A security researcher uncovered a high-risk vulnerability in the popular LangChain JS framework that could allow attackers to read arbitrary files on servers running applications built with the framework.
LangChain, an open-source project designed to assist developers in building applications powered by large language models (LLMs), offers libraries in both Python and JavaScript.
LangChain is a framework that makes it easier for developers to use large language models (LLMs) in various applications.
Recently, a 37-year-old cybersecurity researcher, Evren, identified that the LangChain JS vulnerability allows threat actors to expose sensitive information.
The vulnerability, classified as an Arbitrary File Read (AFR) issue, stems from improper input validation when handling user-supplied URLs.
By exploiting this flaw with Server Side Request Forgery (SSRF), an attacker could craft malicious URLs pointing to local files on the server, enabling them to access and read sensitive information they should not have access to.
These vulnerabilities can enable XSS attacks, which inject malicious code into victims’ browsers. Widely used JS libraries or frameworks with security flaws can also impact numerous sites simultaneously.
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“The fact that this project has more than 11,000 stars and more than 380,000 weekly downloads shows its popularity and widespread use,” stated the security researcher who discovered the vulnerability. “I could not find any guidelines in the LangChain documentation indicating what measures should be taken when receiving a URL from a user, which in my personal opinion, poses a high risk.”
The researcher provided a proof-of-concept (PoC) code demonstrating how an attacker could leverage the vulnerability.
The vulnerability was reported to the LangChain team, who classified it as “Informative”. The team stated that LangChain JS utilizes the Playwright project in the background and that developers are responsible for its secure implementation.
However, the researchers noted that the LangChain documentation lacks clear guidelines on the precautions developers should take when receiving URLs from users, leading them to consider this vulnerability high-risk.
Threat actors can use this vulnerability to access files on the server without authorization, which helps expose sensitive data.
It allows developers to easily use LLMs in Python or JavaScript for document analysis, summarization, conversational AI, and code analysis.
Mitigations
Here below, we have mentioned all the mitigations:-
- Implement strict input validation, as this will properly sanitize and validate all the URLs.
- Maintain an allowed domains list to restrict the URL fetching to only a specific set of domains that are marked as trusted.
- Make sure to deny and block access to sensitive URL schemas like file://, ftp://, and others that should not be accessible.
- Network segmentation is a must, as this helps in limiting the access to internal network resources and services.
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