[
    {
        "id": "osp-24948",
        "type": "article-journal",
        "title": "TempQ-Jail: Query-Constrained Candidate Ranking for Text-to-Video Jailbreak Attacks",
        "author": [
            {
                "family": "Fang",
                "given": "Tianmeng"
            },
            {
                "family": "Wang",
                "given": "Jiancheng"
            },
            {
                "family": "Wang",
                "given": "Chen"
            },
            {
                "family": "Wang",
                "given": "Liming"
            },
            {
                "family": "Wang",
                "given": "Wei"
            },
            {
                "family": "Liu",
                "given": "Jiayang"
            },
            {
                "family": "Cao",
                "given": "Xiaochun"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/tempq-jail-query-constrained-candidate-ranking-for-text-to-video-jailbreak-attacks",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Existing text-to-video (T2V) jailbreak methods mainly seek more effective or stealthier attack candidates. In guarded T2V systems, however, video generation and security evaluation are costly, so an attacker often cannot test a large candidate pool. We therefore formulate T2V jailbreak as a query-constrained candidate allocation and ranking problem and propose TempQ-Jail. The method combines heterogeneous attack mechanisms to expand candidate coverage, estimates each candidate's end-to-end attack value from security-gate passage, dangerous visual generation, preservation of the original intent, and temporal validity, and ranks candidates so that high-value attacks appear early in a limited query trajectory. We evaluate TempQ-Jail on CogVideoX-5B using 70 common viable intents derived from T2VSafetyBench and compare it with six representative T2V jailbreak methods under a unified protocol. TempQ-Jail achieves TP-ASR@5 and TP-ASR@10 of 48.9% and 65.4%, improving over the strongest baselines by 4.6 and 4.0 percentage points, respectively. It also obtains the highest AUC-TP (0.469) and the lowest AvgQ (6.3). Analyses of query trajectories, candidate allocation, failure attribution, and ablations show that TempQ-Jail more effectively identifies and prioritises candidates with complete attack potential under limited query budgets."
    }
]