arXiv cs.AI· Rongting Zhang, Martin Bertran, Sandeep Avula, Chia-Jung Lee, Vanessa Murdock·· 4 小时前AI 评分49
超越指令覆盖:用任务篡改攻击基准测试间接提示词注入
Beyond Instruction Override: Benchmarking Indirect Prompt Injection with Task-Tampering Attacks
AI 导读
arXiv 论文提出以“权限”而非指令措辞定义间接提示词注入(IPI),并发布包含 90 个验证案例、覆盖三个工具使用环境的任务篡改攻击基准。在 5 个 LLM 上,针对决策规则、可用动作和状态的攻击平均成功率高于针对目标的攻击;检测-移除防御将目标类攻击降至 2.4%,但观察和状态类攻击仍有 9.5% 和 12.1%,工具策略防御下仍有 15.4% 成功率(无防御为 17.0%)。
正文 · 原文
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