Comparison
awesome-ai-safety vs plexiglass
Verdict
Pick awesome-ai-safety if awesome-ai-safety is a curated list of papers and technical articles focused on ensuring AI quality and safety across various machine learning domains including CV and NLP; pick plexiglass if plexiglass is a toolkit for detecting and mitigating vulnerabilities in Large Language Models through adversarial attacks and deep-learning techniques.
Markdown twin · awesome-ai-safety alternatives · plexiglass alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | awesome-ai-safety | plexiglass |
|---|---|---|
| Maintenance | Dormant (473d since push) As of 3w · github_public_v1 | Slowing (178d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · 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-ai-safety
- A curated list of papers and technical articles on AI Quality & Safety
- plexiglass
- A toolkit for detecting and protecting against vulnerabilities in Large Language Models (LLMs).
Stars
- awesome-ai-safety
- 220
- plexiglass
- 153
Forks
- awesome-ai-safety
- 39
- plexiglass
- 18
Open issues
- awesome-ai-safety
- 17
- plexiglass
- 0
Language
- awesome-ai-safety
- -
- plexiglass
- Python
Adopt for
- awesome-ai-safety
- awesome-ai-safety is a curated list of papers and technical articles focused on ensuring AI quality and safety across various machine learning domains including CV and NLP.
- plexiglass
- Plexiglass is a toolkit for detecting and mitigating vulnerabilities in Large Language Models through adversarial attacks and deep-learning techniques.
Persona
- awesome-ai-safety
- -
- plexiglass
- -
Runtime
- awesome-ai-safety
- -
- plexiglass
- -
License
- awesome-ai-safety
- Apache-2.0
- plexiglass
- Apache-2.0
Last pushed
- awesome-ai-safety
- Apr 14, 2025
- plexiglass
- Feb 4, 2026
Categories
- awesome-ai-safety
- Evaluation & Observability
- plexiglass
- Evaluation & Observability
Trust and health
Maintenance
- awesome-ai-safety
- Dormant (18%)
- plexiglass
- Slowing (36%)
Days since push
- awesome-ai-safety
- 473d
- plexiglass
- 178d
Open issues (now)
- awesome-ai-safety
- 17
- plexiglass
- 0
Full report
- awesome-ai-safety
- Trust report
- plexiglass
- Trust report
Choose awesome-ai-safety if…
- Pricing: The repository is free to use under the Apache-2.0 license. However, external resources linked might have their own licensing terms or costs..
- Tags unique to awesome-ai-safety: ai, ai safety, ai-alignment, ai-quality.
- When you need an aggregated source to explore topics such as AI alignment, robustness, fairness in ML models.
When NOT to use awesome-ai-safety
- Not suitable if your requirement is a repository with hands-on coding examples rather than research papers and articles.
- Avoid this resource if you are searching for datasets or tools that are not in the form of academic literature but practical utilities.
- This platform may not provide sufficient guidance on hardware-specific testing, where practical constraints diverge from theoretical models.
Choose plexiglass if…
- Tags unique to plexiglass: adversarial-attacks, adversarial-machine-learning, cybersecurity, deep-learning.
- When a team needs to evaluate the robustness of their LLM against specific adversarial attack vectors within Python-based projects.
- More recently updated (last pushed Feb 4, 2026).
When NOT to use plexiglass
- If a project is not using Python, because Plexiglass is specifically built for Python environments.
- When the team does not have access to the deep-learning techniques required by Plexiglass, as it heavily relies on such methods to mitigate vulnerabilities.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Giskard-AI/awesome-ai-safety) · observed Aug 1, 2026
- GitHub forks (Giskard-AI/awesome-ai-safety) · observed Aug 1, 2026
- Last push (Giskard-AI/awesome-ai-safety) · observed Apr 14, 2025
- License file (Apache-2.0) · observed Aug 1, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (safellama/plexiglass) · observed Aug 2, 2026
- GitHub forks (safellama/plexiglass) · observed Aug 2, 2026
- Last push (safellama/plexiglass) · observed Feb 4, 2026
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-ai-safety 220 · plexiglass 153 (synced Aug 1, 2026).
Common questions
- What is the difference between awesome-ai-safety and plexiglass?
- awesome-ai-safety: A curated list of papers and technical articles on AI Quality & Safety. plexiglass: A toolkit for detecting and protecting against vulnerabilities in Large Language Models (LLMs).. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-ai-safety over plexiglass?
- Choose awesome-ai-safety over plexiglass when Pricing: The repository is free to use under the Apache-2.0 license. However, external resources linked might have their own licensing terms or costs.; Tags unique to awesome-ai-safety: ai, ai safety, ai-alignment, ai-quality; When you need an aggregated source to explore topics such as AI alignment, robustness, fairness in ML models.
- When should I choose plexiglass over awesome-ai-safety?
- Choose plexiglass over awesome-ai-safety when Tags unique to plexiglass: adversarial-attacks, adversarial-machine-learning, cybersecurity, deep-learning; When a team needs to evaluate the robustness of their LLM against specific adversarial attack vectors within Python-based projects; More recently updated (last pushed Feb 4, 2026).
- When should I avoid awesome-ai-safety?
- Not suitable if your requirement is a repository with hands-on coding examples rather than research papers and articles. Avoid this resource if you are searching for datasets or tools that are not in the form of academic literature but practical utilities. This platform may not provide sufficient guidance on hardware-specific testing, where practical constraints diverge from theoretical models.
- When should I avoid plexiglass?
- If a project is not using Python, because Plexiglass is specifically built for Python environments. When the team does not have access to the deep-learning techniques required by Plexiglass, as it heavily relies on such methods to mitigate vulnerabilities.
- Is awesome-ai-safety or plexiglass more popular on GitHub?
- awesome-ai-safety has more GitHub stars (220 vs 153). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-ai-safety and plexiglass open source?
- Yes - both are open-source projects on GitHub (awesome-ai-safety: Apache-2.0, plexiglass: Apache-2.0).
- Where can I find alternatives to awesome-ai-safety or plexiglass?
- GraphCanon lists graph-backed alternatives at awesome-ai-safety alternatives and plexiglass alternatives (awesome-ai-safety markdown twin, plexiglass 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-ai-safety or plexiglass?
- awesome-ai-safety: Dormant. plexiglass: 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 awesome-ai-safety and plexiglass?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-ai-safety trust report; plexiglass trust report.