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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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41 entries

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

AutoDAN: Interpretable LLM Jailbreak

Published Analyzed Source: arXiv

AutoDAN is an interpretable gradient-based adversarial attack that generates readable prompts to bypass perplexity filters and jailbreak LLMs. The attack crafts prompts that elicit harmful behaviors while maintaining sufficient readability to avoid detection by existing perplexity-based defenses. This is achieved through a left-to-right token-by-token generation process optimizing for both jailbreaking success and prompt readability.

Autodan: Automatic and interpretable adversarial attacks on large language models
Evaluated models: GPT-3.5 Turbo, GPT-4, Guanaco 7B+4 more
  • GPT-3.5 Turbo
  • GPT-4
  • Guanaco 7B
  • Llama 2 Chat
  • Pythia 12B
  • Vicuna 13B
  • Vicuna 7B
Model layer
Injection
Jailbreak
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.