Rethinking Indirect Prompt Injection as a Test-Time Search Problem
We formulate indirect prompt injection as a test-time search over a task-dependent attack surface induced by the environment, user task, and injection task. To operationalize this formulation, we introduce an agentic attacker with a dedicated search harness that performs environment reconnaissance, structured reasoning over attack strategies, and adaptive evaluation using victim-agent feedback. Across heterogeneous tasks, we find that increasing attacker test-time compute improves vulnerability discovery and exploitation, while ablations show that explicit strategy management is important for avoiding redundant search and sustaining gains at larger budgets. These results suggest that agen...
| Authors | Duong M. Nguyen, Joon Sik Kim, Blazej Manczak et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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