AgentTrace：基于执行级指纹的黑盒 LLM 智能体模型溯源框架
AgentTrace: Execution-Level Fingerprinting of Black-Box LLM Agents
AI 导读
研究提出 AgentTrace，一种通过观察智能体执行轨迹识别其背后基础模型的主动溯源框架。它注入结构歧义探针触发 tokenization 敏感的序列化失败，并用轻量 Markov 链建模后续修复序列，在对抗性身份伪装下达到 96.0% 归因准确率，还能区分共享相同 tokenizer 的同家族变体（如 Llama-3 8B/70B），对提示词混淆和温度缩放保持稳健。
正文 · 原文
Abstract:Verifying the provenance of autonomous LLM agents is a critical security challenge. Existing semantic attribution is easily obfuscated by system prompts, while active watermarking conflicts with schema-constrained tool generation. We propose AgentTrace, an active framework that identifies the foundation model behind a black-box agent via observable execution traces. AgentTrace injects structurally ambiguous probes to trigger tokenization-sensitive serialization failures at rigid tool interfaces. While these initial failures provide coarse-grained separation across model families, the temporal sequence of subsequent repair operations reveals fine-grained, model-specific recovery behaviors. By modeling these multi-step trajectories with lightweight Markov chains, AgentTrace achieves 96.0% attribution accuracy under adversarial identity spoofing. Crucially, sequential repair modeling successfully distinguishes the evaluated intra-family variants sharing identical tokenizers (e.g., Llama-3 8B/70B), substantially outperforming unordered behavioral statistics while remaining robust against prompt obfuscation and temperature scaling.
| Comments: | 10 pages, 4 figures |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32092 [cs.CR] |
| (or arXiv:2610.32092v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32092 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Gejian Zhao [view email]
[v1]
Fri, 23 Oct 2026 02:55:15 UTC (234 KB)
来源：arXiv cs.AI · arxiv.org