--- title: "llm-guard vs plexiglass" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/protectai-llm-guard-vs-safellama-plexiglass" tools: ["protectai-llm-guard", "safellama-plexiglass"] --- # llm-guard vs plexiglass *GraphCanon updated Oct 6, 2026* ## Verdict Pick llm-guard if lLM Guard is a toolkit tailored for securing interactions with large language models, focusing on safeguarding against prompt injection and adversarial attacks; pick plexiglass if plexiglass is a Python-based toolkit aimed at detecting and mitigating vulnerabilities in Large Language Models, with a focus on cybersecurity and adversarial attacks. [llm-guard](https://protectai.github.io/llm-guard/) reports 3.2k GitHub stars, 462 forks, and 38 open issues, last pushed Jul 8, 2026. [plexiglass](https://github.com/w4-advisory/plexiglass) has 156 stars, 18 forks, and 0 open issues, last pushed Feb 4, 2026. Figures are from public GitHub metadata via [llm-guard's repository](https://github.com/protectai/llm-guard) and [plexiglass's repository](https://github.com/w4-advisory/plexiglass). | | [llm-guard](/tools/protectai-llm-guard.md) | [plexiglass](/tools/safellama-plexiglass.md) | | --- | --- | --- | | Tagline | The Security Toolkit for LLM Interactions | A toolkit for detecting and protecting against vulnerabilities in Large Language Models (LLMs). | | Stars | 3,213 | 156 | | Forks | 462 | 18 | | Open issues | 38 | 0 | | Language | Python | Python | | Adopt for | LLM Guard is a toolkit tailored for securing interactions with large language models, focusing on safeguarding against prompt injection and adversarial attacks. | Plexiglass is a Python-based toolkit aimed at detecting and mitigating vulnerabilities in Large Language Models, with a focus on cybersecurity and adversarial attacks. | | Persona | - | - | | Runtime | - | - | | License | MIT | Plexiglass is available under the Apache-2.0 license, allowing for broad usage and modification with attribution. | | Categories | Developer Tools, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-guard](/tools/protectai-llm-guard.md) | [plexiglass](/tools/safellama-plexiglass.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Slowing (36%) | | Days since push | 89d | 225d | | Archived on GitHub | Yes | No | | Open issues (now) | 38 | 0 | | Stars delta | +9 (30d) | +3 (30d) | | Full report | [trust report](/tools/protectai-llm-guard/trust.md) | [trust report](/tools/safellama-plexiglass/trust.md) | ## Shared compatibility - **Python**: [llm-guard](/tools/protectai-llm-guard.md) - Python runtime; [plexiglass](/tools/safellama-plexiglass.md) - Python runtime ## Decision facts: llm-guard - **Requirements:** Min 2 GB RAM; Ensure you have Python version 3.9 or higher.; Some advanced features require additional libraries which are automatically installed as needed. - **Adopt for:** LLM Guard is a toolkit tailored for securing interactions with large language models, focusing on safeguarding against prompt injection and adversarial attacks. ## Decision facts: plexiglass - **Pricing:** freemium - The toolkit is free to use under the Apache-2.0 license, but advanced features or support might incur costs. - **Requirements:** Min 4 GB RAM; Python environment is required for installation and operation. - **Adopt for:** Plexiglass is a Python-based toolkit aimed at detecting and mitigating vulnerabilities in Large Language Models, with a focus on cybersecurity and adversarial attacks. - **License detail:** Plexiglass is available under the Apache-2.0 license, allowing for broad usage and modification with attribution. ## Choose when ### Choose llm-guard if… - License: llm-guard is MIT, plexiglass is Apache-2.0. - Requirements: Min 2 GB RAM; Ensure you have Python version 3.9 or higher.; Some advanced features require additional libraries which are automatically installed as needed.. - Tags unique to llm-guard: chatgpt, large-language-models, llm-security, prompt-engineering. - Also covers Developer Tools. - - You need to secure your application from sophisticated prompt injection techniques. ### Choose plexiglass if… - License: plexiglass is Apache-2.0, llm-guard is MIT. - Pricing: The toolkit is free to use under the Apache-2.0 license, but advanced features or support might incur costs.. - Requirements: Min 4 GB RAM; Python environment is required for installation and operation.. - Tags unique to plexiglass: adversarial-attacks, cybersecurity, deep-learning, deep-neural-networks. - When you need to evaluate the robustness of Large Language Models against adversarial attacks. ## When NOT to use llm-guard - - If you are working in a low-security environment or with small-scale projects where advanced security mechanisms are not necessary. - - In cases where integrating external libraries and ensuring Python version compatibility may introduce complexities that outweigh the benefits. ## When NOT to use plexiglass - If your project does not involve Large Language Models and you are looking for general cybersecurity tools. - When you are working with small-scale models where the overhead of using Plexiglass might not be justified. ## Common questions ### What is the difference between llm-guard and plexiglass? llm-guard: The Security Toolkit for LLM Interactions. 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 llm-guard over plexiglass? Choose llm-guard over plexiglass when License: llm-guard is MIT, plexiglass is Apache-2.0; Requirements: Min 2 GB RAM; Ensure you have Python version 3.9 or higher.; Some advanced features require additional libraries which are automatically installed as needed.; Tags unique to llm-guard: chatgpt, large-language-models, llm-security, prompt-engineering; Also covers Developer Tools; - You need to secure your application from sophisticated prompt injection techniques. ### When should I choose plexiglass over llm-guard? Choose plexiglass over llm-guard when License: plexiglass is Apache-2.0, llm-guard is MIT; Pricing: The toolkit is free to use under the Apache-2.0 license, but advanced features or support might incur costs.; Requirements: Min 4 GB RAM; Python environment is required for installation and operation.; Tags unique to plexiglass: adversarial-attacks, cybersecurity, deep-learning, deep-neural-networks; When you need to evaluate the robustness of Large Language Models against adversarial attacks. ### When should I avoid llm-guard? - If you are working in a low-security environment or with small-scale projects where advanced security mechanisms are not necessary. - In cases where integrating external libraries and ensuring Python version compatibility may introduce complexities that outweigh the benefits. ### When should I avoid plexiglass? If your project does not involve Large Language Models and you are looking for general cybersecurity tools. When you are working with small-scale models where the overhead of using Plexiglass might not be justified. ### Is llm-guard or plexiglass more popular on GitHub? llm-guard has more GitHub stars (3,213 vs 156). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-guard and plexiglass open source? Yes - both are open-source projects on GitHub (llm-guard: MIT, plexiglass: Apache-2.0). ### Where can I find alternatives to llm-guard or plexiglass? GraphCanon lists graph-backed alternatives at [llm-guard alternatives](/tools/protectai-llm-guard/alternatives) and [plexiglass alternatives](/tools/safellama-plexiglass/alternatives) ([llm-guard markdown twin](/tools/protectai-llm-guard/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/protectai-llm-guard-vs-safellama-plexiglass.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-guard or plexiglass? llm-guard: Archived. 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 llm-guard and plexiglass? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-guard trust report](/tools/protectai-llm-guard/trust); [plexiglass trust report](/tools/safellama-plexiglass/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=protectai-llm-guard`](/api/graphcanon/graph?tool=protectai-llm-guard) - 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/_