The Framing Gap: Indirect Prompt-Injection Exfiltration Defeats Surface-Level Defenses in Tool-Using Agents

📅 2026-08-27
📈 Citations: 0
Influential: 0
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🤖 AI Summary
研究解决了工具使用型LLM代理在读取攻击者控制的内容时可能泄露秘密的问题,通过分析不同防御方法的有效性,发现限制目的地或隔离能力是有效的解决策略。
📝 Abstract
A tool-using LLM agent that reads attacker-controlled web content while holding a secret faces indirect prompt injection: the content may make it exfiltrate the secret. In a safe synthetic lab (canary secret, mock tools, matched clean-vs-poisoned metric) we report the framing gap: across six models, ten overt injection classes are refused (gpt-4o 0%), but reframing the identical leak as a mandatory integrity signature, config field, or look-alike "trusted" host drives gpt-4o 0% to 100%. The attack is cheap, and its cost is three-level: paraphrasing a known mechanism is trivial (96% at 3 wordings), swapping the field inside a known-effective template is also cheap (up to 60%), while authoring a fresh page around a new mechanism is hard (0/130) -- the reusable asset is the template, not the mechanism. An ablation shows the mechanism is instruction/data confusion, not defeated alignment: removing the confidentiality policy leaves base attacks at 0% and moves reframing only 31.9% to 38.1%. What closes the gap is payload-blind checks: a destination allow-list (0%, when destinations are closed) and a capability-isolating planner/reader split (0%). A broad "in any form" policy clause also closes it at the acting model (to 0%) but is brittle (dropping the catch-all reopens it to 48.8%). A published fine-tuning defense (SecAlign, CCS 2025) does not close it on a tool agent (32.5%, positive-control-validated), nor does channel separation (38.8%); an output-normalizing guard loses to a held-out encoding (ROT13, 100%). Robustness comes from constraining the destination or isolating the capability, not from the acting model recognizing the attack.
Problem

Research questions and friction points this paper is trying to address.

indirect prompt injection
tool-using LLM agent
secret exfiltration
framing gap
surface-level defenses
Innovation

Methods, ideas, or system contributions that make the work stand out.

indirect prompt injection
framing gap
destination allow-list
planner/reader split
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M
Md Habibur Rahman
Dept. of AI Convergence Engineering, Gyeongsang National University, Jinju, South Korea
Jaeho Kim
Jaeho Kim
Dept. of AI Convergence Engineering, Gyeongsang National University, Jinju, South Korea