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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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2 entries

Matches every word across titles, descriptions, sources, affected systems, and models.

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
Model layer
Infrastructure
Poisoning
Jailbreak

Life-Cycle Router Misrouting

Published Analyzed Source: arXiv

Predictive Large Language Model (LLM) routers, specifically those utilizing Deep Neural Network (DNN) and Matrix Factorization (MF) architectures, are vulnerable to adversarial manipulation and backdoor poisoning. These routers are designed to optimize cost and latency by dynamically directing simple queries to "weak" (cheap) models and complex queries to "strong" (expensive) models. Attackers can exploit this mechanism in two ways: 1. Inference-time Attacks: By appending specific adversarial…

Life-Cycle Routing Vulnerabilities of LLM Router
Evaluated models: Not reported
Model layer
Infrastructure
Prompt layer
Poisoning

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.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.