LMDeploy vulnerabilities
15 known vulnerabilities in LMDeploy from InternLM. 15 are rated high or critical. Each entry lists the affected versions and the release that fixes it, where the vendor has published one.
Latest first
- 7.5 High severity
lmdeploy: Server-Side Request Forgery
LMDeploy is a toolkit for compressing, deploying, and serving large language models. From 0.12.3 until 0.15.0, _is_safe_url validates remote media URLs with urlparse while requests.Session().get interprets backslashes differently when sending the request. An a
- 9.8 Critical severity
lmdeploy: Deserialization of Untrusted Data
InternLM LMDeploy 0.9.0 through 0.18.0 contains an unauthenticated remote code execution vulnerability that allows remote attackers to run OS commands by sending pickled data to the /update_weights endpoint. Attackers can submit a base64-encoded crafted pickle
- 8.8 High severity
lmdeploy: Uncontrolled Resource Consumption
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 publi
- 9.8 Critical severity
lmdeploy: Deserialization of Untrusted Data
LMDeploy is a toolkit for compressing, deploying, and serving large language models. Starting in version 0.9.2 and prior to version 0.16.0, LMDeploy's PyTorch DistServe/PD-disaggregation control plane used `recv_pyobj()` to deserialize messages received throug
- 7.5 High severity
lmdeploy: Missing Release of Resource after Effective Lifetime
InternLM LMDeploy through 0.17.0 in DistServe prefill/decode disaggregation mode fails to release scheduler sessions because the proxy uses user-facing session IDs instead of internal scheduler keys. Unauthenticated attackers can send completion requests to th
- 7.5 High severity
lmdeploy: Reachable Assertion
InternLM LMDeploy through 0.17.0 contains a reachable assertion vulnerability in the DistServe decode migration loop that allows unauthenticated attackers to terminate the inference engine. Attackers can submit a migration_request with an empty remote_block_id
- 9.8 Critical severity
lmdeploy: Deserialization of Untrusted Data
LMDeploy is a toolkit for compressing, deploying, and serving large language models. Starting in version 0.9.1 and prior to version 0.10.2, the LMdeploy implements an rpc server (AsyncRPCServer in zmq_rpc.py) for supporting the RPC communications. In its core
- 9.8 Critical severity
lmdeploy: Deserialization of Untrusted Data
LMDeploy deserializes disaggregated-serving peer messages with pickle. The handle_zmq_recv coroutine in lmdeploy/pytorch/disagg/conn/engine_conn.py reads peer-to-peer cache-free requests with recv_pyobj(), which deserializes the received bytes with pickle.load
- 8.6 High severity
lmdeploy: Server-Side Request Forgery
LMDeploy through 0.14.0, fixed in commit 03c3130, contains a server-side request forgery (SSRF) vulnerability in the _load_http_url function within the connection.py media handler, where the private-IP guard validates only the original URL without re-validatin
- 7.8 High severity
lmdeploy: Improper Control of Generation of Code
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.
- 7.8 High severity
lmdeploy: Improper Control of Generation of Code
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. A
- 7.5 High severity
lmdeploy: Server-Side Request Forgery
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` fe
- 8.8 High severity
lmdeploy: Deserialization of Untrusted Data
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.
- 7.8 High severity
LMDeploy: Improper Control of Generation of Code
A vulnerability was found in InternLM LMDeploy up to 0.7.1. It has been declared as critical. Affected by this vulnerability is the function Open of the file lmdeploy/docs/en/conf.py. The manipulation leads to code injection. It is possible to launch the attac
- 7.8 High severity
LMDeploy: Deserialization of Untrusted Data
A vulnerability was found in InternLM LMDeploy up to 0.7.1. It has been classified as critical. Affected is the function load_weight_ckpt of the file lmdeploy/lmdeploy/vl/model/utils.py of the component PT File Handler. The manipulation leads to deserializatio