LLM Agents can Autonomously Exploit One-day Vulnerabilities
(2404.08144)Abstract
LLMs have becoming increasingly powerful, both in their benign and malicious uses. With the increase in capabilities, researchers have been increasingly interested in their ability to exploit cybersecurity vulnerabilities. In particular, recent work has conducted preliminary studies on the ability of LLM agents to autonomously hack websites. However, these studies are limited to simple vulnerabilities. In this work, we show that LLM agents can autonomously exploit one-day vulnerabilities in real-world systems. To show this, we collected a dataset of 15 one-day vulnerabilities that include ones categorized as critical severity in the CVE description. When given the CVE description, GPT-4 is capable of exploiting 87% of these vulnerabilities compared to 0% for every other model we test (GPT-3.5, open-source LLMs) and open-source vulnerability scanners (ZAP and Metasploit). Fortunately, our GPT-4 agent requires the CVE description for high performance: without the description, GPT-4 can exploit only 7% of the vulnerabilities. Our findings raise questions around the widespread deployment of highly capable LLM agents.
Overview
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The paper investigates LLMs capabilities, especially GPT-4, in exploiting real-world one-day cybersecurity vulnerabilities.
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A dataset of 15 real-world one-day vulnerabilities from the CVE database was used to assess the effectiveness of GPT-4 against other models and scanners.
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GPT-4 had an 87% success rate in exploiting vulnerabilities with CVE descriptions, significantly outperforming other models and traditional scanners.
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The findings highlight the potential misuse of powerful LLM agents like GPT-4 in cyber attacks and the need for future research in both offensive and defensive uses of LLMs in cybersecurity.
LLM Agents' Capability to Autonomously Exploit Real-World One-Day Vulnerabilities
Introduction to Research Objectives
Recent advances in LLMs have prompted a surge of interest in their application across various domains, including cybersecurity. While previous research predominantly focused on hypothetical or simplified scenarios of cybersecurity vulnerabilities, our investigation explores the capacity of LLM agents to exploit real-world one-day vulnerabilities. By leveraging a dataset of 15 such vulnerabilities, including critically severe ones as per CVE descriptions, we assess the ability of GPT-4 against other models and traditional open-source vulnerability scanners.
Dataset and Methodology
Our research utilized a curated benchmark of 15 real-world one-day vulnerabilities drawn from the CVE database and academic literature, ensuring reproducibility within open-source environments. These vulnerabilities span a range of application areas, from web development frameworks to container management software, with a significant portion marked as high or critical severity.
For the experiment, we deployed a single LLM agent framework, equipped with standard tools and capabilities like web browsing, terminal access, and file manipulation, to exploit the identified vulnerabilities. The effectiveness of the agent, particularly the variation of GPT-4, was evaluated against other LLM models and compared with the outputs from traditional vulnerability scanners.
Key Findings
The standout result from our testing is the significant performance differential between GPT-4 and all other evaluated models and tools. GPT-4 demonstrated an 87% success rate in exploiting the vulnerabilities when provided with CVE descriptions, a stark contrast to the 0% success rate achieved by both the other LLM models and the open-source vulnerability scanners.
Upon removal of CVE descriptions from the agent's resources, GPT-4's success rate notably dropped to 7%, indicating the model's reliance on detailed vulnerability information for successful exploitation. This decline also underscores the complexity of discovering vulnerabilities without explicit guidance, a task at which GPT-4's success rate was considerably lower.
Implications and Future Research Directions
This research underscores the advanced capabilities of LLM agents like GPT-4 in autonomously exploiting real-world cybersecurity vulnerabilities. Such findings prompt critical discussions around the deployment and management of powerful LLM agents, given their potential misuse in black-hat hacking activities.
Further investigation is warranted into enhancing LLM agents’ autonomous vulnerability discovery capabilities without direct CVE guidance. Additionally, incorporating mechanisms like planning modules or subagents might augment their exploitation success rates. From a defensive standpoint, our findings catalyze the cybersecurity field to explore novel strategies that leverage LLM agents' capabilities for vulnerability identification and patching, thereby bolstering systems' security postures against automated attacks.
Conclusion
Our research presents a pioneering exploration into the real-world vulnerability exploitation capabilities of LLM agents, particularly highlighting the emergent prowess of GPT-4 in this domain. As we stand on the cusp of potentially transformative applications of LLMs in cybersecurity, it is imperative to balance innovation with ethical considerations and security best practices to mitigate the risks associated with these powerful tools.
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