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Search Results (10 CVEs found)
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2026-92971 | 1 Internlm | 1 Lmdeploy | 2026-09-17 | 7.5 High |
| 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_ids list to trigger an AssertionError that crashes the engine loop and causes subsequent inference requests to fail. | ||||
| CVE-2026-92983 | 1 Internlm | 1 Lmdeploy | 2026-09-17 | 7.5 High |
| 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 the proxy endpoint that accumulate unreleased scheduler metadata and memory until the prefill worker is out-of-memory killed. | ||||
| CVE-2026-46517 | 1 Internlm | 1 Lmdeploy | 2026-08-31 | 7.8 High |
| 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. | ||||
| CVE-2026-76850 | 1 Internlm | 1 Lmdeploy | 2026-08-20 | 9.8 Critical |
| 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.loads(), and the isinstance check against DistServeCacheFreeRequest runs only after deserialization has already completed. The peer that supplies those bytes is caller-controlled: p2p_connect passes remote_engine_endpoint_info.zmq_address from the request body to connect() on the ZMQ PULL socket, and the POST /distserve/p2p_initialize and /distserve/p2p_connect endpoints in lmdeploy/serve/openai/api_server.py apply no authentication unless the server is started with api_keys, which defaults to None. A remote attacker can direct an engine to pull from a ZMQ endpoint under their control and execute arbitrary code in the engine process. Deployments that do not enable disaggregated serving are not affected, because the receive loop is only started once the migration backend accepts the connection. | ||||
| CVE-2026-63764 | 1 Internlm | 1 Lmdeploy | 2026-07-23 | 8.6 High |
| 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-validating hosts after HTTP redirects. An unauthenticated attacker can submit a crafted image_url to the chat completions endpoint pointing to an attacker-controlled host that returns a redirect to a private IP or cloud-metadata endpoint, causing the server to follow the redirect and expose internal service content through the model pipeline. | ||||
| CVE-2026-46432 | 1 Internlm | 1 Lmdeploy | 2026-06-10 | 7.8 High |
| 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. At time of publication, there are no publicly available patches. | ||||
| CVE-2026-33626 | 1 Internlm | 1 Lmdeploy | 2026-04-23 | 7.5 High |
| 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` fetches arbitrary URLs without validating internal/private IP addresses, allowing attackers to access cloud metadata services, internal networks, and sensitive resources. Version 0.12.3 patches the issue. | ||||
| CVE-2025-67729 | 1 Internlm | 1 Lmdeploy | 2025-12-31 | 8.8 High |
| 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. This allows an attacker to execute arbitrary code on the victim's machine when they load a malicious .bin or .pt model file. This issue has been patched in version 0.11.1. | ||||
| CVE-2025-3162 | 1 Internlm | 1 Lmdeploy | 2025-04-23 | 5.3 Medium |
| 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 deserialization. Attacking locally is a requirement. The exploit has been disclosed to the public and may be used. | ||||
| CVE-2025-3163 | 1 Internlm | 1 Lmdeploy | 2025-04-23 | 5.3 Medium |
| 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 attack on the local host. The exploit has been disclosed to the public and may be used. | ||||
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