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Automated AI penetration testing framework uses free LLMs to cut costs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PentestChain, a novel framework for automated penetration testing that utilizes free-tier and local Large Language Models (LLMs) to reduce costs. The system employs a cost-aware cascade, starting with a local Ollama model (qwen2.5-7b) and progressing to free-tier services like OpenRouter and Cerebras, with a rule-based fallback. This approach aims to make continuous, automated security testing accessible to smaller organizations by minimizing API expenses. The framework also addresses security concerns related to exposing an MCP-orchestrated engine, proposing mitigations for potential vulnerabilities. AI

IMPACT This framework could significantly lower the barrier to entry for AI-driven security testing, making advanced capabilities accessible to smaller organizations.

RANK_REASON The item describes a novel framework and evaluation protocol presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Automated AI penetration testing framework uses free LLMs to cut costs

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The item describes a novel framework and evaluation protocol presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rushabh Vipulkumar Patel, Dipo Dunsin, Mohammed Almaiah, Mohamed Chahine Ghanem ·

    PentestChain: A Cost-Aware, MCP-Orchestrated Framework for Automated Penetration Testing with Free-Tier LLMs

    arXiv:2609.18120v1 Announce Type: cross Abstract: AI-driven penetration testing has been demonstrated with premium frontier models such as GPT-4, but the per-engagement token cost makes continuous, automated testing unaffordable for the smaller organisations that need it most. Th…

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