Writing study: When the Prompt Becomes Visual: Vision-Centric Jailbreak Attacks for Large Image Editing Models
← ICML 2026 oral writing corpus
Jiacheng Hou, Yining Sun, Ruochong Jin, Haochen Han, Fangming Liu, Wai Kin Victor Chan, Alex Jinpeng Wang · Official ICML 2026 oral record
Figure, table and section ledger
Editorial contribution type: Datasets, benchmarks & measurement (primary).
Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.
- Canonical PDF
- 27 physical pages
- Before references / appendix
- ≈ 8.31 page-equivalents
- Main figures / tables
- 7 / 3
- Attached appendix figures / tables
- 12 / 5
- Attached appendix span
- ≈ 15.91 page-equivalents
Source-bound correction: Join wrapped printed heading text using adjacent source lines; original heading start coordinates are unchanged.
Section-by-section lengths and source pages
The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.
| Top-level heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1. Introduction | 1 | ≈ 1.32 pp. | 4.89% |
| 2. VJA: Jailbreak Attack in Vision | 3 | ≈ 1.12 pp. | 4.15% |
| 3. IESBench: Benchmarking the Safety of Large Image Editing Models | 4 | ≈ 1.65 pp. | 6.11% |
| 4. Experiments | 5 | ≈ 2.5 pp. | 9.26% |
| 5. Related Work | 8 | ≈ 0.49 pp. | 1.81% |
| 6. Conclusion and Limitations | 8 | ≈ 0.23 pp. | 0.85% |
| Impact Statement | 9 | ≈ 0.11 pp. | 0.41% |
| Acknowledgment | 9 | ≈ 0.08 pp. | 0.3% |
| References | 9 | ≈ 2.78 pp. | 10.3% |
| Appendix | 12 | ≈ 6.35 pp. | 23.52% |
| A. Additional Results | 18 | ≈ 4.25 pp. | 15.74% |
| B. More Details about IESBench | 22 | ≈ 3.84 pp. | 14.22% |
| C. Experimental Setup | 26 | ≈ 1.47 pp. | 5.44% |
All detected figure and table locations
Main text
Argument and organization
- 1 Introduction; 2 Vision-Centric Attack
Connects a new input interface to its safety threat and a proposed defense.
- 3 IESBench; 4 Experiments
Defines data construction and distinct safety/editing metrics before comparisons and judge analysis.
- 5 Related Work; 6 Conclusion and Limitations
Places the interface in context and identifies visual-understanding failures that constrain attack efficacy.
Problem, claim and contribution
The threat is framed around a change in interaction modality, with image input and image output distinguished from earlier multimodal text-generation settings.
Abstract, p.1; Section 2, p.3 · Main paper
Argument and section order
Benchmark construction and scoring have their own sections rather than appearing only as experimental setup.
Section 3, pp.4-5 · Main paper
Figures, captions and visual explanation
The defense diagram marks cache reuse and a stop decision; the main table separately groups deployment types and four metrics, with caption-defined risk colors.
Figure 3, p.3; Table 1, p.5 · Main paper
Tables, comparisons and evidence
Safeguard bypass, valid editing and severe harmful output are different measurements; the text explains that a returned but invalid edit can count as attack success.
Section 3.2, p.5; Appendix B.5 and C.1, p.26 · Main paper
Appendix and reproducibility
Runtime overhead, human-judge agreement and transfer are documented separately, allowing the negligible-overhead and judge-quality claims to be inspected independently.
Appendices A.4-A.6, pp.21-22 · Main paper
Clarity and writing conventions
The conclusion acknowledges that weak visual understanding can reduce attack effectiveness, but some prose/table details need consistency checks, including the abstract versus Table 1 GPT Image score.
Abstract, p.1; Table 1, p.5; Conclusion, pp.8-9 · Main paper
What to reuse—and what not to infer
Transferable lesson: Define the measurement chain from accepted request to valid output to harmful output, then audit every headline against the corresponding table.
Boundary: ASR here is not a rate of valid harmful images; model/version and judge choices matter, and the paper contains numerical/prose inconsistencies that should not be copied.
Apply this lesson using the argument and evidence worksheet.
Inspection record and sources
- Checked
- 2026-10-07
- Reading status
- Writing-focused manuscript scan + selected visuals inspected
- Main text
- Writing-oriented scan of the main argument through physical p.9, with focused physical-page text inspection on 1, 3, 4, 5, 7, 8, 9. Framing, section progression, mechanism/proof explanation, displays, comparison conditions and conclusions were inspected. This is not exhaustive technical reading, proof verification or reproduction.
- Appendix / supplement
- Appendix organization and main-text pointers were checked; focused text inspection of physical pages 14, 21, 22, 26. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
- Visually inspected PDF pages
- 3, 5
This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.