Skip to content

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
Before references / appendix: ≈ 30.78%References / intervening material: ≈ 10.3%Attached appendix: ≈ 58.93%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

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 headingStarts on PDF pageEstimated spanShare of entire PDF
1. Introduction1≈ 1.32 pp.4.89%
2. VJA: Jailbreak Attack in Vision3≈ 1.12 pp.4.15%
3. IESBench: Benchmarking the Safety of Large Image Editing Models4≈ 1.65 pp.6.11%
4. Experiments5≈ 2.5 pp.9.26%
5. Related Work8≈ 0.49 pp.1.81%
6. Conclusion and Limitations8≈ 0.23 pp.0.85%
Impact Statement9≈ 0.11 pp.0.41%
Acknowledgment9≈ 0.08 pp.0.3%
References9≈ 2.78 pp.10.3%
Appendix12≈ 6.35 pp.23.52%
A. Additional Results18≈ 4.25 pp.15.74%
B. More Details about IESBench22≈ 3.84 pp.14.22%
C. Experimental Setup26≈ 1.47 pp.5.44%
All detected figure and table locations

Argument and organization

  1. 1 Introduction; 2 Vision-Centric Attack

    Connects a new input interface to its safety threat and a proposed defense.

  2. 3 IESBench; 4 Experiments

    Defines data construction and distinct safety/editing metrics before comparisons and judge analysis.

  3. 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.