Awesome AI Security
github.com/ottosulin/awesome-ai-security- Category
- Cybersecurity
- Rank
- No. 970Tools index
Previous survey · No. 951 ·
- Pricing
- Open Source
- Platform
- web
- Type
- TOOL
- Builder
- @ottosulin
- GitHub
- 1.5k stars
- Date
About
A comprehensive curated list of AI security resources including frameworks, standards, offensive and defensive tools, learning materials, and open source security tools. Essential for security professionals working with AI systems and machine learning.
What it does
Awesome AI Security is a navigable reading and discovery index. Its sections route readers from governance guidance into attack methods, evaluations, controls, practical labs, agent security skills, and specialized models.
Why it's ranked here
The collection earns attention through unusually broad topic organization and strong representation from OWASP, NIST, ISO, MITRE, CSA, and ENISA. It works best as a map of the field, not an assessment of each resource.
What's good
The taxonomy connects policy and engineering concerns. Readers can move from risk frameworks and terminology to red-team tools, guardrails, sandboxing, model scanning, privacy, and supply-chain security. Separate lab and CTF listings provide concrete learning routes.
Tradeoffs
Curation depth varies. Some entries explain scope and capabilities, while others are bare links. The visible text provides no consistent selection criteria, comparison method, compatibility matrix, or validation record. One lab entry also contains malformed list markup.
How to use it well
Security engineers, AI platform teams, risk owners, and learners can use it to seed research, training plans, threat modeling, or tool shortlists. Follow the primary links and evaluate candidates independently. It does not replace testing, implementation guidance, or product selection due diligence.
Technical notes+
README.md is the main artifact and implements the project as a Markdown link catalogue with a nested table of contents. Its hierarchy covers learning resources, governance and risk management, attack techniques, benchmarks, defensive controls, agentic security skills, and security-focused models. Entries mix plain links with italicized summaries, and the contribution route is a pull request or direct contact. LICENSE supplies the MIT License. No executable interface, package manifest, installation process, or runtime mechanism appears in the provided repository text.
Observed
- License
- MIT License
- Interface
- Markdown-based link directory
- Install surface
- No installation process is described in the provided repository text
- Contribution surface
- Contributions are requested through pull requests or direct contact
- Content structure
- Nested sections span learning, governance, attacks, evaluations, defenses, agent skills, and security-focused models
- Repository artifacts provided
- README.md and LICENSE
Read from README.md, LICENSE.
What it can do
Provide curated AI security frameworks
User search or browse request → List of AI security frameworks and standards
Catalog offensive AI security tools
Security professional's tool requirements → Collection of offensive AI security testing tools
Catalog defensive AI security tools
Security professional's protection requirements → Collection of defensive AI security tools
Aggregate AI security learning materials
Educational content search request → Curated educational resources and materials
List open source AI security tools
Open source tool requirements → Directory of open source security tools for AI systems
Provide AI governance frameworks
Governance framework inquiry → AI governance and compliance frameworks
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Editorially curated, with community endorsements as a secondary signal. Corrections welcome.