Updated 7/21/2026

Language Model Security Database

Explore primary-source AI security research, evaluated models, attack techniques, and defensive evidence.

959 research findings · 1077 evaluated models

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Quantization Contrastive Jailbreak

Updated 1/14/2026

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

Affects: Qwen 2.5 1.5B Instruct, Qwen 2.5 3B Instruct, Llama 3.2 1B Instruct +1 more

Source: arXiv

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.