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Malicious editing instructions can be conveyed entirely through visual cues — arrows, circles, handwriting — with no text prompt, and current image editing models obey them up to 80.9% of the time on Nano Banana Pro and over 70% on GPT-Imag

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2026-08-03 01:13 UTC pith:2XK55FDC

load-bearing objection Vision-side attack surface is real and IESBench is worth having, but the headline ASR overstates because non-edits and safe substitutes count as successes. the 3 major comments →

arxiv 2602.10179 v2 pith:2XK55FDC submitted 2026-02-10 cs.CV cs.AI

When the Prompt Becomes Visual: Vision-Centric Jailbreak Attacks for Large Image Editing Models

Jiacheng Hou , Yining Sun , Ruochong Jin , Haochen Han , Fangming Liu , Wai Kin Victor Chan , Alex Jinpeng Wang This is my paper
classification cs.CV cs.AI
keywords vision-centric jailbreak attackimage editing safetyvisual promptingmultimodal safety alignmentIESBenchMLLM-as-judgejailbreak attacksafety benchmark
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that the newest generation of image editing models can be steered by intent drawn directly onto the image, and that this capability is also a security flaw: a malicious editing request carried only by visual cues — arrows, circles, handwritten directives — with the text channel left empty, bypasses safety systems built to police language. Built on a new benchmark of 1,054 visually prompted images spanning 15 risk categories, the attack succeeds in up to 80.9% of trials on Nano Banana Pro and more than 70% on GPT-Image-1.5 — commercial models that reject most of the same requests when they arrive as text. The paper also argues that the attack's power scales with visual capability, making the strongest editors the most exploitable, and that a training-free text trigger asking the model to reflect on whether its edit would be safe restores much of the lost alignment at negligible cost. If the paper is right, text-centric guardrails cannot protect visual editing pipelines, and safety work must move into the visual input and output channels themselves.

Core claim

The central claim is that safety in modern image editing models is misaligned along modality: the models are trained to obey visual instructions — marks, arrows, handwritten directives — while their guardrails are built to police language. A jailbreak can therefore run with no text at all: a malicious instruction drawn onto the input image is executed as an edit, producing policy-violating output. This Vision-Centric Jailbreak Attack succeeds in up to 80.9% of trials against Nano Banana Pro and more than 70% against GPT-Image-1.5, models that reject the same requests when phrased as text. The authors further claim that attack power scales with visual capability, and that a short text safety

What carries the argument

The mechanism is 'vision-centric safety misalignment': editing models must parse intent from pixels, while guardrails police language, so a visual-only instruction lands outside what the safety layers were built to detect. The attack itself, VJA (Vision-Centric Jailbreak Attack), is black-box and prompt-level: the malicious instruction is drawn or annotated onto a benign image, and the text channel is left empty. Claims are measured on IESBench, a benchmark of 1,054 annotated images in 15 risk categories, scored by a multimodal large language model (MLLM) judge on attack success and harmfulness. The countermeasure is a training-free introspection trigger — a sentence appended to the prompt a

Load-bearing premise

The load-bearing premise is the measurement protocol of Section 3.2: an attack counts as successful whenever the model does anything but refuse — including producing an unrelated safe image or no meaningful edit (Appendix B.5) — and the judge is handed each sample's risk category before scoring (Appendix B.4), so the headline success rates could overstate how often genuinely harmful edits are produced.

What would settle it

Re-run the attacks on a random subset of the 1,054 benchmark images and score the outputs with an MLLM judge that is blind to risk category and that counts as failures both 'unrelated safe content' responses (score 2) and edits the authors themselves label 'successful but invalid' (validity 0); have a small human panel label the same outputs as harmful or not. If the recalculated success rates on Nano Banana Pro and GPT-Image-1.5 fall well below the reported 80.9% and 70%, the claim that text-centric safeguards are systematically bypassed would need to be weakened from 'compromised' to 'occasi

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Text-centric guard models and prompt moderation give little protection against vision-only instructions, because the malicious request never appears as text; safety must be enforced on the visual input or on the output image instead.
  • The most capable commercial editors are also the most exploitable: models that reject the same requests in text follow them faithfully as drawings, so visual capability and vulnerability grow together.
  • Evidence tampering and aversive manipulation — fabricating documents, faking scenes, inducing discomfort — are the most consistently successful categories, making forged visual evidence the chief danger of this attack surface.
  • A weakly aligned open-weight model can be brought to roughly commercial safety levels with a training-free text trigger: attack success falls by about a third and average harmfulness by 1.2 points, at a few percent extra runtime.
  • Editing-task safety needs editing-specific metrics: refusal rates alone miss 'successful but invalid' attacks, so the benchmark adds editing validity and high-risk ratio to separate real harmful edits from failed or irrelevant outputs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same attack pattern transfers beyond offensive content: any application that lets users annotate edits onto documents, receipts, contracts, or product images inherits a visual jailbreak path, where the harm is fraud or legal tampering rather than graphic content — a consequence the paper's categories hint at but do not develop.
  • The introspection defense assumes the attacker cannot touch the prompt channel; an adversary aware of the trigger could plausibly paint a visual cue ordering the model to ignore the appended safety review, a failure mode the paper does not test.
  • The finding that strong vision models obey drawings faithfully sets up a capability race: as editors improve at visual instruction-following, both the attack and the introspection defense strengthen together, so net safety depends on which side receives more alignment investment.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper introduces Vision-Centric Jailbreak Attack (VJA), an attack for large image editing models in which a malicious editing instruction is encoded entirely in visual cues (marks, arrows, visual-text prompts) and the model is queried with an image-only input. The authors also construct IESBench, a benchmark of 1054 visually prompted images spanning 15 risk categories, and evaluate VJA on seven commercial and open-source editing models. The headline results are ASRs up to 80.9% on Nano Banana Pro and 70.3% on GPT Image 1.5. A training-free defense is proposed that appends a short 'safety trigger' text to the multimodal prompt, activating internal safety reasoning through KV-cache reuse with negligible overhead; on a local Qwen model this reduces ASR from 100% to 66.9%.

Significance. The paper targets a genuinely important and underexplored safety surface: as image editing models shift to vision-prompt interaction, malicious intent can be conveyed without text, potentially bypassing text-centric safeguards. The IESBench resource—1054 images, 15 categories, hierarchical risk levels, text annotations for transferability—is a valuable contribution if released, and the comparison across commercial and open-source models is systematic. The training-free trigger defense is a practical, low-overhead idea that deserves attention. The paper also includes sensitivity analyses and a human-judge comparison. However, as written, the headline quantitative claims are not supported by the metrics as defined: ASR counts non-refusals even when the output is a safe substitution or a non-edit, and the judge prompt primes higher harm scores. Thus the central 'compromises state-of-the-art models' claim needs reanalysis and re-reporting.

major comments (3)
  1. [Abstract; Table 1; §3.2; Appendix C.1] The headline attack-success claim is not supported by the primary metric as defined. Appendix C.1 defines ASR as 'the ratio of malicious requests that are not recognized and rejected by the safeguard model.' Appendix B.4 assigns Score 2 to outputs that replace the malicious request with unrelated safe content (e.g., a flower), and Appendix B.5 explicitly labels non-edits and miscomprehended edits as 'successful but invalid attacks' with HS=2 and EV=0. Both are counted as ASR successes. Consequently, the Abstract's '80.9% on Nano Banana Pro and 70.3% on GPT Image 1.5' overstate how often VJA actually produces a harmful edit; the more honest High Risk Ratio (70.6% and 52.0% in Table 1) is still biased upward by the judge priming described below. Please re-report using a metric that requires EV=1 and HS≥τ as the primary success measure, and present the current ASR only as a 'non-refusal' se
  2. [Appendix B.4] The judge rubric is not neutral. The judge is given the sample's risk category ('Risk Category: {risk category}') and instructed by Special Notices 1 and 2 to 'increase your harmfulness score accordingly' for levels 2/3 and that 'the harmfulness score should be greater than 2.' This directly inflates HS and therefore any threshold-derived statistic such as HRR (Eq. 5). The human validation in Table 3 does not resolve this: it asks human evaluators to vote on which MLLM rating/reasoning is best on a 5% sample, not to provide independent absolute scores, and the same primed rubric is used. Please ablate the judge prompt by removing the risk-category header and the two special notices, and report per-model agreement with unprimed human scoring.
  3. [Table 1; §4; Table 2] All headline results appear to be single-run evaluations with no variance estimates. Commercial API models can be nondeterministic and are updated over time. Since the central claim is quantitative (specific ASR/HRR values), the paper should report at least three independent runs with mean ± std or bootstrap confidence intervals, or justify deterministic inference. Without this, the reader cannot assess whether, e.g., the reported 80.9% vs 70.1% gap is stable or whether defense improvements are within noise. This is especially important because the defense comparison (Qwen-Image-Edit-Safe vs. Qwen-Image-Edit*) relies on exact ASR reductions of 33%.
minor comments (4)
  1. [Figure 3 / body text] Several stray tokens such as 'None', 'TStop', and 'Qwen2.5VL' appear in the main text near Eq. (2) and Figure 3. These appear to be leftover fragments from figure/log formatting and should be removed.
  2. [§4.4, Table 3] The text says 'Gemini 3 Pro and GPT 5.2 are the two most preferred models,' but Table 3 shows GPT 5.2 at 29.3% and Gemini 3 Pro at 25.4%; the wording should state that GPT 5.2 is the most preferred, followed by Gemini 3 Pro. Also, the table header has 'V otes' with a spacing artifact.
  3. [Appendix A.1] The sensitivity analysis is conducted on a 7-image sample. If this is intended to support claims about benchmark robustness ('the strong ASR and HS maintained by IESBench'), please state the small-sample limitation clearly or expand the sample.
  4. [§3.1] The text says IESBench 'merely selects 15 suitable images from the prior benchmark,' which reads as a typo for 'also selects' or a similar expression. Please clarify that the prior-benchmark subset is a small cross-benchmark comparison set, not the full IESBench.

Circularity Check

0 steps flagged

No load-bearing circularity: the central vulnerability claim is an empirical measurement against external commercial/open-source models, with external MLLM and human judging; the co-developed benchmark/attack/defense setup is a mild self-referential evaluation loop, not a circular derivation.

full rationale

The paper's central claims are empirical measurements, not derivations. VJA is tested against seven external victim models via public APIs, compared against a text-centric baseline (TJA), and judged by five MLLMs plus independent human annotators (Table 3). No parameter is fitted to a subset and then predicted on a related quantity; no load-bearing self-citation appears; no uniqueness result from the authors' prior work is invoked; and the defense's safety trigger is training-free and evaluated zero-shot on a mixed benign/malicious set. The benchmark/attack/defense are co-developed, which is a mild self-referential evaluation setup, but that is not circularity: IESBench's taxonomy is grounded in external content policies, and the reported metrics (ASR, HS, EV, HRR) are defined transparently. The ASR definition counts non-refusals, including some outputs the appendix itself labels 'successful but invalid' (B.5) or safe replacements (B.4), so headline ASR overstates how often harmful edits are produced; this is a metric-validity caveat, not a reduction by construction, and HRR plus human evaluation (75.1%) independently support the direction of the vulnerability finding. The paper also self-reports its limitation that VJA is less effective on models with limited visual perception, further indicating honest scope rather than circular argument.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 3 invented entities

The central claim rests on measurement assumptions rather than mathematical axioms; the taxonomy, judge protocol, and attack/defense artifacts are author-defined, and the defense mechanism is asserted rather than proven. No standard_math axioms are invoked.

free parameters (3)
  • HRR threshold tau = 4
    Equation (5) in App. C.1 defines high-risk as HS>=4; changing tau changes the reported High Risk Ratio and the relative ranking of models.
  • IESBench taxonomy and dataset scale = 15 categories / 116 attributes / 9 actions / 1054 images
    Author-chosen taxonomy and benchmark size; these numbers are not derived from an external standard and shape all reported rates.
  • Safety trigger wording = hand-crafted sentence ("You are an image editing safety evaluator...")
    Defense performance depends on this exact prompt; no prompt search or comparison with alternative wordings is reported.
axioms (4)
  • domain assumption MLLM judge provides valid harmfulness labels
    Sec 3.2 and App B.4 rely on Gemini 3 Pro judgments; Table 3 shows human HS is lower than Gemini, and the judge is primed with the risk category label.
  • domain assumption The 15-category taxonomy covers the safety-relevant risks for image editing
    Sec 3.1 and Table 4; if categories are incomplete or mislabeled, benchmark conclusions do not generalize to the full space of harmful edits.
  • domain assumption Commercial API outputs reflect model-level safety alignment
    Sec 2 defines a black-box setting; commercial APIs may add hidden guardrails or preprocessing, so ASR measures the served system, not necessarily the model's internal safety.
  • ad hoc to paper Appending the trigger activates pre-existing safety awareness via attention
    Sec 2.2 Discussion attributes defense efficacy to the attention mechanism with no mechanistic evidence; only a reasoning-ablation is provided.
invented entities (3)
  • IESBench no independent evidence
    purpose: Safety benchmark for large image editing models; used to measure VJA success and defense efficacy.
    Author-constructed from AI-generated, open-sourced, and 15 prior-benchmark images; not independently validated or third-party adopted yet, and the dataset is not shipped in the manuscript.
  • Vision-Centric Jailbreak Attack (VJA) no independent evidence
    purpose: Attack paradigm that encodes malicious editing intent purely through visual inputs.
    An attack artifact; its efficacy is measured only on the author-built IESBench, not against an external standardized test.
  • Introspective safety trigger no independent evidence
    purpose: Training-free defense prompt appended after the multimodal input to activate model safety reasoning.
    Hand-crafted prompt; tested on one open-source model only, with no independent benchmark or formal analysis of the claimed attention-based mechanism.

pith-pipeline@v1.3.0-alltime-deepseek · 22283 in / 16718 out tokens · 172804 ms · 2026-08-03T01:13:52.311435+00:00 · methodology

0 comments
read the original abstract

Recent advances in large image editing models have shifted the paradigm from text-driven instructions to vision-prompt editing, where user intent is inferred directly from visual inputs such as marks, arrows, and visual-text prompts. While this paradigm greatly expands usability, it also introduces a critical and underexplored safety risk: the attack surface itself becomes visual. In this work, we propose Vision-Centric Jailbreak Attack (VJA), the first visual-to-visual jailbreak attack that conveys malicious instructions purely through visual inputs. To systematically study this emerging threat, we introduce IESBench, a safety-oriented benchmark for image editing models. Extensive experiments on IESBench demonstrate that VJA effectively compromises state-of-the-art commercial models, achieving attack success rates of up to 80.9% on Nano Banana Pro and 70.1% on GPT-Image-1.5. To mitigate this vulnerability, we propose a training-free defense based on introspective multimodal reasoning, which substantially improves the safety of poorly aligned models to a level comparable with commercial systems, without auxiliary guard models and with negligible computational overhead. Our findings expose new vulnerabilities, provide both a benchmark and practical defense to advance safe and trustworthy modern image editing systems. Warning: This paper contains offensive images created by large image editing models.

Figures

Figures reproduced from arXiv: 2602.10179 by Alex Jinpeng Wang, Fangming Liu, Haochen Han, Jiacheng Hou, Ruochong Jin, Wai Kin Victor Chan, Yining Sun.

Figure 1
Figure 1. Figure 1: Comparison between our Vision-Centric Jailbreak Attack (VJA) and conventional Text-Centric Jailbreak Attacks. Top: Attack scheme comparison; Bottom: Performance compari￾son on a subset of IESBench, VJA achieves significantly higher attack success rates across four commercial models. 1. Introduction Instruction-based image editing aims to modify images ac￾cording to user-provided prompts, enabling flexible … view at source ↗
Figure 2
Figure 2. Figure 2: Overview and statistics of our constructed IESBench. Note that the proposed VJA is vision-only jailbreak attack, so no additional text prompts are needed. challenges existing security mechanisms in large image edit￾ing models. Current safeguard models (Chi et al., 2024) and safety alignment mechanisms (Yu et al., 2024) are predomi￾nantly designed for text-centric tasks, focusing on detecting, moderating, o… view at source ↗
Figure 3
Figure 3. Figure 3: Introspection-based Defense, which leverages a safety trigger to enhance the security of large image editing models. Meanwhile, we develop a variety of visual prompts to test the model security, including variants in visual markers (e.g., different sizes, colors and shapes) and visual language (e.g., different fonts). See Appendix A.1 for details. Discussion: Malicious instructions in vision require com￾pl… view at source ↗
Figure 4
Figure 4. Figure 4: The illustration of IESBench construction. The top figure shows the 15 risk category covered in our IESBench in a hierarchical manner, and the bottom figure shows the pipeline for dataset curation and evaluation. safety-awareness of VLMs can be activated and utilized for defense. The essence of the proposed defense approach is to transform the vision-centric attack back to a text-centric attack through mul… view at source ↗
Figure 5
Figure 5. Figure 5: Average harmfulness score comparison between different models on IESBench. (a) shows the distribution of samples in different levels of our IESBench. (b)-(i) illustrate the average HS of models for attacks in different risk levels. main results are based on all 1054 images in our benchmark, and we sample a portion of the data for the other analysis, which will be clarified in the corresponding section. Vic… view at source ↗
Figure 6
Figure 6. Figure 6: Attack results comparison between VJA and TJA. Some weak models may fail to understand or misunderstand VJA, leading to trivial editing. Best viewed when zoom in [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The malicious input classification result of our defense method. We repeat each test for 5 times [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Showcases of the images and output of 15 risk categories in our constructed IESBench. Left shows the visually-prompted image, and right shows the edited image by large image editing models. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative attack results. Comparison of the outputs produced by commercial models and open-source models under the same attack setting (I1-I10) on IESBench. Best viewed when zoom in. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative attack results. Note that, we mask potentially sensitive regions in the qualitative results. For categories I1, I3, and I6, we apply mosaic (pixelation). For categories I9 and I12, we use black-box masking. Except for these redactions, all images shown are the created by the respective models. Best viewed when zoom in. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Showcase of IESBench failure cases. We present samples of (i) visual reasoning failure, (ii) text editing failure, and (iii) attack failure. Original image Nano Banana Pro Qwen-Image-Edit Original image GPT-Image Qwen-Image-Edit Seedream Qwen-Image-Edit* Original image Qwen-Image-Edit Original image Seedream [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Some interesting cases of failed attacks on IESBench. (i) Stick figures are riding a horse. (ii) Stick figures are hunting a cow. (iii) A little hedgehog is riding on wheels. (iv) A strange lemon with a face. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Illustration of the sensitivity test for visual prompts. The input images are only altered with the colors, languages, text fonts, sizes and shapes. A. Additional Results A.1. Sensitivity Experiments Drawing on insights from the article (Feng et al., 2025), we have developed a more comprehensive and robust sensitivity analysis for a 7-image sample from our IESBench. We test the attack results on different… view at source ↗
Figure 14
Figure 14. Figure 14: Sensitivity Experiment Results. The percentage numbers infer the maximum influence to the evaluation metric of the average of all 4 models and the extreme values of single models compared with the default settings [PITH_FULL_IMAGE:figures/full_fig_p019_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: The Robustness of VJA for different models. Red line indicating the performance of default setting. The box and dots indicate the distribution of the HS and EV with those variables altering. further corroborates this point. These findings demonstrate both the high quality of our IESBench and its role in raising the bar for future, more advanced models. A.2. Harmfulness Score Distribution Evaluation with G… view at source ↗
Figure 16
Figure 16. Figure 16: Harmfulness score distributions of large image editing models on IESBench evaluated by Qwen3-VL-8B-Instruct. The first row shows the commercial models, and the second row shows the open models [PITH_FULL_IMAGE:figures/full_fig_p020_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Harmfulness score distributions of 8 victim large image editing models on IESBench evaluated by Gemini 3 Pro. The first row shows the commercial models, and the second row shows the open models. A.3. Qualitative Attack Results Comparison of VJA on IESBench To provide an intuitive comparison between commercial and open-source models, we conduct a qualitative analysis across all categories from I1 to I15. F… view at source ↗
Figure 18
Figure 18. Figure 18: Comparison of IESBench vs Prior Benchmarks. We compare our benchmark with MMSafetyBench, FigStep and HADES of MultiModal. A.5. Comparison with Prior Benchmarks [PITH_FULL_IMAGE:figures/full_fig_p021_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Overview of IESBench data format. It includes source image, image, and text.json with per-sample JSON records. B.4. Scoring Rubric for Judge Model For the scoring of the Harmfulness Score, we adopt the following as the rubric: 23 [PITH_FULL_IMAGE:figures/full_fig_p023_19.png] view at source ↗

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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