Agentic AI Top10 Vulnerability
Open threat taxonomy for the top 10 risks in autonomous AI systems.
Essential reading for any organization deploying agentic AI. Its open, expert-backed taxonomy fills a critical void, but it's a reference document—not a scanner or tool. Pair it with active security tooling for full coverage.
- Security researchers focused on AI/ML threat modeling
- AI developers building autonomous agents needing a security baseline
- Compliance officers assessing agentic AI risk
- Enterprise architects designing secure AI systems
- Teams seeking an automated vulnerability scanner
- Organizations wanting a turnkey compliance solution
- Users without foundational AI security knowledge
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In short
Agentic AI Top10 Vulnerability — Open threat taxonomy for the top 10 risks in autonomous AI systems. Best for Security researchers focused on AI/ML threat modeling, AI developers building autonomous agents needing a security baseline, Compliance officers assessing agentic AI risk. Free to use.
Viability Score
How likely is Agentic AI Top10 Vulnerability to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Top 10 threat taxonomy for agentic AI security
- Detailed risk descriptions with real-world scenarios
- Mitigation strategies for each risk
- Vendor-neutral and community-driven framework
- Free and open access to full document
- GitHub repository for contributions and issue tracking
- Canonical reference for OWASP and CSA red teaming
- Independent expert consensus (no vendor funding)
About Agentic AI Top10 Vulnerability
The Agentic AI Top10 Vulnerability initiative is the first open, vendor-neutral framework to systematically identify and mitigate the most critical security risks in agentic AI systems. Spearheaded by Vishwas Manral and contributed to by over 50 experts from 20 organizations—including Cisco, GSK, Palo Alto Networks, Google, and Meta—it provides a structured taxonomy of threats such as prompt injection, tool manipulation, data leakage, and autonomous decision-making failures, each paired with detailed mitigation strategies. The framework is entirely independent: no vendor funding was accepted, ensuring objectivity. It is freely downloadable as a PDF from the project's website and GitHub repository, and the community is encouraged to contribute. The work serves as a canonical reference for OWASP and CSA red teaming efforts, offering a shared language for assessing and defending against emerging autonomous AI vulnerabilities. Designed for security researchers, AI developers, compliance officers, and enterprise architects, this living document will evolve as the threat landscape shifts. It is not a product or service but an open standard that anyone can adopt and adapt, with future updates driven by empirical findings and community input. Compared to proprietary security checklists, this framework's collaborative model ensures broad applicability across industries and regulatory environments, making it the definitive baseline for agentic AI threat modeling in 2025 and beyond.
Behind the Verdict
If you're building or deploying autonomous AI agents in 2025, this framework should be your starting point for threat modeling. It's the first industry-wide effort to name and categorize the top vulnerabilities, and the expert consensus gives it weight—20 organizations and 50+ contributors, including major names like Cisco, Google, and Palo Alto Networks. The fact that it's entirely volunteer-driven and vendor-independent is a genuine strength; no one is selling a solution, so the analysis stays objective. We'd reach for this when we need a structured, shareable taxonomy to align a security team or to justify investments in specific controls—like input validation against prompt injection or sandboxing for tool misuse. That said, this is not a tool. There's no automated scanning, no dashboard, no code to integrate. It's a PDF and a GitHub repo. Where it bites is if you're looking for a turnkey vulnerability scanner or compliance checklist—you'll need to operationalize the recommendations yourself. The closest alternative is OWASP's existing LLM Top 10, but this framework goes deeper into agentic-specific behaviors like autonomous decision-making and tool orchestration failures. In practice, treat this as the analysis layer; combine it with runtime monitoring and adversarial testing to close the loop. The living-document promise is key—future updates should keep it relevant as attacks evolve.
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Use Cases
- Reference the top 10 risks when designing an agentic AI system to avoid known pitfalls.
- Use the mitigation strategies to harden your autonomous AI agent against prompt injection.
- Incorporate the framework into your red teaming playbook for consistent evaluation.
- Align your compliance documentation with industry-recognized best practices.
- Contribute new threat scenarios to the GitHub repository to help the community.
Limitations
- This is an educational and reference framework, not a software tool.
- It does not provide automated scanning, monitoring, or enforcement.
- Users must manually apply the concepts to their own systems.
- The project relies on community contributions for updates and may not cover every emerging threat in real time.
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