Refactoring for TGL integration (including Phoenix, FA4, and cross-node support)#7
Refactoring for TGL integration (including Phoenix, FA4, and cross-node support)#7
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…d sent to draft process
… flashinfer dependency
…s in cross-node case
avnermay
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| self.block_size = config.kvcache_block_size | ||
| self.enforce_eager = config.enforce_eager | ||
| self.tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_path if config.tokenizer_path else config.model, use_fast=True) | ||
| self.tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_path if config.tokenizer_path else config.model, use_fast=True, trust_remote_code=True) |
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Static Code Analysis Risk: Together python huggingface trust remote code
trust_remote_code=True downloads and executes arbitrary Python code from the model repository without sandboxing (OWASP LLM03:2025 Supply Chain). A malicious or compromised model repo can achieve RCE on every host that loads the model (CWE-94). Pin to a verified commit hash and audit remote code before use, or use models that don't require trust_remote_code.
Severity: High 🚨
Status: Open 🔴
References:
- https://cwe.mitre.org/data/definitions/94
- https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained
- https://genai.owasp.org/llmrisk/llm032025-supply-chain/
- https://hiddenlayer.com/research/weaponizing-machine-learning-models-with-ransomware/
Suggested reviewers 🧐: @avnermay
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