--- title: "plexiglass vs AI-Infra-Guard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/safellama-plexiglass-vs-tencent-ai-infra-guard" tools: ["safellama-plexiglass", "tencent-ai-infra-guard"] --- # plexiglass vs AI-Infra-Guard *GraphCanon updated Aug 2, 2026* ## Verdict Pick plexiglass if plexiglass is a toolkit for detecting and mitigating vulnerabilities in Large Language Models through adversarial attacks and deep-learning techniques; pick AI-Infra-Guard if aI-Infra-Guard is designed for organizations looking to secure their AI infrastructures comprehensively through various scanning and evaluation tools. [plexiglass](https://github.com/safellama/plexiglass) reports 153 GitHub stars, 18 forks, and 0 open issues, last pushed Feb 4, 2026. [AI-Infra-Guard](https://tencent.github.io/AI-Infra-Guard/) has 4.3k stars, 419 forks, and 13 open issues, last pushed Jul 28, 2026. Figures are from public GitHub metadata via [plexiglass's repository](https://github.com/safellama/plexiglass) and [AI-Infra-Guard's repository](https://github.com/Tencent/AI-Infra-Guard). | | [plexiglass](/tools/safellama-plexiglass.md) | [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) | | --- | --- | --- | | Tagline | A toolkit for detecting and protecting against vulnerabilities in Large Language Models (LLMs). | A full-stack AI Red Teaming platform securing AI ecosystems | | Stars | 153 | 4,316 | | Forks | 18 | 419 | | Open issues | 0 | 13 | | Language | Python | Python | | Adopt for | Plexiglass is a toolkit for detecting and mitigating vulnerabilities in Large Language Models through adversarial attacks and deep-learning techniques. | AI-Infra-Guard is designed for organizations looking to secure their AI infrastructures comprehensively through various scanning and evaluation tools. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [plexiglass](/tools/safellama-plexiglass.md) | [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 178d | 0d | | Open issues (now) | 0 | 13 | | Full report | [trust report](/tools/safellama-plexiglass/trust.md) | [trust report](/tools/tencent-ai-infra-guard/trust.md) | ## Shared compatibility - **Python**: [plexiglass](/tools/safellama-plexiglass.md) - Python runtime; [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) - Python runtime ## 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. ## Decision facts: AI-Infra-Guard - **Adopt for:** AI-Infra-Guard is designed for organizations looking to secure their AI infrastructures comprehensively through various scanning and evaluation tools. ## Choose when ### 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. - Leaner open-issue backlog (0). ### Choose AI-Infra-Guard if… - Tags unique to AI-Infra-Guard: agent-security, ai-red-teaming, llm-evaluation, security-tools. - Also covers LLM Frameworks. - AI-Infra-Guard ships Docker support for self-hosted deployment. - If you need advanced LLM jailbreak evaluation capabilities specific to the vulnerabilities identified by Tencent's research, consider using AI-Infra-Guard. ## 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. ## When NOT to use AI-Infra-Guard - Avoid if you are looking exclusively for a tool that focuses solely on the runtime behavior of LLMs without broader infrastructural scanning capabilities. - Not recommended when your primary focus is on network-level security rather than comprehensive AI infrastructure security assessments and evaluations. ## Common questions ### What is the difference between plexiglass and AI-Infra-Guard? plexiglass: A toolkit for detecting and protecting against vulnerabilities in Large Language Models (LLMs).. AI-Infra-Guard: A full-stack AI Red Teaming platform securing AI ecosystems. See the comparison table for live GitHub stats and shared categories. ### When should I choose plexiglass over AI-Infra-Guard? Choose plexiglass over AI-Infra-Guard 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; Leaner open-issue backlog (0). ### When should I choose AI-Infra-Guard over plexiglass? Choose AI-Infra-Guard over plexiglass when Tags unique to AI-Infra-Guard: agent-security, ai-red-teaming, llm-evaluation, security-tools; Also covers LLM Frameworks; AI-Infra-Guard ships Docker support for self-hosted deployment; If you need advanced LLM jailbreak evaluation capabilities specific to the vulnerabilities identified by Tencent's research, consider using AI-Infra-Guard. ### 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. ### When should I avoid AI-Infra-Guard? Avoid if you are looking exclusively for a tool that focuses solely on the runtime behavior of LLMs without broader infrastructural scanning capabilities. Not recommended when your primary focus is on network-level security rather than comprehensive AI infrastructure security assessments and evaluations. ### Is plexiglass or AI-Infra-Guard more popular on GitHub? AI-Infra-Guard has more GitHub stars (4,316 vs 153). Stars measure visibility, not whether either tool fits your constraints. ### Are plexiglass and AI-Infra-Guard open source? Yes - both are open-source projects on GitHub (plexiglass: Apache-2.0, AI-Infra-Guard: Apache-2.0). ### Where can I find alternatives to plexiglass or AI-Infra-Guard? GraphCanon lists graph-backed alternatives at [plexiglass alternatives](/tools/safellama-plexiglass/alternatives) and [AI-Infra-Guard alternatives](/tools/tencent-ai-infra-guard/alternatives) ([plexiglass markdown twin](/tools/safellama-plexiglass/alternatives.md), [AI-Infra-Guard markdown twin](/tools/tencent-ai-infra-guard/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/safellama-plexiglass-vs-tencent-ai-infra-guard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, plexiglass or AI-Infra-Guard? plexiglass: Slowing. AI-Infra-Guard: Very active. 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 plexiglass and AI-Infra-Guard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [plexiglass trust report](/tools/safellama-plexiglass/trust); [AI-Infra-Guard trust report](/tools/tencent-ai-infra-guard/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=safellama-plexiglass`](/api/graphcanon/graph?tool=safellama-plexiglass) - 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/_