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arXiv cs.AI· Rongting Zhang, Martin Bertran, Sandeep Avula, Chia-Jung Lee, Vanessa Murdock·· 12 小时前AI 评分42

超越指令覆盖:用任务篡改攻击评测间接提示注入

Beyond Instruction Override: Benchmarking Indirect Prompt Injection with Task-Tampering Attacks

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研究者提出按“权限”而非指令式措辞定义间接提示注入(IPI):植入内容试图影响其来源无权决定的决策即构成攻击。新基准覆盖三个工具使用环境的 90 个验证案例,攻击篡改良性任务所需的决策而非替换任务,可针对智能体的目标、决策规则、可用动作、预期后果、观测解读或状态信念六处。

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Abstract:Indirect prompt-injection (IPI) benchmarks predominantly test planted instructions that redirect a tool-using agent toward an attacker-chosen task. Yet attacker-controlled content can instead tamper with the outcome of the task already in progress. A buyer's dispute evidence can cite a false refund policy to inflate a refund without changing the task. We define IPI by authority rather than instruction-like wording: planted content constitutes an attack when it attempts to influence a decision that its source is not authorized to determine. We introduce a benchmark that makes this boundary explicit: in each of 90 validated cases across three tool-use environments, the user request specifies the source that resolves the targeted decision. Every attack tampers with a decision required by the benign task rather than replacing that task. Each case has attacks with the same harmful outcome that target up to six parts of the agent's decision making: its objective, decision rules, available actions, expected consequences, reading of observations, or beliefs about the current state. Without a defense, attacks on three of the five other targets (decision rules, available actions, and state) have higher mean success than objective attacks across five LLMs. A detection-and-removal defense reduces objective-targeting attacks to 2.4% success but leaves observation- and state-targeting attacks at 9.5% and 12.1%; this gap persists under detection-optimized rewrites. A tool-policy defense still allows 15.4% attack success, close to 17.0% without a defense, because these attacks generally retain expected tools and manipulate values obtained during execution. Evaluations whose attacks redirect the task or target only the agent's objective can therefore overestimate the coverage of these defenses. Code and data are available at this https URL.
Comments: 34 pages, 6 figures. Code and data: this https URL
Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
Cite as: arXiv:2611.07640 [cs.CR]
  (or arXiv:2611.07640v1 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2611.07640

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Rongting Zhang [view email]
[v1] Thu, 5 Nov 2026 08:25:27 UTC (377 KB)

来源:arXiv cs.AI · arxiv.org