--- title: "awesome-ai-safety vs plexiglass" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/giskard-ai-awesome-ai-safety-vs-safellama-plexiglass" tools: ["giskard-ai-awesome-ai-safety", "safellama-plexiglass"] --- # awesome-ai-safety vs plexiglass *GraphCanon updated Aug 2, 2026* ## 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. [awesome-ai-safety](https://giskard.ai) reports 220 GitHub stars, 39 forks, and 17 open issues, last pushed Apr 14, 2025. [plexiglass](https://github.com/safellama/plexiglass) has 153 stars, 18 forks, and 0 open issues, last pushed Feb 4, 2026. Figures are from public GitHub metadata via [awesome-ai-safety's repository](https://github.com/Giskard-AI/awesome-ai-safety) and [plexiglass's repository](https://github.com/safellama/plexiglass). | | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | [plexiglass](/tools/safellama-plexiglass.md) | | --- | --- | --- | | Tagline | A curated list of papers and technical articles on AI Quality & Safety | A toolkit for detecting and protecting against vulnerabilities in Large Language Models (LLMs). | | Stars | 220 | 153 | | Forks | 39 | 18 | | Open issues | 17 | 0 | | Language | - | Python | | Adopt for | 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 is a toolkit for detecting and mitigating vulnerabilities in Large Language Models through adversarial attacks and deep-learning techniques. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-ai-safety](/tools/giskard-ai-awesome-ai-safety.md) | [plexiglass](/tools/safellama-plexiglass.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 473d | 178d | | Open issues (now) | 17 | 0 | | Full report | [trust report](/tools/giskard-ai-awesome-ai-safety/trust.md) | [trust report](/tools/safellama-plexiglass/trust.md) | ## Decision facts: awesome-ai-safety - **Pricing:** freemium - The repository is free to use under the Apache-2.0 license. However, external resources linked might have their own licensing terms or costs. - **Adopt for:** 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. ## Decision facts: plexiglass - **Adopt for:** Plexiglass is a toolkit for detecting and mitigating vulnerabilities in Large Language Models through adversarial attacks and deep-learning techniques. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/giskard-ai-awesome-ai-safety/alternatives) and [plexiglass alternatives](/tools/safellama-plexiglass/alternatives) ([awesome-ai-safety markdown twin](/tools/giskard-ai-awesome-ai-safety/alternatives.md), [plexiglass markdown twin](/tools/safellama-plexiglass/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/giskard-ai-awesome-ai-safety-vs-safellama-plexiglass.md) 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](/tools/giskard-ai-awesome-ai-safety/trust); [plexiglass trust report](/tools/safellama-plexiglass/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=giskard-ai-awesome-ai-safety`](/api/graphcanon/graph?tool=giskard-ai-awesome-ai-safety) - 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/_