Comparison
awesome-llm-security vs do-not-answer
Verdict
Pick awesome-llm-security if awesome LLM Security is a curated list of resources related to the security aspects of large language models. It covers various attack methodologies, defenses, and platform security through papers, benchmarks, tools, and; pick do-not-answer if dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses.
Markdown twin · awesome-llm-security alternatives · do-not-answer alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-llm-security | do-not-answer |
|---|---|---|
| Maintenance | Slowing (351d since push) As of 2w · github_public_v1 | Dormant (788d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization 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
- awesome-llm-security
- A curation of tools, documents and projects about LLM Security
- do-not-answer
- A Dataset for Evaluating Safeguards in LLMs
Stars
- awesome-llm-security
- 1.7k
- do-not-answer
- 339
Forks
- awesome-llm-security
- 312
- do-not-answer
- 29
Open issues
- awesome-llm-security
- 173
- do-not-answer
- 0
Language
- awesome-llm-security
- -
- do-not-answer
- Jupyter Notebook
Adopt for
- awesome-llm-security
- Awesome LLM Security is a curated list of resources related to the security aspects of large language models. It covers various attack methodologies, defenses, and platform security through papers, benchmarks, tools, and
- do-not-answer
- Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses.
Persona
- awesome-llm-security
- -
- do-not-answer
- -
Runtime
- awesome-llm-security
- -
- do-not-answer
- -
License
- awesome-llm-security
- -
- 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.
Last pushed
- awesome-llm-security
- Aug 20, 2025
- do-not-answer
- Jun 7, 2024
Categories
- awesome-llm-security
- Evaluation & Observability
- do-not-answer
- Evaluation & Observability
Trust and health
Maintenance
- awesome-llm-security
- Slowing (36%)
- do-not-answer
- Dormant (18%)
Days since push
- awesome-llm-security
- 351d
- do-not-answer
- 788d
Open issues (now)
- awesome-llm-security
- 173
- do-not-answer
- 0
Full report
- awesome-llm-security
- Trust report
- do-not-answer
- Trust report
Choose awesome-llm-security if…
- Pricing: As an open-source project without defined pricing models, its use is generally free under the terms of its license (license details are not provided)..
- Tags unique to awesome-llm-security: awesome-list, llm, security.
- When you are specifically looking for detailed information on both white-box and black-box attacks targeted at Large Language Models (LLMs), which 'awesome-llm-security' comprehensively catalogs.
When NOT to use awesome-llm-security
- When your primary interest is in general software security or vulnerabilities unrelated to language models, since 'awesome-llm-security' zeroes in on attack vectors specifically for LLMs.
- If you are solely interested in tools and methods that are not publicly discussed or peer-reviewed; the repository focuses on documented approaches within reputable academic publications.
Choose do-not-answer if…
- 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.
- Leaner open-issue backlog (0).
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (corca-ai/awesome-llm-security) · observed Aug 6, 2026
- GitHub forks (corca-ai/awesome-llm-security) · observed Aug 6, 2026
- Last push (corca-ai/awesome-llm-security) · observed Aug 20, 2025
- License file (unknown) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: awesome-llm-security 1.7k · do-not-answer 339 (synced Aug 6, 2026).
Common questions
- What is the difference between awesome-llm-security and do-not-answer?
- awesome-llm-security: A curation of tools, documents and projects about LLM Security. do-not-answer: A Dataset for Evaluating Safeguards in LLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-llm-security over do-not-answer?
- Choose awesome-llm-security over do-not-answer when Pricing: As an open-source project without defined pricing models, its use is generally free under the terms of its license (license details are not provided).; Tags unique to awesome-llm-security: awesome-list, llm, security; When you are specifically looking for detailed information on both white-box and black-box attacks targeted at Large Language Models (LLMs), which 'awesome-llm-security' comprehensively catalogs.
- When should I choose do-not-answer over awesome-llm-security?
- Choose do-not-answer over awesome-llm-security when 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; Leaner open-issue backlog (0).
- When should I avoid awesome-llm-security?
- When your primary interest is in general software security or vulnerabilities unrelated to language models, since 'awesome-llm-security' zeroes in on attack vectors specifically for LLMs. If you are solely interested in tools and methods that are not publicly discussed or peer-reviewed; the repository focuses on documented approaches within reputable academic publications.
- 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.
- Is awesome-llm-security or do-not-answer more popular on GitHub?
- awesome-llm-security has more GitHub stars (1,672 vs 339). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-llm-security and do-not-answer open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-llm-security or do-not-answer?
- GraphCanon lists graph-backed alternatives at awesome-llm-security alternatives and do-not-answer alternatives (awesome-llm-security markdown twin, do-not-answer 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, awesome-llm-security or do-not-answer?
- awesome-llm-security: Slowing. do-not-answer: 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 awesome-llm-security and do-not-answer?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-llm-security trust report; do-not-answer trust report.