Comparison
do-not-answer vs AutoDefense
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.
Markdown twin · do-not-answer alternatives · AutoDefense alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | do-not-answer | AutoDefense |
|---|---|---|
| Maintenance | Dormant (788d since push) As of 2w · github_public_v1 | Slowing (201d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- do-not-answer
- A Dataset for Evaluating Safeguards in LLMs
- AutoDefense
- Multi-Agent LLM Defense against Jailbreak Attacks
Stars
- do-not-answer
- 339
- AutoDefense
- 68
Forks
- do-not-answer
- 29
- AutoDefense
- 20
Open issues
- do-not-answer
- 0
- AutoDefense
- 1
Language
- do-not-answer
- Jupyter Notebook
- AutoDefense
- Python
Adopt for
- do-not-answer
- Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses.
- AutoDefense
- AutoDefense uses a multi-agent framework to mitigate jailbreak attacks on LLMs, installed via Python.
Persona
- do-not-answer
- -
- AutoDefense
- -
Runtime
- do-not-answer
- -
- AutoDefense
- -
License
- do-not-answer
- Dual licensing model, datasets under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and source files under Apache 2.0 license.
- AutoDefense
- MIT
Last pushed
- do-not-answer
- Jun 7, 2024
- AutoDefense
- Jan 15, 2026
Categories
- do-not-answer
- Evaluation & Observability
- AutoDefense
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- do-not-answer
- Dormant (18%)
- AutoDefense
- Slowing (36%)
Days since push
- do-not-answer
- 788d
- AutoDefense
- 201d
Open issues (now)
- do-not-answer
- 0
- AutoDefense
- 1
Owner type
- do-not-answer
- Organization
- AutoDefense
- User
Full report
- do-not-answer
- Trust report
- AutoDefense
- Trust report
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.
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.
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Libr-AI/do-not-answer) · observed Aug 5, 2026
- GitHub forks (Libr-AI/do-not-answer) · observed Aug 5, 2026
- Last push (Libr-AI/do-not-answer) · observed Jun 7, 2024
- License file (Apache-2.0) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (XHMY/AutoDefense) · observed Aug 5, 2026
- GitHub forks (XHMY/AutoDefense) · observed Aug 5, 2026
- Last push (XHMY/AutoDefense) · observed Jan 15, 2026
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: do-not-answer 339 · AutoDefense 68 (synced Aug 5, 2026).
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 and AutoDefense alternatives (do-not-answer markdown twin, AutoDefense markdown twin), 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 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; AutoDefense trust report.