Evaluating LLM Capabilities in Automated Penetration Testing: A $1,500 Experiment

An independent security researcher explores the efficacy of Large Language Models (LLMs) in identifying and exploiting vulnerabilities within a purposefully insecure application, analyzing whether current AI can automate complex hacking workflows.

Experimental Design and Methodology

The experiment was designed to test the practical application of LLMs in a red-teaming scenario. The author developed a custom application specifically engineered with known security vulnerabilities to serve as a controlled environment. The primary goal was to determine if LLMs could independently discover these flaws and execute successful exploits without significant human guidance.

Resource Allocation and Execution

The researcher invested approximately $1,500 in API credits and computational resources to stress-test various LLM architectures. The process involved prompting the models to perform reconnaissance, identify attack vectors, and generate functional exploit code to compromise the target application.

Key Findings and Analysis

The study focuses on the gap between the theoretical ability of LLMs to write code and their actual capacity to perform multi-step security exploitation. By iterating through different prompts and models, the author assessed the reliability of AI-driven penetration testing and the limitations imposed by safety filters and the "hallucination" of non-existent vulnerabilities.

Note: Due to the limited description provided in the source material, specific technical metrics regarding which models were used or the exact success rate of the exploits are not available in this summary.