--- title: "AutoAudit vs AI-Infra-Guard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ddzipp-autoaudit-vs-tencent-ai-infra-guard" tools: ["ddzipp-autoaudit", "tencent-ai-infra-guard"] --- # AutoAudit vs AI-Infra-Guard *GraphCanon updated Aug 24, 2026* ## Verdict Pick AutoAudit if autoAudit leverages LLMs specifically for cyber security tasks and supports custom fine-tuning through models such as GPT, LLAMA, LoRA, and QLORA; 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. [AutoAudit](https://github.com/ddzipp/AutoAudit) reports 354 GitHub stars, 38 forks, and 4 open issues, last pushed Feb 28, 2025. [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 [AutoAudit's repository](https://github.com/ddzipp/AutoAudit) and [AI-Infra-Guard's repository](https://github.com/Tencent/AI-Infra-Guard). | | [AutoAudit](/tools/ddzipp-autoaudit.md) | [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) | | --- | --- | --- | | Tagline | LLM for Cyber Security | A full-stack AI Red Teaming platform securing AI ecosystems | | Stars | 354 | 4,316 | | Forks | 38 | 419 | | Open issues | 4 | 13 | | Language | HTML | Python | | Adopt for | AutoAudit leverages LLMs specifically for cyber security tasks and supports custom fine-tuning through models such as GPT, LLAMA, LoRA, and QLORA. | AI-Infra-Guard is designed for organizations looking to secure their AI infrastructures comprehensively through various scanning and evaluation tools. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AutoAudit](/tools/ddzipp-autoaudit.md) | [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 542d | 0d | | Open issues (now) | 4 | 13 | | Stars delta | -1 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/ddzipp-autoaudit/trust.md) | [trust report](/tools/tencent-ai-infra-guard/trust.md) | ## Decision facts: AutoAudit - **Adopt for:** AutoAudit leverages LLMs specifically for cyber security tasks and supports custom fine-tuning through models such as GPT, LLAMA, LoRA, and QLORA. ## 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 AutoAudit if… - AutoAudit is primarily HTML; AI-Infra-Guard is Python. - License: AutoAudit is MIT, AI-Infra-Guard is Apache-2.0. - Tags unique to AutoAudit: cyber-security, fine-tuning, gpt, llama. - Also covers Model Training. - When your project requires a language model focused on cyber security applications rather than general content generation. ### Choose AI-Infra-Guard if… - AI-Infra-Guard is primarily Python; AutoAudit is HTML. - License: AI-Infra-Guard is Apache-2.0, AutoAudit is MIT. - Tags unique to AI-Infra-Guard: agent-security, ai-red-teaming, llm-evaluation, skills-security. - 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 AutoAudit - For projects needing broad, general-purpose text generation that does not require cyber security expertise embedded in the model. - In scenarios where proprietary data privacy is a concern, given AutoAudit's nature as an LLM for cyber security may imply certain data processing policies could be less flexible. ## 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 AutoAudit and AI-Infra-Guard? AutoAudit: LLM for Cyber Security. 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 AutoAudit over AI-Infra-Guard? Choose AutoAudit over AI-Infra-Guard when AutoAudit is primarily HTML; AI-Infra-Guard is Python; License: AutoAudit is MIT, AI-Infra-Guard is Apache-2.0; Tags unique to AutoAudit: cyber-security, fine-tuning, gpt, llama; Also covers Model Training; When your project requires a language model focused on cyber security applications rather than general content generation. ### When should I choose AI-Infra-Guard over AutoAudit? Choose AI-Infra-Guard over AutoAudit when AI-Infra-Guard is primarily Python; AutoAudit is HTML; License: AI-Infra-Guard is Apache-2.0, AutoAudit is MIT; Tags unique to AI-Infra-Guard: agent-security, ai-red-teaming, llm-evaluation, skills-security; 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 AutoAudit? For projects needing broad, general-purpose text generation that does not require cyber security expertise embedded in the model. In scenarios where proprietary data privacy is a concern, given AutoAudit's nature as an LLM for cyber security may imply certain data processing policies could be less flexible. ### 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 AutoAudit or AI-Infra-Guard more popular on GitHub? AI-Infra-Guard has more GitHub stars (4,316 vs 354). Stars measure visibility, not whether either tool fits your constraints. ### Are AutoAudit and AI-Infra-Guard open source? Yes - both are open-source projects on GitHub (AutoAudit: MIT, AI-Infra-Guard: Apache-2.0). ### Where can I find alternatives to AutoAudit or AI-Infra-Guard? GraphCanon lists graph-backed alternatives at [AutoAudit alternatives](/tools/ddzipp-autoaudit/alternatives) and [AI-Infra-Guard alternatives](/tools/tencent-ai-infra-guard/alternatives) ([AutoAudit markdown twin](/tools/ddzipp-autoaudit/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/ddzipp-autoaudit-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, AutoAudit or AI-Infra-Guard? AutoAudit: Dormant. 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 AutoAudit and AI-Infra-Guard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AutoAudit trust report](/tools/ddzipp-autoaudit/trust); [AI-Infra-Guard trust report](/tools/tencent-ai-infra-guard/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ddzipp-autoaudit`](/api/graphcanon/graph?tool=ddzipp-autoaudit) - 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/_