Introduction
Attackers can compromise AI-powered email automation by sending specially crafted emails that exploit a critical prompt injection flaw in LangChain's GmailToolkit. This vulnerability enables arbitrary code execution in environments running the affected component, posing a significant risk to organizations using automated email workflows.
LangChain is a prominent open-source framework for building applications powered by large language models (LLMs). Its GmailToolkit component allows developers to automate and integrate Gmail interactions within AI workflows. With widespread adoption in the AI and automation community, vulnerabilities in LangChain components have broad implications for the security of LLM-driven systems.
Technical Information
CVE-2025-46059 is an indirect prompt injection vulnerability in LangChain's GmailToolkit component, specifically affecting version 0.3.51. The vulnerability arises when the toolkit processes email content that contains hidden or obfuscated instructions, which are then interpreted by the underlying language model as executable commands. Attackers can craft email messages with HTML elements or encoded content that bypasses basic sanitization. When the GmailToolkit ingests such an email, the embedded instructions can be executed as code, leading to arbitrary code execution in the application environment. This flaw is tracked under CWE-94 (Improper Control of Generated Code).
Public sources indicate that the root cause is insufficient sanitization of email content before it is passed to the LLM for processing. No public code snippets or exploit details are currently available.
Affected Systems and Versions
- Product: LangChain GmailToolkit
- Affected version: 0.3.51
- Other versions: No evidence of other affected versions was found in public sources
- Vulnerable configuration: Any deployment using GmailToolkit from LangChain v0.3.51 that processes untrusted email content
Vendor Security History
LangChain has previously experienced similar vulnerabilities, including CVE-2023-44467 and CVE-2023-46229, both related to prompt injection and code execution issues. The vendor has a track record of responding quickly to security reports, but the recurrence of such issues suggests ongoing challenges in securely integrating LLMs with external data sources.