Summary
- Large language models (LLMs) are otherwise harmless and neutral.
- They can generate responses that may be unsafe or unethical.
- These potentially harmful responses often violate AI safety guidelines and can leak sensitive system information.
- This can be mitigated by safety guards that usually prevent unsafe responses.
- This provides LLM users with assurances that the responses they receive are safe and unbiased.
- Research shows that these safety measures are not entirely bulletproof.
- LLM jailbreaking can lead to unauthorized use of these systems.
- Attackers can embed unsafe content in LLM responses.
- One of the techniques is called the Bad Likert Judge.
- Obtaining detailed knowledge about an LLM’s weaknesses.
- This technique uses Likert scales to measure the harmfulness of content.
- Bad Likert Judge can increase the attack success rate by more than 60%.
- This highlights the huge impact of this technique in enhancing the effectiveness of jailbreak attempts across various language models.
Original Article