Structured Visual Narratives Undermine Safety Alignment in Multimodal Large Language Models
This paper exposes a critical vulnerability in Multimodal Large Language Models (MLLMs): safety alignment fails when harmful intent is embedded in structured visual narratives. The authors introduce ComicJailbreak, a benchmark of 1,167 three-panel comics where panels 1–2 establish narrative context and panel 3 contains a blank speech bubble filled with a paraphrased harmful goal. The model is prompted to "complete the comic" by generating the fourth panel. Across 15 state-of-the-art MLLMs, comic-based attacks achieve ensemble success rates exceeding 90% on Gemini-family models and 85%+ on most open-source models—substantially outperforming plain-text and random-image baselines. The work also reveals that existing defenses (AdaShield, Attack as Defense) trigger severe over-refusal on benign prompts, and that automated safety judges are unreliable on sensitive-but-benign content.
The paper makes a solid contribution to multimodal AI safety by systematically demonstrating that narrative structure—not just visual perturbation—undermines alignment. The ComicJailbreak benchmark is well-designed with paired harmful/benign goals across 10 harm categories, and the human evaluation with κ = 0.751 supports the reliability of findings. However, the work is bounded by intentionally simple templates (three-panel, minimal visual complexity), limited English-only evaluation, and unresolved trade-offs between safety and helpfulness when defenses are applied. The core claim that "current safety evaluators can be unreliable on sensitive but non-harmful content" is well-supported by the data showing FPR = 0.234 and FNR = 0.422 on benign prompts.
The experimental design is rigorous: evaluation across 15 diverse MLLMs (6 commercial, 9 open-source), five task setups (article, code, instructional, message, speech), and a human annotation study of 2,869 outputs with double-annotation and adjudication. The ablation in Appendix D.1 isolates narrative modality effects, showing both text and visual narratives increase ASR over direct prompting. The paired benign/harmful evaluation is a methodological strength, enabling measurement of both unsafe compliance and over-refusal. The paraphrasing pipeline with manual intent-preservation review ensures goal fidelity.
First, the template design is intentionally minimal (Section 4.1.2: "visually simple and consistent across setups"), which limits ecological validity—real-world visual narratives have richer layouts, longer arcs, and stylized typography. The authors acknowledge this in limitations but do not test whether complexity actually matters. Second, the defense evaluation (Section 2.2) demonstrates severe trade-offs—AdaShield and AsD increase refusal rates on benign prompts by 80%+ for some models—but offers no path toward resolving this safety-helpfulness tension beyond noting it warrants "careful consideration." Third, the reliance on automated judges for the main results (despite showing they are brittle on benign prompts) introduces measurement risk, though the targeted human evaluation mitigates this partially.
The comparison to baselines is fair and comprehensive: plain-text attacks (direct harmful queries), rule-based text jailbreaks (refusal suppression combined with role framing), and random-image text overlays (text on unrelated meme images). The comic attacks achieve EASR of 90%+ on Gemini models versus 14.5–29% for plain-text and 21–86% for rule-based, demonstrating the unique effectiveness of narrative framing. The comparison to JailbreakBench and JailbreakV is appropriate—the authors build on these established benchmarks rather than claiming superiority. The defense comparison across AdaShield, AsD, and self-reflection shows consistent patterns: prompt-based defenses reduce ASR but increase RR, while self-reflection has modest effects.
Reproducibility is reasonably strong for a multimodal safety paper. The paper provides: complete prompt templates (Table 6), detailed comic generation procedures (Section 4.1.2, Appendix A), hyperparameters for API calls (temperature=1e-6, seed=42, max tokens 2048/4096), and the judging protocol with exact thresholds. The dataset construction pipeline is described with filtering criteria. However, the ComicJailbreak dataset itself is not released (no mention of release), commercial API results may vary with model updates, and the paraphrasing step involves manual review that cannot be exactly replicated. The human evaluation guidelines (Appendix G) are minimal but the adjudication procedure for disagreements is specified.
Multimodal Large Language Models (MLLMs) extend text-only LLMs with visual reasoning, but also introduce new safety failure modes under visually grounded instructions. We study comic-template jailbreaks that embed harmful goals inside simple three-panel visual narratives and prompt the model to role-play and "complete the comic." Building on JailbreakBench and JailbreakV, we introduce ComicJailbreak, a comic-based jailbreak benchmark with 1,167 attack instances spanning 10 harm categories and 5 task setups. Across 15 state-of-the-art MLLMs (six commercial and nine open-source), comic-based attacks achieve success rates comparable to strong rule-based jailbreaks and substantially outperform plain-text and random-image baselines, with ensemble success rates exceeding 90% on several commercial models. Then, with the existing defense methodologies, we show that these methods are effective against the harmful comics, they will induce a high refusal rate when prompted with benign prompts. Finally, using automatic judging and targeted human evaluation, we show that current safety evaluators can be unreliable on sensitive but non-harmful content. Our findings highlight the need for safety alignment robust to narrative-driven multimodal jailbreaks.
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