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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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101 entries

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

Parametric Model Unalignment

Published Analyzed Source: arXiv

Large Language Models (LLMs) employing Reinforcement Learning from Human Feedback (RLHF) and instruction tuning methods may exhibit superficial safety guardrails vulnerable to parametric red-teaming attacks. Fine-tuning the model on a dataset of harmful prompts and their corresponding helpful (but harmful) responses can bypass built-in safety mechanisms, resulting in the model generating unsafe outputs. This vulnerability is demonstrated by achieving an 88% success rate in eliciting harmful…

Language model unalignment: Parametric red-teaming to expose hidden harms and biases
Evaluated models: Claude 1, Claude 2, GPT-4+6 more
  • Claude 1
  • Claude 2
  • GPT-4
  • Llama 2 7B Chat
  • Llama 2 13B Chat
  • Vicuna 1 7B
  • Vicuna 1 13B
  • Vicuna 2 7B
  • Vicuna 2 13B
Model layer
Jailbreak
Poisoning
Extraction

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.