Rethinking Indirect Prompt Injection as a Test-Time Search ProblemRethinking Indirect Prompt Injection as a Test-Time Search Problem
Computer Science > Artificial Intelligence [Submitted on 3 Sep 2026] Title:Rethinking Indirect Prompt Injection asComputer Science > Artificial Intelligence [Submitted on 3 Sep 2026] Title:Rethinking Indirect Prompt Injection as
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- Computer Science > Artificial IntelligenceComputer Science > Artificial Intelligence
- [Submitted on 3 Sep 2026][Submitted on 3 Sep 2026]
- Title:Rethinking Indirect Prompt Injection as a Test-Time Search ProblemTitle:Rethinking Indirect Prompt Injection as a Test-Time Search Problem
Computer Science > Artificial Intelligence
[Submitted on 3 Sep 2026]
Title:Rethinking Indirect Prompt Injection as a Test-Time Search Problem
View PDF HTML (experimental)Abstract: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 agentic security evaluations should characterize both the attacker's search procedure and compute budget, rather than treating attack success as a budget-independent property of the victim. More broadly, our findings identify the attacker's adaptive search over the system attack surfaces as an important and underexplored security risk for tool-using agents.
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Computer Science > Artificial Intelligence
[Submitted on 3 Sep 2026]
Title:Rethinking Indirect Prompt Injection as a Test-Time Search Problem
View PDF HTML (experimental)Abstract: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 agentic security evaluations should characterize both the attacker's search procedure and compute budget, rather than treating attack success as a budget-independent property of the victim. More broadly, our findings identify the attacker's adaptive search over the system attack surfaces as an important and underexplored security risk for tool-using agents.
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Connected Papers (What is Connected Papers?)
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scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
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arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
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