arXiv:2505.14368v2 Announce Type: replace-cross Abstract: Recent studies demonstrate that Large Language Models (LLMs) are vulnerable to attacks that generate harmful or sensitive outputs. As open-source LLMs are increasingly adopted in high-impact applications such as finance, law, and healthcare, systematically investigating their security risks is becoming increasingly important towards trustworthy LLM era. This paper comprehensively studies effective prompt injection attacks against 14 widely used open-source and three closed-source LLMs on five attack benchmarks. Moreover, existing evalua
The increasing adoption of open-source LLMs in critical applications necessitates a immediate systematic investigation into their security vulnerabilities, especially as they move into production environments.
This research highlights significant security risks in widely used open-source LLMs, which are foundational to many AI initiatives across finance, law, and healthcare, impacting trust and reliability.
The understanding of open-source LLMs' vulnerability to prompt injection attacks shifts from theoretical concern to empirically validated widespread risk, requiring immediate defensive measures and new evaluation standards.
- · Cybersecurity firms specializing in AI
- · Developers of robust red-teaming tools
- · Organizations prioritizing AI safety and security
- · Organizations deploying vulnerable open-source LLMs without proper safeguards
- · LLM developers not prioritizing security-by-design
- · Users unknowingly exposed to compromised AI outputs
Increased investment in AI security research and development of countermeasures against prompt attacks for open-source LLMs.
New regulatory guidelines and industry best practices will emerge for the secure deployment and ongoing auditing of LLM applications, especially in critical sectors.
A potential bifurcation in the LLM market could occur, with a premium placed on 'secure by design' models, influencing adoption curves and vendor selection.
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