arXiv:2606.10525v1 Announce Type: cross Abstract: Indirect prompt injection poses a critical threat to LLM agents that interact with untrusted external data, yet automated attack methods--proven effective for jailbreaking--remain underexplored in realistic agentic settings. We present a comprehensive empirical evaluation of automated prompt injection attacks against LLM agents, adapting both white-box (GCG) and black-box (TAP) methods to the agentic setting within the AgentDojo framework. We evaluate across 80 task pairs spanning four domains and multiple models, and find that black-box optimi
The rapid deployment and increasing autonomy of AI agents necessitate immediate attention to their vulnerabilities, particularly prompt injection, which is a known attack vector in LLMs.
This research provides a foundational understanding of critical security risks in autonomous AI systems, which could undermine trust and functionality across various applications including enterprise and defense.
The understanding of AI agent security shifts from theoretical to empirically validated, highlighting the urgency for robust defense mechanisms before widespread agentic AI adoption.
- · AI security researchers
- · Cybersecurity firms
- · Developers of secure AI frameworks
- · LLM agents with untrusted external data
- · Organizations relying on insecure AI agents
- · Developers neglecting security-by-design
Mass adoption of AI agents could be delayed or constrained by security concerns, leading to increased investment in defensive AI technologies.
Government and industry will likely accelerate the development of standards and regulations for AI agent security, potentially creating a new compliance burden.
A 'security race' emerges in AI development, similar to the cybersecurity landscape, influencing the competitive advantage of AI providers.
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Read at arXiv cs.AI