Assessing Automated Prompt Injection Attacks in Agentic Environments
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 optimization substantially outperforms gradient-based methods, a gap we attribute to GCG's optimization instability under reasonable compute budgets. We ...
| Authors | David Hofer, Edoardo Debenedetti, Florian Tramèr |
|---|---|
| Primary category | cs.CR |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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