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High 7.5
LMDeploy has an SSRF bypass
### Summary The URL checking logic in lmdeploy has a logical flaw that could be bypassed by attackers, leading to SSRF attacks. ### Details The current lmdeploy project uses `_is_safe_url` to validate the input URL. The main logic is to perform security check…
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High 8.8
LMDeploy vulnerable to arbitrary code execution via eval() of untrusted quant_dtype in model config loading
LMDeploy is a toolkit for compressing, deploying, and serving large language models. Versions 012.1 through 0.12.2 contain a code injection vulnerability in `lmdeploy/pytorch/config.py` line 620 that allows an attacker to execute arbitrary Python code by publ…
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High 7.8
LMDeploy: Hardcoded trust_remote_code=True is an implicit unsafe remote-code load path with no user opt-out
LMDeploy is a toolkit for compressing, deploying, and serving large language models. In versions 0.12.3 and prior, hardcoded "trust_remote_code=True" enables HF supply-chain RCE without user opt-in. Version 0.13.0 patches the issue.
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High 7.8
LMDeploy: Arbitrary code execution via hardcoded trust_remote_code=True in lmdeploy model initialization
LMDeploy is a toolkit for compressing, deploying, and serving large language models. In versions 0.12.3 and prior, LMDeploy is vulnerable to arbitrary code execution through hardcoded "trust_remote_code=True" in multiple HuggingFace model-loading call sites. …
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High 7.5
LMDeploy Vulnerable to Server-Side Request Forgery (SSRF) via Vision-Language Image Loading
LMDeploy is a toolkit for compressing, deploying, and serving large language models. Versions prior to 0.12.3 have a Server-Side Request Forgery (SSRF) vulnerability in LMDeploy's vision-language module. The `load_image()` function in `lmdeploy/vl/utils.py` f…
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High 8.8
lmdeploy vulnerable to Arbitrary Code Execution via Insecure Deserialization in torch.load()
LMDeploy is a toolkit for compressing, deploying, and serving LLMs. Prior to version 0.11.1, an insecure deserialization vulnerability exists in lmdeploy where torch.load() is called without the weights_only=True parameter when loading model checkpoint files.…