Prompt injection remains top LLM vulnerability, new research shows

A new paper from arXiv details prompt injection attacks against open-source LLMs, finding that models like Stablelm2, Mistral, and Vicuña are highly vulnerable. The research proposes an Attack Success Probability (ASP) metric to better evaluate these attacks, which can achieve around 90% success. Separately, a dev.to article highlights that prompt injection remains the top vulnerability for LLMs, according to OWASP, and is an architectural, not just a prompt engineering, problem. This vulnerability is particularly concerning as AI agents are increasingly given access to real-world tools. AI

IMPACT Prompt injection remains a critical security flaw, necessitating architectural changes rather than simple prompt fixes for AI systems.

RANK_REASON The cluster focuses on a research paper detailing vulnerabilities in LLMs and a related article discussing these vulnerabilities in the context of security standards.

Read on arXiv cs.CL →

• Amber Forrest
• IBM Think
• Kevin Liu
• Kunal Ganglani
• LLM
• Matthew Kosinski
• Microsoft Bing Chat
• OWASP
• Prompt injection
• arXiv
• Hugging Face
• Jiawen Wang
• Mistral AI
• Openchat
• Stablelm2
• Vicuña

• safety
• paper
• policy

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