A serious security flaw has been uncovered in LangChain Core, a widely used framework for building applications powered by large language models (LLMs). The vulnerability could allow attackers to steal sensitive data, manipulate AI responses, and potentially gain deeper system access through crafted inputs.
The issue, now tracked as CVE-2025-68664, carries a high severity score of 9.3 out of 10, making it one of the most critical vulnerabilities discovered in the LangChain ecosystem to date.
What Is LangChain and Why This Matters
LangChain is a foundational framework used by developers to build AI-powered applications that interact with large language models. It provides reusable components for memory, agents, tools, and prompt handling. Because of its wide adoption, any flaw in its core architecture can affect a large number of applications across different industries.
The newly discovered vulnerability affects LangChain Core, the central module responsible for handling object serialization and data flow between components.
How the Vulnerability Works
Security researchers discovered that LangChain failed to properly sanitize certain user-controlled inputs during serialization. Specifically, the issue lies in how the framework handles objects containing a special internal key called "lc".
This key is normally used internally to identify trusted LangChain objects. However, attackers can craft input that mimics this structure. When such data is processed, the system mistakenly treats it as a trusted object rather than untrusted user input.
As a result, attackers can:
- Inject malicious object structures
- Trigger unsafe deserialization
- Access internal application data
- Manipulate prompt behavior or logic flow
In some scenarios, this could lead to exposure of environment secrets or execution of unintended operations within the application.
How the Attack Could Be Exploited
The vulnerability becomes particularly dangerous when applications:
- Accept user-generated content
- Serialize and deserialize LLM responses
- Allow metadata or tool outputs to be processed automatically
Researchers demonstrated that malicious inputs could be delivered through areas such as metadata fields, response objects, or prompt variables. Once processed, these inputs could activate hidden logic paths inside the application.
In advanced cases, attackers could abuse this behavior to load unauthorized components, extract sensitive information, or influence how the AI responds to future prompts.
Patch and Mitigation Measures
The LangChain team has released security updates that address the issue by introducing stricter validation rules.
Key protections now include:
- A new allowlist-based system that restricts which object types can be deserialized
- Disabled automatic loading of environment variables during deserialization
- Improved handling of internal object markers to prevent spoofing
Users are strongly advised to update immediately.
Affected Versions:
- langchain-core: versions before 1.2.5
- langchain (Python): versions before 1.2.3
- @langchain/core (JavaScript): versions before 1.1.8
Earlier releases remain vulnerable and should not be used in production.
Why This Matters for AI Security
This incident highlights a growing concern in modern AI systems: traditional software vulnerabilities can become far more dangerous when combined with generative AI and automated workflows.
When AI models are allowed to process and act on untrusted data, even small design oversights can lead to major security failures. The LangChain issue shows how prompt injection, serialization flaws, and automation can intersect in ways that attackers can exploit at scale.
Final Thoughts
As AI frameworks become more powerful and widely adopted, security must evolve just as quickly. Developers should treat AI pipelines with the same caution as any other production system—especially when handling user input, secrets, or automated decision-making.
Updating dependencies, reviewing serialization logic, and applying strict input validation are no longer optional—they are essential.
Staying informed and proactive is the best defense.