LLM Agents can Autonomously Exploit One-day Vulnerabilities

Summary 

This research paper explores the ability of large language models (LLMs), specifically GPT-4, to autonomously exploit real-world cybersecurity vulnerabilities. The authors demonstrate that GPT-4, given a vulnerability description (CVE), successfully exploited 87% of a curated dataset of 15 one-day vulnerabilities—a significantly higher success rate than other LLMs and open-source vulnerability scanners. This high success rate diminishes when the CVE description is removed, highlighting the challenge of vulnerability discovery. The study underscores the potential risks associated with deploying powerful LLMs and emphasizes the need for further research into mitigating these risks. The economic implications of using LLMs for exploitation versus human penetration testers are also considered.

Let’s discuss the capabilities and limitations of LLM agents in cybersecurity, based on the provided excerpt from “LLM_Agents_can_Autonomously_Exploit_One-day_Vulnerabili-_ties.pdf.”

Capabilities of LLM Agents in Cybersecurity

• LLM agents, specifically GPT-4, have demonstrated the ability to autonomously exploit real-world, one-day vulnerabilities. Researchers tested GPT-4 on a benchmark of 15 vulnerabilities sourced from the Common Vulnerabilities and Exposures (CVE) database and academic papers, achieving an 87% success rate.
• The vulnerabilities tested spanned website vulnerabilities, container vulnerabilities, and vulnerable Python packages, with over half categorized as “high” or “critical” severity.
• This success rate significantly outperforms other tested methods, including GPT-3.5, eight open-source LLMs, and open-source vulnerability scanners like ZAP and Metasploit, all of which achieved a 0% success rate.
• GPT-4’s success rate remained high (82%) even when considering only vulnerabilities discovered after its knowledge cutoff date, indicating an ability to exploit vulnerabilities it hasn’t been specifically trained on.
• The GPT-4 agent was able to exploit a variety of vulnerabilities, including those requiring multiple steps and the use of various tools.
• For instance, the agent successfully exploited the “ACIDRain” vulnerability, a complex attack that involves exploiting a race condition in database-backed web applications.
• **The study suggests that exploiting vulnerabilities is potentially easier for GPT-4 than identifying them.**When the CVE description was removed, the agent’s success rate dropped to 7%, though it could still identify the correct vulnerability in 33.3% of cases.

Limitations of LLM Agents in Cybersecurity

• The study highlights the critical role of the CVE description in GPT-4’s success. Without this detailed description, the agent’s ability to exploit vulnerabilities drops significantly.
• This dependence on the CVE description suggests that while GPT-4 excels at exploitation, its ability to autonomously discover vulnerabilities remains limited.
• The study also observed that the GPT-4 agent sometimes struggled with complex website layouts and large amounts of data, impacting its navigation and performance.
• The researchers suggest that incorporating features like planning modules, subagents, and larger tool response sizes could improve the agent’s performance.
• While the study demonstrates the potential cost-effectiveness of using LLM agents for exploiting vulnerabilities, it’s crucial to acknowledge the ethical implications. The authors emphasize the potential for malicious use and the need for responsible development and deployment of such technologies.

The study suggests that while LLM agents show promise in cybersecurity, their capabilities and limitations need further investigation. It is crucial to address the ethical concerns and ensure these technologies are used responsibly.

Side Note for myself

This is gpt 4o and it can combine with blackdagger blackcart

get files from this link and generate me bash command for testing I have this vulnerability in my target 192.168.1.1 https://github.com/watchtowrlabs/palo-alto-panos-cve-2024-0012/tree/main



To assess whether your target at 192.168.1.1 is vulnerable to CVE-2024-0012, you can utilize the Nuclei template provided by watchTowr Labs. This template is designed to detect the authentication bypass vulnerability in Palo Alto Networks PAN-OS.

**Steps to Perform the Test:**

1. **Install Nuclei:**
   If you haven't installed Nuclei, you can do so by following the instructions on the [Nuclei GitHub repository](https://github.com/projectdiscovery/nuclei).

2. **Download the Nuclei Template:**
   Retrieve the specific template for CVE-2024-0012 using the following command:

   ```bash
   wget https://raw.githubusercontent.com/watchtowrlabs/palo-alto-panos-cve-2024-0012/main/palo-alto-vpn-CVE-2024-0012-check-wt.yaml

3. Run the Nuclei Scan: Execute the scan against your target IP address:

   nuclei -u https://192.168.1.1 -t palo-alto-vpn-CVE-2024-0012-check-wt.yaml

   Replace https://192.168.1.1 with the appropriate URL if your management interface uses a different protocol or port.

Important Considerations:

• Authorization: Ensure you have explicit permission to test this system. Unauthorized testing is illegal and unethical.

• Security Measures: Be aware that running such tests can have unintended consequences. Proceed with caution and consider consulting with a security professional.

For more detailed information, refer to the watchTowr Labs GitHub repository.