--- title: "do-not-answer vs AutoDefense" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/libr-ai-do-not-answer-vs-xhmy-autodefense" tools: ["libr-ai-do-not-answer", "xhmy-autodefense"] --- # do-not-answer vs AutoDefense *GraphCanon updated Aug 5, 2026* ## Verdict Pick do-not-answer if dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses; pick AutoDefense if autoDefense uses a multi-agent framework to mitigate jailbreak attacks on LLMs, installed via Python. [do-not-answer](https://github.com/Libr-AI/do-not-answer) reports 339 GitHub stars, 29 forks, and 0 open issues, last pushed Jun 7, 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 [do-not-answer's repository](https://github.com/Libr-AI/do-not-answer) and [AutoDefense's repository](https://github.com/XHMY/AutoDefense). | | [do-not-answer](/tools/libr-ai-do-not-answer.md) | [AutoDefense](/tools/xhmy-autodefense.md) | | --- | --- | --- | | Tagline | A Dataset for Evaluating Safeguards in LLMs | Multi-Agent LLM Defense against Jailbreak Attacks | | Stars | 339 | 68 | | Forks | 29 | 20 | | Open issues | 0 | 1 | | Language | Jupyter Notebook | Python | | Adopt for | Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses. | AutoDefense uses a multi-agent framework to mitigate jailbreak attacks on LLMs, installed via Python. | | Persona | - | - | | Runtime | - | - | | License | Dual licensing model, datasets under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and source files under Apache 2.0 license. | MIT | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [do-not-answer](/tools/libr-ai-do-not-answer.md) | [AutoDefense](/tools/xhmy-autodefense.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 788d | 201d | | Open issues (now) | 0 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/libr-ai-do-not-answer/trust.md) | [trust report](/tools/xhmy-autodefense/trust.md) | ## Decision facts: do-not-answer - **Adopt for:** Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses. - **License detail:** Dual licensing model, datasets under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and source files under Apache 2.0 license. ## Decision facts: AutoDefense - **Adopt for:** AutoDefense uses a multi-agent framework to mitigate jailbreak attacks on LLMs, installed via Python. ## Choose when ### Choose do-not-answer if… - do-not-answer is primarily Jupyter Notebook; AutoDefense is Python. - License: do-not-answer is Apache-2.0, AutoDefense is MIT. - Tags unique to do-not-answer: datasets, ethical ai, llm-evaluation, safeguard testing. - To assess the reliability of safeguards implemented in your Large Language Model. ### Choose AutoDefense if… - AutoDefense is primarily Python; do-not-answer is Jupyter Notebook. - License: AutoDefense is MIT, do-not-answer 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 do-not-answer - If you require tools for direct implementation or fine-tuning LLMs rather than evaluating them. - Your project does not involve assessing ethical compliance or safeguard measures within language models. ## 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 do-not-answer and AutoDefense? do-not-answer: A Dataset for Evaluating Safeguards in LLMs. AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks. See the comparison table for live GitHub stats and shared categories. ### When should I choose do-not-answer over AutoDefense? Choose do-not-answer over AutoDefense when do-not-answer is primarily Jupyter Notebook; AutoDefense is Python; License: do-not-answer is Apache-2.0, AutoDefense is MIT; Tags unique to do-not-answer: datasets, ethical ai, llm-evaluation, safeguard testing; To assess the reliability of safeguards implemented in your Large Language Model. ### When should I choose AutoDefense over do-not-answer? Choose AutoDefense over do-not-answer when AutoDefense is primarily Python; do-not-answer is Jupyter Notebook; License: AutoDefense is MIT, do-not-answer 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 do-not-answer? If you require tools for direct implementation or fine-tuning LLMs rather than evaluating them. Your project does not involve assessing ethical compliance or safeguard measures within language models. ### 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 do-not-answer or AutoDefense more popular on GitHub? do-not-answer has more GitHub stars (339 vs 68). Stars measure visibility, not whether either tool fits your constraints. ### Are do-not-answer and AutoDefense open source? Yes - both are open-source projects on GitHub (do-not-answer: Apache-2.0, AutoDefense: MIT). ### Where can I find alternatives to do-not-answer or AutoDefense? GraphCanon lists graph-backed alternatives at [do-not-answer alternatives](/tools/libr-ai-do-not-answer/alternatives) and [AutoDefense alternatives](/tools/xhmy-autodefense/alternatives) ([do-not-answer markdown twin](/tools/libr-ai-do-not-answer/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/libr-ai-do-not-answer-vs-xhmy-autodefense.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, do-not-answer or AutoDefense? do-not-answer: 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 do-not-answer and AutoDefense? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [do-not-answer trust report](/tools/libr-ai-do-not-answer/trust); [AutoDefense trust report](/tools/xhmy-autodefense/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=libr-ai-do-not-answer`](/api/graphcanon/graph?tool=libr-ai-do-not-answer) - 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/_