--- title: "AutoAudit vs agentic_security" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ddzipp-autoaudit-vs-msoedov-agentic-security" tools: ["ddzipp-autoaudit", "msoedov-agentic-security"] --- # AutoAudit vs agentic_security *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 agentic_security if agentic Security is an agent-based framework for scanning vulnerabilities in large language models with a Python-based toolkit for robust security assessments via fuzz testing. [AutoAudit](https://github.com/ddzipp/AutoAudit) reports 354 GitHub stars, 38 forks, and 4 open issues, last pushed Feb 28, 2025. [agentic_security](https://agentic-security.vercel.app) has 1.9k stars, 270 forks, and 70 open issues, last pushed Jun 23, 2026. Figures are from public GitHub metadata via [AutoAudit's repository](https://github.com/ddzipp/AutoAudit) and [agentic_security's repository](https://github.com/msoedov/agentic_security). | | [AutoAudit](/tools/ddzipp-autoaudit.md) | [agentic_security](/tools/msoedov-agentic-security.md) | | --- | --- | --- | | Tagline | LLM for Cyber Security | Agentic LLM Vulnerability Scanner / AI red teaming kit | | Stars | 354 | 1,943 | | Forks | 38 | 270 | | Open issues | 4 | 70 | | 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. | Agentic Security is an agent-based framework for scanning vulnerabilities in large language models with a Python-based toolkit for robust security assessments via fuzz testing. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 - Permissive license encouraging free use and modification under the condition of preserving notices. | | 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) | [agentic_security](/tools/msoedov-agentic-security.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 542d | 35d | | Open issues (now) | 4 | 70 | | Stars delta | -1 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/ddzipp-autoaudit/trust.md) | [trust report](/tools/msoedov-agentic-security/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: agentic_security - **Adopt for:** Agentic Security is an agent-based framework for scanning vulnerabilities in large language models with a Python-based toolkit for robust security assessments via fuzz testing. - **License detail:** Apache-2.0 - Permissive license encouraging free use and modification under the condition of preserving notices. ## Choose when ### Choose AutoAudit if… - AutoAudit is primarily HTML; agentic_security is Python. - License: AutoAudit is MIT, agentic_security 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 agentic_security if… - agentic_security is primarily Python; AutoAudit is HTML. - License: agentic_security is Apache-2.0, AutoAudit is MIT. - Tags unique to agentic_security: agent-framework, fuzzing, llm-evaluation, red-teaming. - Also covers LLM Frameworks. - agentic_security ships Docker support for self-hosted deployment. - Developers need to ensure their LLMs are secure from jailbreak attempts and vulnerabilities. ## 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 agentic_security - Teams require solutions that do not involve agent frameworks for vulnerability scanning. - Projects seek a no-fuzz-testing approach for evaluating LLM security. ## Common questions ### What is the difference between AutoAudit and agentic_security? AutoAudit: LLM for Cyber Security. agentic_security: Agentic LLM Vulnerability Scanner / AI red teaming kit. See the comparison table for live GitHub stats and shared categories. ### When should I choose AutoAudit over agentic_security? Choose AutoAudit over agentic_security when AutoAudit is primarily HTML; agentic_security is Python; License: AutoAudit is MIT, agentic_security 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 agentic_security over AutoAudit? Choose agentic_security over AutoAudit when agentic_security is primarily Python; AutoAudit is HTML; License: agentic_security is Apache-2.0, AutoAudit is MIT; Tags unique to agentic_security: agent-framework, fuzzing, llm-evaluation, red-teaming; Also covers LLM Frameworks; agentic_security ships Docker support for self-hosted deployment; Developers need to ensure their LLMs are secure from jailbreak attempts and vulnerabilities. ### 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 agentic_security? Teams require solutions that do not involve agent frameworks for vulnerability scanning. Projects seek a no-fuzz-testing approach for evaluating LLM security. ### Is AutoAudit or agentic_security more popular on GitHub? agentic_security has more GitHub stars (1,943 vs 354). Stars measure visibility, not whether either tool fits your constraints. ### Are AutoAudit and agentic_security open source? Yes - both are open-source projects on GitHub (AutoAudit: MIT, agentic_security: Apache-2.0). ### Where can I find alternatives to AutoAudit or agentic_security? GraphCanon lists graph-backed alternatives at [AutoAudit alternatives](/tools/ddzipp-autoaudit/alternatives) and [agentic_security alternatives](/tools/msoedov-agentic-security/alternatives) ([AutoAudit markdown twin](/tools/ddzipp-autoaudit/alternatives.md), [agentic_security markdown twin](/tools/msoedov-agentic-security/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-msoedov-agentic-security.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, AutoAudit or agentic_security? AutoAudit: Dormant. agentic_security: Steady. 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 agentic_security? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AutoAudit trust report](/tools/ddzipp-autoaudit/trust); [agentic_security trust report](/tools/msoedov-agentic-security/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/_