Quantization Contrastive Jailbreak
Published Analyzed Source: arXiv
A malicious model supply chain vulnerability exists involving a technique termed Adversarial Contrastive Learning (ACL) for Large Language Model (LLM) quantization attacks. This vulnerability allows an attacker to publish a model that appears benign and preserves high utility in full precision (e.g., BF16 or FP32) but exhibits malicious behaviors—such as jailbreak, over-refusal, or advertisement injection—immediately upon zero-shot quantization (e.g., INT8, FP4, or NF4).
Adversarial Contrastive Learning for LLM Quantization Attacks
Evaluated models: Qwen 2.5 1.5B Instruct, Qwen 2.5 3B Instruct, Llama 3.2 1B Instruct+1 more
- Qwen 2.5 1.5B Instruct
- Qwen 2.5 3B Instruct
- Llama 3.2 1B Instruct
- Llama 3.2 3B Instruct