arXiv：cs.AI· Yang Luo, Tiantian Ji, Zifeng Kang, Yucheng Wang, Yong Liu, Zhiyi Yin, Xinran Liu·· 20 小时前
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
JevProbe 是首个自动化评估 Jev 程序决策劫持（JPDH）的框架，通过追踪对抗性影响从可达上下文表面到程序消费决策再到下游安全后果。在 228 个流行 Jev 应用和 2,273 个测试用例中，81.5% 出现决策翻转，其中 98.6% 可能导致潜在下游安全影响。相比最强适配基线，JevProbe 将宏平均决策翻转率（DFR）提升 50.4 个百分点，现有防御措施保护有限。
正文
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