Key Points
1. Large language models (LLMs) have become increasingly capable and can now autonomously hack websites without prior knowledge of vulnerabilities.
2. A study showed that LLM agents can perform complex tasks, such as blind database schema extraction and SQL injections, without human feedback.
3. The capabilities of LLM agents, especially GPT-4, were demonstrated to autonomously find vulnerabilities in real-world websites.
4. The study involved giving LLM agents the ability to read documents, call functions to manipulate a web browser, and access context from previous actions in order to autonomously hack websites.
5. The research found that the success rate of the most capable agent, GPT-4, was 73.3% for hacking websites, and the cost of attempting a hack was approximately $9.81 per website.
6. Open-source LLMs, including GPT-3.5, were found to have a 0% success rate in autonomously hacking websites, while GPT-4 demonstrated significant success.
7. GPT-4 was capable of performing complex website hacks involving as many as 48 function calls per successful hack.
8. The study highlighted the need for LLM providers to think carefully about deploying and releasing models, and it raised concerns about the potential use of LLMs in cyber attacks and the need for responsible release policies.
9. The findings were disclosed to OpenAI prior to publication, and the researchers acknowledged funding from the Open Philanthropy project for the research.
Summary
The research paper explores the capabilities of large language models (LLMs) to function autonomously as agents and their potential impact on cybersecurity. The paper specifically focuses on the potential of LLM agents to autonomously hack websites and the limited understanding of their offensive capabilities. The research shows that LLM agents can autonomously hack websites, demonstrating complex tasks such as blind database schema extraction and SQL injections without human feedback.
The most capable agent has successfully hacked 73.3% of tested vulnerabilities, including finding vulnerabilities in real-world websites. The paper also compares the offensive capabilities of different LLM models, highlighting the limitations of open-source models compared to frontier models like GPT-4. It discusses the cost of autonomously hacking websites with LLM agents, comparing it to human effort.
Additionally, the paper emphasizes the need for careful consideration in releasing LLM models and the implications for cybersecurity. The research has been conducted in a responsible manner, and the findings have been disclosed to OpenAI prior to publication.
Reference: https://arxiv.org/abs/2402.06664v1