arXiv cs.AI· Yang Luo, Tiantian Ji, Zifeng Kang, Yucheng Wang, Yong Liu, Zhiyi Yin, Xinran Liu·· 3 天前AI 评分46
JevProbe：揭示真实世界 Jev 应用中的程序决策劫持
Nudge the Judge: Uncovering Program Decision Hijacking in Real-World Jev Applications
AI 导读
研究者提出 JevProbe，首个自动化评估 Jev Program Decision Hijacking（JPDH）的框架，追踪攻击者从可达上下文到程序决策再到下游安全后果的影响链。
正文 · 原文
Abstract:System One models such as Jev change how software makes decisions by mapping open-ended semantic contexts into structured judgments that directly drive program execution. However, this paradigm introduces a new security risk, which we term Jev Program Decision Hijacking (JPDH): attacker-controlled contexts can redirect program decisions through schema-valid Jev judgments. Existing evaluations focus primarily on model-level attacks or predefined injection channels, leaving attack reachability and downstream security consequences in real-world Jev applications underexplored.
In this paper, we present JevProbe, the first automated framework to evaluate JPDH by tracing adversarial influence from reachable context surfaces through program-consumed decisions to downstream security consequences in real-world Jev applications. Our key insight is that application logic maps diverse Jev judgments to observable decisions, enabling systematic, program-grounded attack evaluation. Across 228 popular real-world Jev applications and 2,273 test cases, 81.5% exhibit decision flips, of which 98.6% lead to potential downstream security impacts. Compared with the strongest adapted baseline, JevProbe improves macro-average Decision Flip Rate (DFR) by 50.4 percentage points, while existing defenses offer limited protection. We further construct JevProbeBench to support systematic evaluation of JPDH in real-world Jev applications. We have responsibly disclosed our findings to Jev application maintainers, pending their responses. The source code of JevProbe and the JevProbeBench dataset are publicly available on GitHub (this https URL).
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.18612 [cs.CR] |
| (or arXiv:2610.18612v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18612 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yang Luo [view email]
[v1]
Tue, 13 Oct 2026 15:59:31 UTC (728 KB)
来源：arXiv cs.AI · arxiv.org