Vulnerabilidades de Sgl-project

A security vulnerability has been detected in sgl-project SGLang up to 0.5.19. Affected by this issue is the function Session.create_req of the file python/sglang/srt/session/session_controller.py of the component Session Cleanup. Such manipulation leads to denial of service. The attack may be performed from remote. The exploit has been disclosed publicly and may be used. The pull request to fix this issue awaits acceptance.

A weakness has been identified in sgl-project SGLang up to 0.5.19. Affected by this vulnerability is an unknown functionality of the file python/sglang/srt/managers/io_struct.py of the component generate Endpoint. This manipulation of the argument session_params causes denial of service. The attack is possible to be carried out remotely. The exploit has been made available to the public and could be used for attacks. The project was informed of the problem early through an issue report but has not responded yet.

A vulnerability was determined in sgl-project SGLang up to 0.5.11. Affected by this vulnerability is the function data_hash of the component Cache Handler. This manipulation causes denial of service. The attack is restricted to local execution. A high degree of complexity is needed for the attack. The exploitation appears to be difficult. The exploit has been publicly disclosed and may be utilized. The pull request to fix this issue awaits acceptance.

A vulnerability was detected in sgl-project SGLang up to 0.5.9. Impacted is the function get_tokenizer of the file python/sglang/srt/utils/hf_transformers_utils.py of the component HuggingFace Transformer Handler. The manipulation of the argument trust_remote_code with the input False as part of Boolean results in code injection. The attack can be executed remotely. A high complexity level is associated with this attack. The exploitability is considered difficult. In get_tokenizer(), when the caller passes trust_remote_code=False and HuggingFace transformers v5 returns a TokenizersBackend instance (the generic fallback for tokenizer classes not in the registry), SGLang silently re-invokes AutoTokenizer.from_pretrained with trust_remote_code=True, overriding the caller's explicit security setting. A model repository containing a malicious tokenizer.py referenced via auto_map in tokenizer_config.json will execute arbitrary Python in the SGLang process during this second call. No log line or warning is emitted. The override affects all current SGLang versions because transformers==5.3.0 is pinned in pyproject.toml. Both tokenizer_mode="auto" and tokenizer_mode="slow" are affected. The exploit is now public and may be used. The vendor was contacted early about this disclosure but did not respond in any way.