A critical security vulnerability has been identified in LangChain’s core library that could allow attackers to extract sensitive system data from artificial intelligence applications. The flaw, tracked as CVE-2025-68664, affects how the framework processes and reconstructs internal data, creating serious risks for organizations relying on AI-driven workflows.
LangChain is a widely adopted framework used to build applications powered by large language models, including chatbots, automation tools, and AI agents. Due to its extensive use across the AI ecosystem, security weaknesses within its core components can have widespread consequences.
The issue stems from how LangChain handles serialization and deserialization. These processes convert data into a transferable format and then rebuild it for use by the application. In this case, two core functions failed to properly safeguard user-controlled data that included a reserved internal marker used by LangChain to identify trusted objects. As a result, untrusted input could be mistakenly treated as legitimate system data.
This weakness becomes particularly dangerous when AI-generated outputs or manipulated prompts influence metadata fields used during logging, event streaming, or caching. When such data passes through repeated serialization and deserialization cycles, the system may unknowingly reconstruct malicious objects. This behavior falls under a known security category involving unsafe deserialization and has been rated critical, with a sev
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