--- title: "MGM vs Visual-Adversarial-Examples-Jailbreak-Large-Language-Models" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jia-lab-research-mgm-vs-unispac-visual-adversarial-examples-jailbreak-large-language-models" tools: ["jia-lab-research-mgm", "unispac-visual-adversarial-examples-jailbreak-large-language-models"] --- # MGM vs Visual-Adversarial-Examples-Jailbreak-Large-Language-Models *GraphCanon updated Aug 18, 2026* ## Verdict Pick MGM if mGM offers a focused approach on multi-modal vision-language generation tasks with specific dependency requirements; 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. [MGM](https://github.com/JIA-Lab-research/MGM) reports 3.3k GitHub stars, 276 forks, and 61 open issues, last pushed May 4, 2024. [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 [MGM's repository](https://github.com/JIA-Lab-research/MGM) and [Visual-Adversarial-Examples-Jailbreak-Large-Language-Models's repository](https://github.com/Unispac/Visual-Adversarial-Examples-Jailbreak-Large-Language-Models). | | [MGM](/tools/jia-lab-research-mgm.md) | [Visual-Adversarial-Examples-Jailbreak-Large-Language-Models](/tools/unispac-visual-adversarial-examples-jailbreak-large-language-models.md) | | --- | --- | --- | | Tagline | Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models | Repository for visual adversarial examples that jailbreak large language models | | Stars | 3,331 | 282 | | Forks | 276 | 30 | | Open issues | 61 | 24 | | Language | Python | Python | | Adopt for | MGM offers a focused approach on multi-modal vision-language generation tasks with specific dependency requirements. | 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 | LLM Frameworks, Model Training | Computer Vision, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [MGM](/tools/jia-lab-research-mgm.md) | [Visual-Adversarial-Examples-Jailbreak-Large-Language-Models](/tools/unispac-visual-adversarial-examples-jailbreak-large-language-models.md) | | --- | --- | --- | | Days since push | 835d | 813d | | Open issues (now) | 61 | 24 | | Stars delta | +1 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/jia-lab-research-mgm/trust.md) | [trust report](/tools/unispac-visual-adversarial-examples-jailbreak-large-language-models/trust.md) | ## Decision facts: MGM - **Adopt for:** MGM offers a focused approach on multi-modal vision-language generation tasks with specific dependency requirements. ## 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 MGM if… - Tags unique to MGM: additional-packages-training-cases, generation, large language models, multi-modality. - Also covers LLM Frameworks. - When working on projects requiring integration of text and visual data for generation tasks. ### Choose Visual-Adversarial-Examples-Jailbreak-Large-Language-Models if… - Tags unique to Visual-Adversarial-Examples-Jailbreak-Large-Language-Models: large-language-model, visual adversarial. - Also covers Computer Vision. - If your goal is to research the resilience of Large Language Models against visual inputs that could mislead or 'jailbreak' their usual behavior. ## When NOT to use MGM - Avoid if your project requires commercial licensing, as MGM is strictly research-use only under CC BY NC 4.0. - Not suitable if you are unable to update or ensure the availability of required Python packages like flash-attn and ninja for training purposes. ## 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 MGM and Visual-Adversarial-Examples-Jailbreak-Large-Language-Models? MGM: Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models. 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 MGM over Visual-Adversarial-Examples-Jailbreak-Large-Language-Models? Choose MGM over Visual-Adversarial-Examples-Jailbreak-Large-Language-Models when Tags unique to MGM: additional-packages-training-cases, generation, large language models, multi-modality; Also covers LLM Frameworks; When working on projects requiring integration of text and visual data for generation tasks. ### When should I choose Visual-Adversarial-Examples-Jailbreak-Large-Language-Models over MGM? Choose Visual-Adversarial-Examples-Jailbreak-Large-Language-Models over MGM when Tags unique to Visual-Adversarial-Examples-Jailbreak-Large-Language-Models: large-language-model, visual adversarial; Also covers Computer Vision; If your goal is to research the resilience of Large Language Models against visual inputs that could mislead or 'jailbreak' their usual behavior. ### When should I avoid MGM? Avoid if your project requires commercial licensing, as MGM is strictly research-use only under CC BY NC 4.0. Not suitable if you are unable to update or ensure the availability of required Python packages like flash-attn and ninja for training purposes. ### 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 MGM or Visual-Adversarial-Examples-Jailbreak-Large-Language-Models more popular on GitHub? MGM has more GitHub stars (3,331 vs 282). Stars measure visibility, not whether either tool fits your constraints. ### Are MGM and Visual-Adversarial-Examples-Jailbreak-Large-Language-Models open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to MGM or Visual-Adversarial-Examples-Jailbreak-Large-Language-Models? GraphCanon lists graph-backed alternatives at [MGM alternatives](/tools/jia-lab-research-mgm/alternatives) and [Visual-Adversarial-Examples-Jailbreak-Large-Language-Models alternatives](/tools/unispac-visual-adversarial-examples-jailbreak-large-language-models/alternatives) ([MGM markdown twin](/tools/jia-lab-research-mgm/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/jia-lab-research-mgm-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, MGM or Visual-Adversarial-Examples-Jailbreak-Large-Language-Models? MGM: 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 MGM and Visual-Adversarial-Examples-Jailbreak-Large-Language-Models? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [MGM trust report](/tools/jia-lab-research-mgm/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=jia-lab-research-mgm`](/api/graphcanon/graph?tool=jia-lab-research-mgm) - 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/_