Comprehensive Analysis of the LangGrinch Vulnerability (CVE-2025-68664): A Critical Security Advisory for LangChain Core

In the rapidly evolving landscape of artificial intelligence, security frameworks are constantly tested by new and unexpected vulnerabilities. Recently, a significant security disclosure was made regarding LangChain, one of the most widely deployed AI framework components globally. This vulnerability, tracked as CVE-2025-68664 and assigned the identifier GHSA-c67j-w6g6-q2cm, has been dubbed “LangGrinch.” It represents a critical flaw in the core serialization logic of the LangChain framework, one that allows for the leakage of secrets and the unsafe instantiation of objects.
This analysis provides a detailed, technical breakdown of the LangGrinch vulnerability, exploring its mechanisms, its potential impact on global systems, and the necessary steps for mitigation. The information presented here is drawn directly from the security research published by Cyata, focusing on the implications for developers, security teams, and organizations utilizing agent-based AI infrastructure.

Introduction: The Nature of the Threat

The LangGrinch vulnerability is not merely a bug in a peripheral tool; it resides within langchain-core, the foundational library upon which countless AI applications are built. The vulnerability is classified under CWE-502: Deserialization of Untrusted Data and has received a CVSS score of 9.3 (Critical).
At its heart, the issue stems from how LangChain handles data serialization. Specifically, the internal serialization functions dumps() and dumpd() failed to properly escape user-controlled dictionaries that contained a reserved key known as 'lc'. This oversight allows an attacker to manipulate data structures—potentially influenced by Large Language Model (LLM) outputs—in a way that tricks the system into creating unauthorized objects. When these objects are instantiated, they can lead to severe consequences, including the extraction of sensitive environment variables and, under specific conditions, arbitrary code execution.

The Criticality of the Vulnerability

To understand why this vulnerability demands immediate attention, it is essential to look at three specific factors highlighted in the advisory: location, scale, and the ease of exploitation.

1. Location: The Vulnerability is in “Core”

Unlike many security issues that affect specific integrations, community-made extensions, or edge-case tools, LangGrinch affects the core library. The vulnerable APIs, dumps() and dumpd(), are native to langchain-core. This means the flaw is not isolated to a niche usage pattern but is present in the fundamental data handling mechanisms used by the framework. When the core of a widely used system is compromised, the potential for damage increases exponentially because almost every interaction with the system may pass through these vulnerable functions.

2. Scale: A Massive Blast Radius

The reach of LangChain in the current AI ecosystem is immense. According to public package telemetry data referenced in the research, LangChain has accumulated hundreds of millions of installations. Specifically, pepy.tech reports approximately 847 million total downloads, while data from pypistats indicates nearly 98 million downloads in the last month alone.
Given these numbers, the “blast radius”—or the number of systems potentially affected—is vast. Organizations ranging from small startups to large enterprises likely utilize this framework in production environments. A critical vulnerability in such a ubiquitous component implies that a significant portion of the modern AI stack is currently at risk.

3. Ease of Exploitation: One Prompt Can Trigger the Attack

Perhaps the most concerning aspect of LangGrinch is the subtlety of its attack vector. In traditional software vulnerabilities, an attacker might need to send a complex payload to a specific endpoint. However, in the context of AI agents, the attack vector can be as simple as a single text prompt.
The most common real-world path for exploitation does not involve an attacker sending a serialized blob directly. Instead, it exploits the way LLMs output data. Fields such as additional_kwargs or response_metadata can be influenced by “prompt injection.” When an LLM generates content that includes malicious structures in these fields, the LangChain framework may serialize and later deserialize this data during normal operations like streaming logs or events. This means a seemingly innocuous prompt can cascade through a complex internal pipeline and trigger the vulnerability.

Technical Deep Dive: Understanding the Mechanics

To fully grasp the risk, one must understand the technical mechanics of the serialization process in LangChain and how the security boundary was breached.

The “lc” Marker

LangChain utilizes a special internal format for serializing objects. In this format, dictionaries that contain a specific key labeled 'lc' are treated as LangChain objects. This marker serves as a signal to the system: “This is not just random data; this is a structured instruction to create a specific object.”
This creates a necessary security invariant: Any user-controlled data that might contain an 'lc' key must be treated with extreme caution. If an attacker can craft a dictionary that looks like a LangChain object (by including this marker), they can trick the deserializer (the load() function) into giving that malicious data meaning and functionality within the system.

The Serialization Gap

The vulnerability arose not because of a complex error, but because of an omission—the absence of code. The dumps() function, responsible for converting objects into a storable format, simply did not escape user-controlled dictionaries that contained the 'lc' key.
When auditing the code, it is often easier to spot a mistake—something that is written incorrectly—than it is to notice something that is missing. For two and a half years, this gap existed in one of the most scrutinized AI frameworks. The vulnerability allowed an attacker to inject an 'lc' marker into data that the system would later trust implicitly.

The Allowlist and Instantiation

When LangChain’s load() or loads() functions encounter data marked with 'lc', they attempt to instantiate an object. However, they do not create just any object; they check against an “allowlist.” This list defines which classes are safe to deserialize.
By default, this allowlist includes classes from:

• langchain_core
• langchain_openai
• langchain_aws
• langchain_anthropic
• Other ecosystem packages.
  The security challenge lies in finding classes within this allowlist that perform dangerous actions when they are initialized (constructed). While many classes have harmless constructors, the research identified specific paths that could be leveraged for attacks.

Attack Scenarios and Potential Impact

The advisory outlines 12 distinct vulnerable flows where this serialization and deserialization pattern occurs. These include standard event streaming, logging, message history management, memory caches, and vector store operations. The outcomes of a successful attack can be devastating.

Secret Extraction from Environment Variables

One of the primary capabilities of the loads() function is its support for a _secret_ type. This feature allows the system to resolve values from environment variables during the deserialization process.
Prior to the patch, the feature secrets_from_env was enabled by default. The logic worked as follows: if the deserialized object identified itself as a secret type and provided an ID (key), the system would check the environment variables. If a match was found, it would return the value of that environment variable.
From an attacker’s perspective, this is a gold mine. If a deserialized object is returned to the attacker—for instance, if it appears in message history fed back into the LLM context—sensitive credentials like API keys, database passwords, or tokens can be leaked directly.

Indirect Exfiltration via Network Requests

Even if an attacker cannot directly see the LLM’s response (a scenario known as “blind prompt injection”), they can still exfiltrate secrets. The research highlighted a specific class within the allowlist: ChatBedrockConverse from langchain_aws.
This class is significant because it performs a network GET request immediately upon construction. An attacker can control the endpoint URL to which this request is sent. By combining this with the secrets_from_env feature, an attacker can populate specific HTTP headers (such as aws_access_key_id) with values taken from the server’s environment variables.
In this scenario, the attacker does not need to see the response; they simply set up a server to listen for the incoming request. The vulnerable application, acting on the attacker’s prompt, reaches out to the attacker’s server and hands over the secrets in the headers.

Arbitrary Code Execution

The advisory also touches on the potential for remote code execution (RCE). Among the classes on the default allowlist is PromptTemplate. This class is used to generate prompts using templates, and it supports the Jinja2 template format.
Jinja2 is a powerful templating engine that can execute arbitrary Python code when rendering a template. While the researchers did not identify a direct path to trigger this rendering solely through the loads() function, the risk remains. If any subsequent operation triggers the rendering of a deserialized, malicious template, code execution becomes possible.
There is also a historical note that in past versions, a Chain class was on the allowlist, which possessed special features that might have facilitated this flow. This suggests that while direct RCE via loads() alone was not confirmed, the boundaries of this vulnerability may stretch further as the ecosystem is investigated.

Who Is Affected? A Practical Checklist

The scope of affected systems is broad. If your application uses vulnerable versions of langchain-core, it is potentially exposed. The advisory lists several common, vulnerable patterns:

1. astream_events(version="v1"): Note that version “v1” uses the vulnerable serialization, while “v2” does not.
2. Runnable.astream_log(): A standard logging mechanism for runnables.
3. dumps() / dumpd() usage: Direct use of these functions on untrusted data, followed by load() / loads().
4. Deserialization of Untrusted Data: Any direct deserialization of data that originates from an untrusted source.
5. Internal Serialization Flows: This includes components like RunnableWithMessageHistory, InMemoryVectorStore.load(), specific caching implementations, and pulling manifests from the LangChain Hub (e.g., hub.pull).
   The system’s behavior is complex, and the advisory warns that it is risky to rely on a simple code review to determine safety. The presence of these flows in common operations suggests that many applications are vulnerable without the developers realizing it.
   The most critical takeaway is that LLM output acts as an untrusted input. If your framework treats portions of that output—specifically fields like additional_kwargs or response_metadata—as structured objects to be serialized and deserialized later, you are vulnerable to prompt injection attacks that exploit this flaw.

Defensive Guidance: Mitigation and Response

For organizations running LangChain in production, immediate action is required. The complexity of the framework means this issue is “tricker than it may seem,” and simple workarounds may not be sufficient.

1. Patch Immediately

The most effective way to reduce risk is to upgrade to a patched version. The LangChain team has released fixes in versions 1.2.5 and 0.3.81.
It is crucial to verify the actual version of langchain-core installed in your production environments. Even if you are using langchain or langchain-community, you must ensure that the underlying langchain-core dependency has been updated.

2. Treat LLM Outputs as Hostile

Security teams must adopt a mindset where LLM outputs are considered “attacker-shaped.” Unless explicitly verified, fields such as additional_kwargs, response_metadata, tool outputs, retrieved documents, and message history should be treated as untrusted inputs. This is particularly vital for applications that stream logs or events and later rehydrate (deserialize) them using a loader.

3. Review Deserialization Features

Even after applying patches, organizations should adhere to the principle of least privilege regarding secret resolution. The secrets_from_env feature was changed to a non-default setting by the project maintainers for a reason. You should not enable secret resolution from environment variables unless you absolutely trust the serialized data source.

The LangChainJS Parallel: A Recurring Risk Pattern

It is important to note that this vulnerability is not isolated to the Python ecosystem. Based on the same research report, a closely related advisory was issued for LangChainJS (identified as GHSA-r399-636x-v7f6 / CVE-2025-68665).
The mechanics in the JavaScript version are nearly identical: confusion over the 'lc' marker during serialization, leading to secret extraction and unsafe instantiation. This parallel serves as a reminder that security patterns often travel across ecosystems. The combination of marker-based serialization, untrusted model output, and deferred deserialization is a structural risk shape that affects AI frameworks regardless of the programming language. Organizations running both Python and JavaScript stacks must ensure both environments are addressed.

Broader Implications: AI Security and Governance

The LangGrinch vulnerability is more than just a bug to be patched; it is a case study in the emerging security challenges of the Agentic AI era.

The Shift in Security Boundaries

We are entering a phase where agentic AI frameworks are becoming critical infrastructure. Traditionally, serialization formats, orchestration pipelines, and tool execution were considered “plumbing”—backend concerns handled by trusted code. Today, they are active parts of the security boundary.
This vulnerability highlights a dangerous pattern: an application may deserialize data it believes it produced safely. However, that serialized output can be influenced by untrusted sources, such as an LLM manipulated via prompt injection. A simple reserved key ('lc') used internally can pivot from a data marker to a mechanism for accessing secrets and executing code.

The Challenge of Visibility

For security leaders, this vulnerability exposes a fundamental governance gap. Most organizations currently cannot quickly or confidently answer the following questions:

• Where are we using agents?
• Which versions of the frameworks are deployed in production?
• Which services have access to sensitive secrets?
• Where do LLM outputs cross trust boundaries?
  This is not merely a developer problem; it is a visibility and governance failure. Without a clear inventory of where agents run and how data flows through them, organizations are effectively “flying blind” when critical advisories like this are released.

Cyata’s Role: The Control Plane for Agentic Identity

The research and disclosure regarding LangGrinch underscore the necessity for robust control planes in AI environments. Cyata, the organization behind the research, focuses on providing visibility, risk assessment, control, and governance for AI systems.

Visibility

To manage risk, organizations must first know what is running. This involves discovering agent runtimes and integrations across various environments, including IDEs, CI/CD pipelines, services, worker jobs, and hosted agents. Tracking the specific frameworks, packages, and versions in use is the first step toward security.

Risk Assessment

Not all vulnerabilities pose an equal risk to every organization. Effective risk assessment involves prioritizing threats based on real-world “blast radius.” Security teams need to triage quickly: Is the system internet-facing? Does it touch secrets? Does it run with elevated credentials?
High-risk paths involve untrusted content flowing into privileged contexts. Identifying where “structured fields” (like metadata or streamed events) cross trust boundaries is crucial for mitigating the impact of vulnerabilities like LangGrinch.

Control

Reducing exposure requires enforcing safer operational defaults before every dependency is fully patched. This includes enforcing isolation boundaries, least privilege access, and policy checks that scale across teams. Guardrails must be established to restrict risky patterns, such as deserializing untrusted data or running sensitive code paths in privileged workers.

Governance

Finally, governance ensures that safe agent usage is repeatable and auditable. Organizations should define policies for approved frameworks, versions, and configurations. Exceptions must be tracked with owners and rationales, and systems must be monitored for drift over time. An audit trail is essential for supporting security reviews and compliance.

Conclusion

The disclosure of the LangGrinch vulnerability serves as a stark reminder of the evolving nature of security in the age of AI. The vulnerability (CVE-2025-68664) in LangChain Core affects millions of installations and presents a critical risk of secret leakage and arbitrary object instantiation.
The response to this issue must be twofold. First, the technical mitigation: immediate patching to versions 1.2.5 or 0.3.81 and a restructuring of how LLM outputs are trusted within applications. Second, and perhaps more importantly, a shift in organizational approach toward AI governance.
As AI agents become deeply integrated into production systems, the “plumbing” of serialization and deserialization becomes a front-line defense. Organizations must invest in the visibility and control necessary to understand their AI stacks. When a major security advisory lands, the goal should not be panic, but a calm, controlled response backed by a real inventory of systems and enforced security guardrails.

Disclosure Timeline

• Report submitted via Huntr: December 4th, 2025
• Acknowledged by LangChain maintainers: December 5th, 2025
• Advisory and CVE published: December 24th, 2025
  The prompt action by the LangChain team in patching the vulnerability and hardening the default configurations is commendable. However, the onus now falls on the global community of developers and security professionals to implement these patches and reassess their trust boundaries in the agentic AI landscape.