Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 21·5 min read
Exploring and Developing a Pre-Model Safeguard with Draft Models
TL;DR
Large Language Models (LLMs) are susceptible to jailbreak attacks that can produce unsafe outputs, highlighting the need for effective safety measures. A pre-model safeguard was developed to audit prompts before they reach the LLM, but it often misses dangerous inputs, resulting in high false-negative rates.
✦ Why It Matters
Engineers can implement both pre- and post-model safeguards to enhance the safety of LLM applications.
Key Takeaways
- Implement pre-model safeguards to assess prompt safety.
- Use post-model guards to evaluate model responses.
- Combine both approaches for improved safety.
- Regularly test for false negatives in safety measures.
Full Summary
Large Language Models (LLMs) can generate harmful content when manipulated through specific prompts, known as jailbreak attacks. To combat this, a pre-model safeguard was created to assess the safety of user prompts before they are processed by the LLM.
However, this approach has a significant limitation: it often fails to identify unsafe prompts, leading to a high rate of false negatives. To address this issue, post-model guards were implemented, which analyze both the initial user prompt and the LLM's generated response.
This dual-layered approach significantly enhances the detection of unsafe outputs. The study found that incorporating post-model audits reduced the false-negative rate, thereby increasing overall safety.
These findings suggest that a combination of pre- and post-model safeguards is essential for improving LLM alignment and safety.