Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents
TL;DR
Web agents using large language models (LLMs) face vulnerabilities from prompt-injection attacks that can harm different stakeholders in varied ways. To address this, a new benchmark called Stakeholder-Centric Prompt Injection Benchmarking (Sysname) was developed to categorize and assess these risks.
✦ Why It Matters
Engineers can use this benchmark to better assess and mitigate risks in LLM-based web agents.
Key Takeaways
- Adopt stakeholder-centric evaluation methods for web agents.
- Identify specific vulnerabilities in your LLM applications.
- Implement defenses against diverse prompt-injection attack patterns.
- Regularly assess the impact of attacks on different stakeholders.
How It Works
The Stakeholder-Centric Prompt Injection Benchmarking framework categorizes attacks based on their objectives and the stakeholders affected. It evaluates the effectiveness of these attacks using both outcome and process-level metrics, allowing for a comprehensive understanding of the risks involved.