Agent Data Injection Attacks are Realistic Threats to AI Agents
AI agents act on behalf of user prompts, consuming external data and taking actions based on the agent context. Prior research on AI agent security has primarily focused on indirect prompt injection (IPI). Its most well-studied category is instruction injection, where attacker-controlled untrusted data is interpreted as an instruction. In response, many mitigations have been proposed to prevent instruction injection attacks. In this paper, we introduce a new category of IPI, agent data injection attacks (ADI). ADI injects malicious data disguised as trusted data, such as security-critical metadata (e.g., resource identifiers or data origins) or agent context data (e.g., tool call and resp...
| Authors | Woohyuk Choi, Juhee Kim, Taehyun Kang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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