提出新型安全威胁模型，揭示智能体反噬部署者的核心风险
Owner-Harm: A Missing Threat Model for AI Agent Safety
- 构建Owner-Harm威胁模型，涵盖8类部署者伤害行为
- 检测准确率仅14.8%（4/27），暴露现有防御系统严重缺陷
- 提出符号-语义泛化框架，揭示上下文缺失会放大3.4倍检测差距
现有AI智能体安全评测聚焦通用犯罪危害（如网络犯罪、骚扰、武器合成），忽视了一类独特且具有商业影响的威胁：智能体伤害其自身部署者。真实事件凸显这一缺口：2024年8月Slack AI凭据泄露、2024年1月Microsoft 365 Copilot日程注入漏洞、2026年3月Meta智能体未经授权发布操作数据。本文提出Owner-Harm正式威胁模型，包含8类损害部署者的智能体行为。在两个基准测试中量化防御差距：组合式安全系统在AgentHarm任务上达100%真阳性率（TPR）/0%假阳性率（FPR），但在AgentDojo注入任务（提示注入引发的部署者伤害）中仅14.8%（4/27；95%置信区间：5.9%-32.5%）。对照实验显示该差距非源于Owner-Harm本质特性（62.7% vs. 59.3%，差值3.4个百分点），而是因环境绑定的符号规则无法跨工具词汇表泛化。在后验300场景的Owner-Harm基准上，仅使用门控机制即达75.3% TPR / 3.3% FPR；加入确定性事后审计验证器后，总体TPR升至85.3%（+10.0个百分点），劫持检测从43.3%提升至93.3%，体现多层互补优势。引入符号-语义防御泛化（SSDG）框架，关联信息覆盖度与检测率。两项实验部分验证其有效性：上下文剥夺使检测差距扩大3.4倍（相关系数R=3.60 vs. R=1.06）；上下文注入表明，有效检测依赖结构化目标-动作对齐，而非文本拼接。
原文摘要 · Abstract (English)
Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a distinct and commercially consequential threat category: agents harming their own deployers. Real-world incidents illustrate the gap: Slack AI credential exfiltration (Aug 2024), Microsoft 365 Copilot calendar-injection leaks (Jan 2024), and a Meta agent unauthorized forum post exposing operational data (Mar 2026). We propose Owner-Harm, a formal threat model with eight categories of agent behavior damaging the deployer. We quantify the defense gap on two benchmarks: a compositional safety system achieves 100% TPR / 0% FPR on AgentHarm (generic criminal harm) yet only 14.8% (4/27; 95% CI: 5.9%-32.5%) on AgentDojo injection tasks (prompt-injection-mediated owner harm). A controlled generic-LLM baseline shows the gap is not inherent to owner-harm (62.7% vs. 59.3%, delta 3.4 pp) but arises from environment-bound symbolic rules that fail to generalize across tool vocabularies. On a post-hoc 300-scenario owner-harm benchmark, the gate alone achieves 75.3% TPR / 3.3% FPR; adding a deterministic post-audit verifier raises overall TPR to 85.3% (+10.0 pp) and Hijacking detection from 43.3% to 93.3%, demonstrating strong layer complementarity. We introduce the Symbolic-Semantic Defense Generalization (SSDG) framework relating information coverage to detection rate. Two SSDG experiments partially validate it: context deprivation amplifies the detection gap 3.4x (R = 3.60 vs. R = 1.06); context injection reveals structured goal-action alignment, not text concatenation, is required for effective owner-harm detection.
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