--- title: "image-hijacks vs JOOD" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/euanong-image-hijacks-vs-naver-ai-jood" tools: ["euanong-image-hijacks", "naver-ai-jood"] --- # image-hijacks vs JOOD *GraphCanon updated Aug 5, 2026* ## Verdict Pick image-hijacks if image Hijacks is an advanced adversarial AI tool for generating images that can control the output of generative models at runtime; pick JOOD if jOOD is an implementation for exploring strategies to jailbreak language and multimodal models using out-of-distribution inputs. It leverages Python and is licensed under Apache-2.0. [image-hijacks](https://image-hijacks.github.io/) reports 57 GitHub stars, 13 forks, and 8 open issues, last pushed Sep 19, 2023. [JOOD](https://github.com/naver-ai/JOOD) has 21 stars, 4 forks, and 2 open issues, last pushed Jun 11, 2025. Figures are from public GitHub metadata via [image-hijacks's repository](https://github.com/euanong/image-hijacks) and [JOOD's repository](https://github.com/naver-ai/JOOD). | | [image-hijacks](/tools/euanong-image-hijacks.md) | [JOOD](/tools/naver-ai-jood.md) | | --- | --- | --- | | Tagline | Adversarial Images Control Generative Models at Runtime | Implementation for multimodal LLM jailbreaking strategy | | Stars | 57 | 21 | | Forks | 13 | 4 | | Open issues | 8 | 2 | | Language | Python | Python | | Adopt for | Image Hijacks is an advanced adversarial AI tool for generating images that can control the output of generative models at runtime. | JOOD is an implementation for exploring strategies to jailbreak language and multimodal models using out-of-distribution inputs. It leverages Python and is licensed under Apache-2.0. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Computer Vision | Computer Vision, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [image-hijacks](/tools/euanong-image-hijacks.md) | [JOOD](/tools/naver-ai-jood.md) | | --- | --- | --- | | Days since push | 1050d | 419d | | Open issues (now) | 8 | 2 | | Owner type | User | Organization | | Full report | [trust report](/tools/euanong-image-hijacks/trust.md) | [trust report](/tools/naver-ai-jood/trust.md) | ## Shared compatibility - **Python**: [image-hijacks](/tools/euanong-image-hijacks.md) - Python runtime; [JOOD](/tools/naver-ai-jood.md) - Python runtime ## Decision facts: image-hijacks - **Requirements:** Min 8 GB RAM; System-specific adjustments might be required, such as setting the `PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring` environment variable.; Large files like cached models or data are stored in the `data/` directory. Ensure this directory is appropriately configured for storage. - **Adopt for:** Image Hijacks is an advanced adversarial AI tool for generating images that can control the output of generative models at runtime. ## Decision facts: JOOD - **Requirements:** Python version to install requirements: Python >= 3.12.7; The package list for dependencies should be sourced from the `requirements.txt` file provided in the repository. - **Adopt for:** JOOD is an implementation for exploring strategies to jailbreak language and multimodal models using out-of-distribution inputs. It leverages Python and is licensed under Apache-2.0. ## Choose when ### Choose image-hijacks if… - License: image-hijacks is MIT, JOOD is Apache-2.0. - Requirements: Min 8 GB RAM; System-specific adjustments might be required, such as setting the `PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring` environment variable.; Large files like cached models or data are stored in the `data/` directory. Ensure this directory is appropriately configured for storage.. - Tags unique to image-hijacks: adversarial, generative, image manipulation, runtime control. - When you need to create specific adversarial scenarios where fine-tuned images manipulate generative model outputs during real-time operation. ### Choose JOOD if… - License: JOOD is Apache-2.0, image-hijacks is MIT. - Requirements: Python version to install requirements: Python >= 3.12.7; The package list for dependencies should be sourced from the `requirements.txt` file provided in the repository.. - Tags unique to JOOD: jailbreaking, multimodal-llms. - Also covers Model Training. - Use JOOD when you need to explore how a multimodal model behaves with unforeseen or out-of-distribution inputs, thus pushing the boundaries of its conventional responses or outputs. ## When NOT to use image-hijacks - Avoid using Image Hijacks for standard machine learning tasks that do not involve runtime manipulation of AI-generated images through adversarial means. - Do not use this tool if you are working within a constrained or sensitive environment where introducing adversarial elements poses an additional risk to system security. ## When NOT to use JOOD - Avoid using JOOD if jailbreaking strategies are not of interest, such as in scenarios requiring strict adherence to model limitations and ethical constraints. - JOOD may not be suitable if you require tools that focus on improving performance or stability of models rather than exploring unconventional behavior or vulnerabilities. ## Common questions ### What is the difference between image-hijacks and JOOD? image-hijacks: Adversarial Images Control Generative Models at Runtime. JOOD: Implementation for multimodal LLM jailbreaking strategy. See the comparison table for live GitHub stats and shared categories. ### When should I choose image-hijacks over JOOD? Choose image-hijacks over JOOD when License: image-hijacks is MIT, JOOD is Apache-2.0; Requirements: Min 8 GB RAM; System-specific adjustments might be required, such as setting the `PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring` environment variable.; Large files like cached models or data are stored in the `data/` directory. Ensure this directory is appropriately configured for storage.; Tags unique to image-hijacks: adversarial, generative, image manipulation, runtime control; When you need to create specific adversarial scenarios where fine-tuned images manipulate generative model outputs during real-time operation. ### When should I choose JOOD over image-hijacks? Choose JOOD over image-hijacks when License: JOOD is Apache-2.0, image-hijacks is MIT; Requirements: Python version to install requirements: Python >= 3.12.7; The package list for dependencies should be sourced from the `requirements.txt` file provided in the repository.; Tags unique to JOOD: jailbreaking, multimodal-llms; Also covers Model Training; Use JOOD when you need to explore how a multimodal model behaves with unforeseen or out-of-distribution inputs, thus pushing the boundaries of its conventional responses or outputs. ### When should I avoid image-hijacks? Avoid using Image Hijacks for standard machine learning tasks that do not involve runtime manipulation of AI-generated images through adversarial means. Do not use this tool if you are working within a constrained or sensitive environment where introducing adversarial elements poses an additional risk to system security. ### When should I avoid JOOD? Avoid using JOOD if jailbreaking strategies are not of interest, such as in scenarios requiring strict adherence to model limitations and ethical constraints. JOOD may not be suitable if you require tools that focus on improving performance or stability of models rather than exploring unconventional behavior or vulnerabilities. ### Is image-hijacks or JOOD more popular on GitHub? image-hijacks has more GitHub stars (57 vs 21). Stars measure visibility, not whether either tool fits your constraints. ### Are image-hijacks and JOOD open source? Yes - both are open-source projects on GitHub (image-hijacks: MIT, JOOD: Apache-2.0). ### Where can I find alternatives to image-hijacks or JOOD? GraphCanon lists graph-backed alternatives at [image-hijacks alternatives](/tools/euanong-image-hijacks/alternatives) and [JOOD alternatives](/tools/naver-ai-jood/alternatives) ([image-hijacks markdown twin](/tools/euanong-image-hijacks/alternatives.md), [JOOD markdown twin](/tools/naver-ai-jood/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/euanong-image-hijacks-vs-naver-ai-jood.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, image-hijacks or JOOD? image-hijacks: Dormant. JOOD: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for image-hijacks and JOOD? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [image-hijacks trust report](/tools/euanong-image-hijacks/trust); [JOOD trust report](/tools/naver-ai-jood/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=euanong-image-hijacks`](/api/graphcanon/graph?tool=euanong-image-hijacks) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_