self-Hosted LLMs for Offensive Security
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This article explores the effectiveness of self-hosted Large Language Models (LLMs) in offensive security scenarios, specifically benchmarking local models against the OWASP Juice Shop. Using a minimal harness and basic HTTP tools, the study evaluates models like gemma4:31b, qwen3.5:27b, and devstral-small-2:24b across challenges involving SQL injection, JWT manipulation, and path traversal. The findings indicate that while local models excel at single-step exploit validation—reaching
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Title: Benchmarking Self-Hosted LLMs for Offensive Security
URL Source: https://dev.to/mark0_617b45cda9782a/benchmarking-self-hosted-llms-for-offensive-security-3jio
Published Time: 2026-04-15T05:45:05Z
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# Benchmarking Self-Hosted LLMs for Offensive Security - DEV Community
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Posted on Apr 15
# Benchmarking Self-Hosted LLMs for Offensive Security
[#cybersecurity](https://dev.to/t/cybersecurity)[#infosec](https://dev.to/t/infosec)[#ai](https://dev.to/t/ai)[#llm](https://dev.to/t/llm)
This article explores the effectiveness of self-hosted Large Language Models (LLMs) in offensive security scenarios, specifically benchmarking local models against the OWASP Juice Shop. Using a minimal harness and basic HTTP tools, the study evaluates models like gemma4:31b, qwen3.5:27b, and devstral-small-2:24b across challenges involving SQL injection, JWT manipulation, and path traversal.
The findings indicate that while local models excel at single-step exploit validation—reaching pass rates as high as 98.5%—they falter during complex, multi-step operations such as UNION-based extraction or a
URL Source: https://dev.to/mark0_617b45cda9782a/benchmarking-self-hosted-llms-for-offensive-security-3jio
Published Time: 2026-04-15T05:45:05Z
Markdown Content:
# Benchmarking Self-Hosted LLMs for Offensive Security - DEV Community
[Skip to content](https://dev.to/mark0_617b45cda9782a/benchmarking-self-hosted-llms-for-offensive-security-3jio#main-content)
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Posted on Apr 15
# Benchmarking Self-Hosted LLMs for Offensive Security
[#cybersecurity](https://dev.to/t/cybersecurity)[#infosec](https://dev.to/t/infosec)[#ai](https://dev.to/t/ai)[#llm](https://dev.to/t/llm)
This article explores the effectiveness of self-hosted Large Language Models (LLMs) in offensive security scenarios, specifically benchmarking local models against the OWASP Juice Shop. Using a minimal harness and basic HTTP tools, the study evaluates models like gemma4:31b, qwen3.5:27b, and devstral-small-2:24b across challenges involving SQL injection, JWT manipulation, and path traversal.
The findings indicate that while local models excel at single-step exploit validation—reaching pass rates as high as 98.5%—they falter during complex, multi-step operations such as UNION-based extraction or a
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