Papers with safety filters
Jailbreaking Attacks vs. Content Safety Filters: How Far Are We in the LLM Safety Arms Race? (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have focused on the models, neglecting the full deployment pipeline . previous studies have underestimated the practical success of these attacks . |
| Approach: | They evaluate the effectiveness of jailbreak attacks targeting LLM safety alignment . they highlight critical gaps and call for further refinement of detection accuracy and usability . |
| Outcome: | The proposed attacks can detect at least one safety filter across the entire deployment pipeline. |
Jailbreaking Safeguarded Text-to-Image Models via Large Language Models (2026.findings-eacl)
Copied to clipboard
| Challenge: | Text-to-image models generate harmful content when unsafe prompts are submitted . authors propose a method to jailbreak text-to image models with safety guardrails . |
| Approach: | They propose a method to jailbreak text-to-image models with safety guardrails . they use a fine-tuned large language model to generate adversarial prompts based on unsafe prompts. |
| Outcome: | The proposed method bypasses safety guardrails and outperforms existing no-box attacks . the proposed method generates adversarial prompts efficiently after fine-tuning the model . |
TombRaider: Entering the Vault of History to Jailbreak Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing jailbreak techniques focus on prompt manipulation or intent obfuscation to bypass safety filters. |
| Approach: | They propose a jailbreak technique that exploits the ability to store, retrieve, and use historical knowledge of Large Language Models (LLMs) they use an inspector agent to extract historical information and an attacker agent to generate adversarial prompts, enabling effective bypassing of safety filters. |
| Outcome: | The proposed jailbreak technique outperforms state-of-the-art jailbreak techniques on six popular models and maintains over 55.4% ASR against defence mechanisms. |