--- title: "JOOD vs Visual-Adversarial-Examples-Jailbreak-Large-Language-Models" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/naver-ai-jood-vs-unispac-visual-adversarial-examples-jailbreak-large-language-models" tools: ["naver-ai-jood", "unispac-visual-adversarial-examples-jailbreak-large-language-models"] --- # JOOD vs Visual-Adversarial-Examples-Jailbreak-Large-Language-Models *GraphCanon updated Aug 5, 2026* ## Verdict 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; pick Visual-Adversarial-Examples-Jailbreak-Large-Language-Models if this tool focuses on generating and studying visual adversarial examples designed to exploit vulnerabilities in large language models such as MiniGPT-4. [JOOD](https://github.com/naver-ai/JOOD) reports 21 GitHub stars, 4 forks, and 2 open issues, last pushed Jun 11, 2025. [Visual-Adversarial-Examples-Jailbreak-Large-Language-Models](https://github.com/Unispac/Visual-Adversarial-Examples-Jailbreak-Large-Language-Models) has 282 stars, 30 forks, and 24 open issues, last pushed May 13, 2024. Figures are from public GitHub metadata via [JOOD's repository](https://github.com/naver-ai/JOOD) and [Visual-Adversarial-Examples-Jailbreak-Large-Language-Models's repository](https://github.com/Unispac/Visual-Adversarial-Examples-Jailbreak-Large-Language-Models). | | [JOOD](/tools/naver-ai-jood.md) | [Visual-Adversarial-Examples-Jailbreak-Large-Language-Models](/tools/unispac-visual-adversarial-examples-jailbreak-large-language-models.md) | | --- | --- | --- | | Tagline | Implementation for multimodal LLM jailbreaking strategy | Repository for visual adversarial examples that jailbreak large language models | | Stars | 21 | 282 | | Forks | 4 | 30 | | Open issues | 2 | 24 | | Language | Python | Python | | 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. | This tool focuses on generating and studying visual adversarial examples designed to exploit vulnerabilities in large language models such as MiniGPT-4. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Computer Vision, Model Training | Computer Vision, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [JOOD](/tools/naver-ai-jood.md) | [Visual-Adversarial-Examples-Jailbreak-Large-Language-Models](/tools/unispac-visual-adversarial-examples-jailbreak-large-language-models.md) | | --- | --- | --- | | Days since push | 419d | 813d | | Open issues (now) | 2 | 24 | | Owner type | Organization | User | | Full report | [trust report](/tools/naver-ai-jood/trust.md) | [trust report](/tools/unispac-visual-adversarial-examples-jailbreak-large-language-models/trust.md) | ## 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. ## Decision facts: Visual-Adversarial-Examples-Jailbreak-Large-Language-Models - **Adopt for:** This tool focuses on generating and studying visual adversarial examples designed to exploit vulnerabilities in large language models such as MiniGPT-4. ## Choose when ### Choose JOOD if… - 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. - 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. ### Choose Visual-Adversarial-Examples-Jailbreak-Large-Language-Models if… - Tags unique to Visual-Adversarial-Examples-Jailbreak-Large-Language-Models: large-language-model, visual adversarial. - If your goal is to research the resilience of Large Language Models against visual inputs that could mislead or 'jailbreak' their usual behavior. - More GitHub stars (282 vs 21) - visibility, not fit. ## 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. ## When NOT to use Visual-Adversarial-Examples-Jailbreak-Large-Language-Models - Avoid using this tool if your objective does not involve security testing or research concerning visual-linguistic interactions with large language models. - If you are looking to enhance general-purpose AI applications without considering adversarial attacks, other frameworks would be more appropriate. ## Common questions ### What is the difference between JOOD and Visual-Adversarial-Examples-Jailbreak-Large-Language-Models? JOOD: Implementation for multimodal LLM jailbreaking strategy. Visual-Adversarial-Examples-Jailbreak-Large-Language-Models: Repository for visual adversarial examples that jailbreak large language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose JOOD over Visual-Adversarial-Examples-Jailbreak-Large-Language-Models? Choose JOOD over Visual-Adversarial-Examples-Jailbreak-Large-Language-Models when 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; 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 choose Visual-Adversarial-Examples-Jailbreak-Large-Language-Models over JOOD? Choose Visual-Adversarial-Examples-Jailbreak-Large-Language-Models over JOOD when Tags unique to Visual-Adversarial-Examples-Jailbreak-Large-Language-Models: large-language-model, visual adversarial; If your goal is to research the resilience of Large Language Models against visual inputs that could mislead or 'jailbreak' their usual behavior; More GitHub stars (282 vs 21) - visibility, not fit. ### 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. ### When should I avoid Visual-Adversarial-Examples-Jailbreak-Large-Language-Models? Avoid using this tool if your objective does not involve security testing or research concerning visual-linguistic interactions with large language models. If you are looking to enhance general-purpose AI applications without considering adversarial attacks, other frameworks would be more appropriate. ### Is JOOD or Visual-Adversarial-Examples-Jailbreak-Large-Language-Models more popular on GitHub? Visual-Adversarial-Examples-Jailbreak-Large-Language-Models has more GitHub stars (282 vs 21). Stars measure visibility, not whether either tool fits your constraints. ### Are JOOD and Visual-Adversarial-Examples-Jailbreak-Large-Language-Models open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to JOOD or Visual-Adversarial-Examples-Jailbreak-Large-Language-Models? GraphCanon lists graph-backed alternatives at [JOOD alternatives](/tools/naver-ai-jood/alternatives) and [Visual-Adversarial-Examples-Jailbreak-Large-Language-Models alternatives](/tools/unispac-visual-adversarial-examples-jailbreak-large-language-models/alternatives) ([JOOD markdown twin](/tools/naver-ai-jood/alternatives.md), [Visual-Adversarial-Examples-Jailbreak-Large-Language-Models markdown twin](/tools/unispac-visual-adversarial-examples-jailbreak-large-language-models/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/naver-ai-jood-vs-unispac-visual-adversarial-examples-jailbreak-large-language-models.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, JOOD or Visual-Adversarial-Examples-Jailbreak-Large-Language-Models? JOOD: Dormant. Visual-Adversarial-Examples-Jailbreak-Large-Language-Models: 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 JOOD and Visual-Adversarial-Examples-Jailbreak-Large-Language-Models? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [JOOD trust report](/tools/naver-ai-jood/trust); [Visual-Adversarial-Examples-Jailbreak-Large-Language-Models trust report](/tools/unispac-visual-adversarial-examples-jailbreak-large-language-models/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=naver-ai-jood`](/api/graphcanon/graph?tool=naver-ai-jood) - 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/_