--- title: "jailbreak-evaluation vs AutoDefense" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/controllability-jailbreak-evaluation-vs-xhmy-autodefense" tools: ["controllability-jailbreak-evaluation", "xhmy-autodefense"] --- # jailbreak-evaluation vs AutoDefense *GraphCanon updated Aug 5, 2026* ## Verdict Pick jailbreak-evaluation if jailbreak-evaluation is a Python package aimed at evaluating if AI models have been jailbroken by generating outputs that diverge from expected programming; pick AutoDefense if autoDefense uses a multi-agent framework to mitigate jailbreak attacks on LLMs, installed via Python. [jailbreak-evaluation](https://arxiv.org/abs/2404.06407) reports 27 GitHub stars, 8 forks, and 0 open issues, last pushed Nov 4, 2024. [AutoDefense](https://arxiv.org/abs/2403.04783) has 68 stars, 20 forks, and 1 open issues, last pushed Jan 15, 2026. Figures are from public GitHub metadata via [jailbreak-evaluation's repository](https://github.com/controllability/jailbreak-evaluation) and [AutoDefense's repository](https://github.com/XHMY/AutoDefense). | | [jailbreak-evaluation](/tools/controllability-jailbreak-evaluation.md) | [AutoDefense](/tools/xhmy-autodefense.md) | | --- | --- | --- | | Tagline | Python package for language model jailbreak evaluation | Multi-Agent LLM Defense against Jailbreak Attacks | | Stars | 27 | 68 | | Forks | 8 | 20 | | Open issues | 0 | 1 | | Language | Python | Python | | Adopt for | jailbreak-evaluation is a Python package aimed at evaluating if AI models have been jailbroken by generating outputs that diverge from expected programming. | AutoDefense uses a multi-agent framework to mitigate jailbreak attacks on LLMs, installed via Python. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [jailbreak-evaluation](/tools/controllability-jailbreak-evaluation.md) | [AutoDefense](/tools/xhmy-autodefense.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 638d | 201d | | Open issues (now) | 0 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/controllability-jailbreak-evaluation/trust.md) | [trust report](/tools/xhmy-autodefense/trust.md) | ## Shared compatibility - **Python**: [jailbreak-evaluation](/tools/controllability-jailbreak-evaluation.md) - Python runtime; [AutoDefense](/tools/xhmy-autodefense.md) - Python runtime ## Decision facts: jailbreak-evaluation - **Requirements:** The tool depends on having PyTorch and FastChat installed; An API key from the OpenAI Platform is required for full functionality - **Adopt for:** jailbreak-evaluation is a Python package aimed at evaluating if AI models have been jailbroken by generating outputs that diverge from expected programming. ## Decision facts: AutoDefense - **Adopt for:** AutoDefense uses a multi-agent framework to mitigate jailbreak attacks on LLMs, installed via Python. ## Choose when ### Choose jailbreak-evaluation if… - License: jailbreak-evaluation is Apache-2.0, AutoDefense is MIT. - Requirements: The tool depends on having PyTorch and FastChat installed; An API key from the OpenAI Platform is required for full functionality. - Tags unique to jailbreak-evaluation: ai safety, evaluation tools, jailbreaks, language-models. - When you need to assess whether an AI model can be manipulated to produce unpredictable or unintended outcomes through specific inputs, such as jailbreaking. ### Choose AutoDefense if… - License: AutoDefense is MIT, jailbreak-evaluation is Apache-2.0. - Tags unique to AutoDefense: defense-mechanism, jailbreak prevention, large language models, llm-defense. - Also covers AI Agents. - Implementing robust defenses for enterprise-level AI projects with high-security requirements ## When NOT to use jailbreak-evaluation - If your project does not involve assessing the security or integrity of how an AI model responds to manipulative input techniques designed to exploit design weaknesses. - When you do not need dependencies on specific frameworks like PyTorch and FastChat, as jailbreak-evaluation requires these without automating their installation. ## When NOT to use AutoDefense - Projects requiring light-weight solutions where multi-agent systems might introduce complexity overhead - Environments without access to Python and its ecosystem, as AutoDefense depends on specific Python packages ## Common questions ### What is the difference between jailbreak-evaluation and AutoDefense? jailbreak-evaluation: Python package for language model jailbreak evaluation. AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks. See the comparison table for live GitHub stats and shared categories. ### When should I choose jailbreak-evaluation over AutoDefense? Choose jailbreak-evaluation over AutoDefense when License: jailbreak-evaluation is Apache-2.0, AutoDefense is MIT; Requirements: The tool depends on having PyTorch and FastChat installed; An API key from the OpenAI Platform is required for full functionality; Tags unique to jailbreak-evaluation: ai safety, evaluation tools, jailbreaks, language-models; When you need to assess whether an AI model can be manipulated to produce unpredictable or unintended outcomes through specific inputs, such as jailbreaking. ### When should I choose AutoDefense over jailbreak-evaluation? Choose AutoDefense over jailbreak-evaluation when License: AutoDefense is MIT, jailbreak-evaluation is Apache-2.0; Tags unique to AutoDefense: defense-mechanism, jailbreak prevention, large language models, llm-defense; Also covers AI Agents; Implementing robust defenses for enterprise-level AI projects with high-security requirements. ### When should I avoid jailbreak-evaluation? If your project does not involve assessing the security or integrity of how an AI model responds to manipulative input techniques designed to exploit design weaknesses. When you do not need dependencies on specific frameworks like PyTorch and FastChat, as jailbreak-evaluation requires these without automating their installation. ### When should I avoid AutoDefense? Projects requiring light-weight solutions where multi-agent systems might introduce complexity overhead Environments without access to Python and its ecosystem, as AutoDefense depends on specific Python packages ### Is jailbreak-evaluation or AutoDefense more popular on GitHub? AutoDefense has more GitHub stars (68 vs 27). Stars measure visibility, not whether either tool fits your constraints. ### Are jailbreak-evaluation and AutoDefense open source? Yes - both are open-source projects on GitHub (jailbreak-evaluation: Apache-2.0, AutoDefense: MIT). ### Where can I find alternatives to jailbreak-evaluation or AutoDefense? GraphCanon lists graph-backed alternatives at [jailbreak-evaluation alternatives](/tools/controllability-jailbreak-evaluation/alternatives) and [AutoDefense alternatives](/tools/xhmy-autodefense/alternatives) ([jailbreak-evaluation markdown twin](/tools/controllability-jailbreak-evaluation/alternatives.md), [AutoDefense markdown twin](/tools/xhmy-autodefense/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/controllability-jailbreak-evaluation-vs-xhmy-autodefense.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, jailbreak-evaluation or AutoDefense? jailbreak-evaluation: Dormant. AutoDefense: Slowing. 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 jailbreak-evaluation and AutoDefense? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [jailbreak-evaluation trust report](/tools/controllability-jailbreak-evaluation/trust); [AutoDefense trust report](/tools/xhmy-autodefense/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=controllability-jailbreak-evaluation`](/api/graphcanon/graph?tool=controllability-jailbreak-evaluation) - 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/_