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Mallory
HighPublic exploit

Insecure Deserialization RCE in LMDeploy torch.load()

IdentifiersCVE-2025-67729CWE-502· Deserialization of Untrusted Data

CVE-2025-67729 is an insecure deserialization vulnerability in LMDeploy, a toolkit for compressing, deploying, and serving large language models (LLMs). The vulnerability exists because the torch.load() function is called without the weights_only=True parameter when loading model checkpoint files. This allows an attacker to craft a malicious .bin or .pt model file that, when loaded by LMDeploy, results in arbitrary code execution on the victim's machine. The issue is present in versions prior to 0.11.1 and has been patched in version 0.11.1.

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ANALYST BRIEF

Impact, mitigation & remediation

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Impact

What an attacker gets, and what they’ve been doing with it.

Successful exploitation allows an attacker to execute arbitrary code on the system running LMDeploy, potentially leading to full system compromise, data theft, lateral movement, or further persistence mechanisms. The vulnerability is remotely exploitable if a victim loads a malicious model file.

Mitigation

If you can’t patch tonight, do this now.

Until the patch can be applied, avoid loading model checkpoint files (.bin or .pt) from untrusted sources. When loading models with torch.load(), explicitly set weights_only=True to prevent deserialization of arbitrary objects.

Remediation

Patch, then assume compromise.

Upgrade LMDeploy to version 0.11.1 or later, where the torch.load() function is called with weights_only=True, preventing code execution via deserialization. Ensure that only trusted model files are loaded.
PUBLIC EXPLOITS

Exploits

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VALID 0 / 0 TOTALView more in app

No public exploit code observed for this vulnerability.

EXPOSURE SURFACE

Affected products & vendors

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VendorProductType
InternlmLmdeployapplication

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Threat actor evidence

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Associated malware

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Detection signatures

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