Digital Event Horizon
A new tool called Vulnhuntr has been unveiled to identify zero-day vulnerabilities in Python codebases using Large Language Model technology. With its ability to analyze entire call chains and detect complex vulnerabilities, Vulnhuntr promises to revolutionize the way security threats are detected and addressed.
Vulnhuntr, a new tool from Protect AI, uses Large Language Models to identify zero-day vulnerabilities in Python codebases.The tool analyzes user input, function calls, and class references to detect security threats.Vulnhuntr focuses on seven types of remotely exploitable vulnerabilities, including SQL Injection and Remote Code Execution.The tool has identified over a dozen zero-day vulnerabilities in large open-source Python projects.Vulnhuntr's approach improves upon traditional static code analyzers by analyzing the entire call chain.However, Vulnhuntr has limitations, including only working on Python code and generating false positives with non-Python code.
Protect AI, a Seattle-based research firm, has recently announced the release of a new tool called Vulnhuntr that utilizes the power of Large Language Models (LLMs) to identify zero-day vulnerabilities in Python codebases. The tool, which relies on Anthropic's Claude AI model, is designed to scan through Python projects and detect potential security threats by analyzing user input, function calls, and class references.
According to Dan McInerney, lead AI threat researcher at Protect AI, the tool was originally designed using Claude and leverages its best practices in prompt engineering. The developers aimed to create a static code analyzer that could identify complex, multi-step vulnerabilities, which are often difficult for traditional static analyzers to detect.
Vulnhuntr focuses on seven types of remotely exploitable vulnerabilities: Arbitrary File Overwrite (AFO), Local File Inclusion (LFI), Server-Side Request Forgery (SSRF), Cross-Site Scripting (XSS), Insecure Direct Object References (IDOR), SQL Injection (SQLi), and Remote Code Execution (RCE). The tool has already identified more than a dozen zero-day vulnerabilities in large, open-source Python projects, including gpt_academic, ComfyUI, FastChat, and Ragflow.
The tool's detection process involves analyzing user input used as keys to access dictionaries, which are instantiated with user-supplied parameters. It then checks for proper validation and sanitization of these inputs to identify potential vulnerabilities. Once a vulnerability is identified, the tool generates a proof-of-concept (PoC) exploit that can be used to demonstrate the severity of the issue.
Vulnhuntr's approach has several advantages over traditional static code analyzers. It can read the entire call chain from user input to server output without blowing its context window, reducing false positives and negatives. The tool also improves upon current static code analyzers by analyzing the entire call chain rather than just small code snippets.
However, Vulnhuntr is not without limitations. It only works on Python code at the moment and depends on access to a Python static analyzer. As a result, the tool may generate false positives when scanning projects that incorporate code in other languages. Additionally, the Claude API, which powers Vulnhuntr's detection capabilities, has a cost associated with it, averaging around $0.50 per token usage.
Despite these limitations, McInerney believes that Vulnhuntr represents a significant improvement over traditional static analyzers and will be particularly useful for identifying complex, multi-step vulnerabilities in Python codebases. The release of this tool marks a new era in the use of Large Language Models for security threat detection, and its potential impact on the cybersecurity industry is substantial.
Related Information:
https://go.theregister.com/feed/www.theregister.com/2024/10/20/python_zero_day_tool/
Published: Sun Oct 20 04:37:02 2024 by llama3.2 3B Q4_K_M
