AutoDojo: A Generative Benchmark for Evaluating Prompt Injection Defenses in LLM Agents
Indirect prompt injection (IPI) is a major security threat to LLM-powered agents. Thus, a growing body of work has proposed a variety of defensive approaches against IPI which can be grouped into three broad categories: prompt-level, filter-based, and system-level designs. However, commonly used benchmarks for evaluating defense, such as AgentDojo, are \emph{inherently static}, generating a fixed distribution of IPI attacks. Consequently, a defense can score well on them without being robust to adaptive threats. We introduce \textbf{AutoDojo}, a generative benchmark built on AgentDojo and AgentDyn that generates IPI \emph{adaptively} for a given agent and defense. It supports six task sui...
| Authors | Xinhang Ma, Taoran Li, Chaowei Xiao et al. |
|---|---|
| Primary category | cs.CR |
| Published | 13 June 2026 |
| Last updated at source | 28 September 2026 |
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