Agentic AI Top10 Vulnerability
Vendor-neutral Top 10 taxonomy of security risks in autonomous AI agents, with mitigations, free and open.
Adopt it as your baseline taxonomy, not as your security program. The framework's value is that it is vendor-neutral — nobody paid for placement — and that it enumerates agentic-specific failure modes (prompt injection, tool manipulation, data leakage, autonomous decision-making failures) that generic AI governance checklists tend to skip. It costs nothing and requires no procurement cycle. But it will not scan an agent, monitor runtime behavior, or generate audit evidence; for that you need a tool such as Lakera, Robust Intelligence, or a GRC platform like Vanta. Download it, map your agent's tool permissions against each risk, then buy tooling.
Verified 7h ago · liveness 53/100 · cite: rightaichoice.com/tools/agentic-ai-top10-vulnerability
- Security researchers doing AI/ML threat modeling
- AI developers building agents who need a security baseline
- Compliance officers assessing agentic AI risk
- Enterprise architects designing autonomous AI systems
- Teams looking for an automated vulnerability scanner
- Organizations wanting a turnkey compliance product
- Buyers without foundational AI security knowledge
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Skip Agentic AI Top10 Vulnerability if you need automated scanning, runtime monitoring, or audit evidence — it is a free threat taxonomy you apply manually, not software that inspects your agents.
The document is free, but turning the ten risks into controls costs internal engineering time — budget the security-engineer hours before you commit the framework to your roadmap.
At $0 it undercuts every commercial agentic AI security product, which is the point: it is the free baseline you pair with anything else. Solo researchers and startups get full value for nothing. Funded security teams should treat the zero price as the reason to spend elsewhere — a runtime protection tool such as Lakera or a GRC platform like Vanta, which cost real money but produce the monitoring and evidence this framework cannot.
In short
Agentic AI Top10 Vulnerability — Vendor-neutral Top 10 taxonomy of security risks in autonomous AI agents, with mitigations, free and open. Best for Security researchers doing AI/ML threat modeling, AI developers building agents who need a security baseline, Compliance officers assessing agentic AI risk. Free to use.
What's new in Agentic AI Top10 Vulnerability
Checked todayAcross the latest 1 update: 1 launch.
Viability Score
How well maintained and how widely used is Agentic AI Top10 Vulnerability? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Top 10 threat taxonomy for agentic AI security
- Detailed risk descriptions with real-world scenarios
- Mitigation strategies paired with each risk
- Vendor-neutral framework with no vendor funding
- Free PDF download of the full document
- Public GitHub repository for contributions and issue tracking
- Community contribution via email and GitHub pull requests
- Covers prompt injection, tool manipulation, and data leakage
- Addresses autonomous decision-making failures
- Contributions from 50+ individuals across 20 organizations
- Named organizational contributors include Cisco, Google, Meta, SAP, Amazon, Palo Alto Networks
- Reference for OWASP and CSA red teaming efforts
- Structured for security researchers, AI developers, and compliance officers
- Positioned as a living document with future community-driven updates
- Independent initiative with no marketing or non-technical funding
About Agentic AI Top10 Vulnerability
Agentic AI Top10 Vulnerability is an open, vendor-neutral framework that names and explains the ten most significant security risks facing agentic AI systems — the kind of software that makes autonomous decisions and takes actions through tools. Initiated by Vishwas Manral with significant contributions from Ken Huang, Akram Sherif, and Rakshith Aralimatti, it was developed with over 50 individuals from 20 organizations, including Cisco Systems, GSK, Palo Alto Networks, Lakera, EY, Google, Meta, SAP, Amazon, Humana, TIAA, and GlobalPayments. Each risk is paired with concrete mitigation strategies, so a security engineer can move from taxonomy to design decisions. No money was accepted from any vendor and no funding was given for marketing, which keeps the content independent of any product roadmap. The full document is downloadable as a PDF at no cost, and contributions are handled through a public GitHub repository and email. It is used as a canonical reference for OWASP and CSA red teaming efforts. It is a reference document, not software: there is no scanner, no dashboard, and no enforcement engine. Buyers evaluating it should treat it as the shared vocabulary layer for agentic AI threat modeling, sitting alongside a GRC platform such as Vanta or Drata and active testing tooling rather than replacing either.
Behind the Verdict
The most useful thing about this framework is what it refuses to be. It is not a product, it carries no vendor logo in a paid placement, and the project explicitly states that no money was taken from any vendor nor was any funding given for marketing. That matters in a category where most 'agentic AI security' content is published by companies selling agentic AI security products. The contributor list — Cisco Systems, GSK, Palo Alto Networks, Lakera, EY, Google, Meta, SAP, Amazon, Humana, TIAA, GlobalPayments, Jacobs, HealthEquity, Sisense, DigitalTurbine, Precize — gives the taxonomy a breadth that a single vendor's threat model rarely has. The second strength is scope discipline. Traditional AI security frameworks tend to treat the model as the unit of analysis. Agentic systems break that assumption: an agent holds memory, calls tools, and acts autonomously, so the failure modes multiply. The framework's coverage of prompt injection, tool manipulation, data leakage, and autonomous decision-making failures reflects that shift, and each risk ships with mitigation strategies rather than stopping at description. For a red teamer, that pairing is the difference between a checklist and a work plan. The weaknesses are structural, not editorial. It is a PDF and a GitHub repository, so the update cadence depends on volunteer contributors; the page itself concedes it is 'just the beginning' and that future updates will refine and expand on the insights. There is no scoring engine, no mapping tool, no API, and no runtime monitoring. Applying it means manually reading ten risks and reasoning about your own architecture. Teams without a security engineer will find it hard to translate 'tool manipulation' into a change to their agent's permission model. And because it is voluntary, it carries no compliance weight on its own — auditors will ask for a control framework (AICM, NIST AI RMF) and evidence, not a threat list. Where it fits: as the shared vocabulary at the start of an agentic AI project, and as the reading list for a red team scoping exercise. Where it does not: replacing an automated scanner, generating evidence for an audit, or satisfying a regulator. Use it to ask better questions, then buy the tools that answer them.
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Real-world workflow fit
Concrete scenarios for the personas Agentic AI Top10 Vulnerability actually fits — and what changes day-one when you adopt it.
Downloads the PDF and walks the top 10 risks one by one against a diagram of the agent's tool permissions and memory store.
Outcome: Produces a gap list of unmitigated risks — most often tool manipulation and data leakage — that becomes the sprint backlog for the agent's security hardening.
Uses the taxonomy to structure the test plan so every finding maps to a named risk rather than a bespoke description.
Outcome: Findings are reported in language the client's compliance team already recognizes, shortening the remediation conversation.
Cites the framework's 20-organization contributor list when justifying the threat model to internal audit and external regulators.
Outcome: Internal policy references an independent industry baseline rather than a single vendor's whitepaper, which is easier to defend.
Use Cases
- Map your agent's tool permissions against the top 10 risks before shipping an autonomous workflow.
- Use the mitigation strategies as acceptance criteria when hardening an agent against prompt injection.
- Build a red team scoping document from the taxonomy so findings use consistent language.
- Align internal AI security policy with an industry-recognized, vendor-neutral reference.
- Contribute new threat scenarios or field findings to the GitHub repository.
- Brief non-security stakeholders on agentic AI risk using a shared vocabulary.
Limitations
- This is a reference document, not software.
- There is no scanner, monitor, dashboard, or enforcement engine — you read it and apply the risks to your own architecture by hand.
- There is no scoring mechanism or API; the guide collection does link a separate OWASP AIVSS v0.5 scoring guide, but this framework itself does not score your system.
- Updates depend on volunteer contributors through GitHub and email, and the document itself describes future updates as refinements to come, so a newly discovered agentic threat may not appear immediately.
- Because adoption is entirely voluntary, it carries no compliance or audit weight on its own — auditors will ask for a control framework and evidence, not a threat list.
as of 2026-09-15
Verification history
We have re-verified Agentic AI Top10 Vulnerability 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Agentic AI Top10 Vulnerability tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Framework
$0
Ideal for
Security researchers, AI developers, compliance officers, and enterprise architects who need a free, vendor-neutral baseline for agentic AI threat modeling.
What this tier adds
Free entry point — the full PDF, the top 10 taxonomy, the mitigation strategies, and GitHub contribution access at no cost.
Where the pricing makes sense
The company stage and team size where Agentic AI Top10 Vulnerability's pricing actually pencils out — and where peers do it cheaper.
At $0 it undercuts every commercial agentic AI security product, which is the point: it is the free baseline you pair with anything else. Solo researchers and startups get full value for nothing. Funded security teams should treat the zero price as the reason to spend elsewhere — a runtime protection tool such as Lakera or a GRC platform like Vanta, which cost real money but produce the monitoring and evidence this framework cannot.
Setup time & first value
How long it actually takes to get something useful out of Agentic AI Top10 Vulnerability — broken out by persona, not the marketing-page minute.
Security researchers and red teamers: about 30 minutes to download and read the PDF, then a few hours to map each risk to a real system. AI developers and architects: roughly half a day to work the ten risks against an existing agent design. Compliance officers without an AI security background: a day or more, since the mitigation strategies assume familiarity with prompt injection and
Switching to or from Agentic AI Top10 Vulnerability
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a vendor whitepaper threat model: replace the single-vendor threat list with this 20-organization, vendor-neutral taxonomy and re-map existing controls to it.
- →From a generic AI governance checklist: keep the controls, add the agentic-specific risks (tool manipulation, autonomous decision-making failure) this framework covers.
- ↗To a runtime protection tool like Lakera: keep the taxonomy as your vocabulary and let the tool enforce and monitor the risks you identified.
- ↗To a GRC platform like Vanta: map the mitigated risks to controls so the framework's output becomes auditable evidence.
Resources & Guides
- Resourceaicloudgovernance.com
Top 10 Agentic Ai Security Risks Key Threats And Mitigation Strategies · Agentic AI Top10 Vulnerability
Helpful link from aicloudgovernance.com
- Resourceaicloudgovernance.com
Ai Guides Best Practices · Agentic AI Top10 Vulnerability
Helpful link from aicloudgovernance.com
- Resourcegithub.com
OWASP Agentic AI · Agentic AI Top10 Vulnerability
Helpful link from github.com
Tutorials & Learning
YouTube returned 6 videos for “Agentic AI Top10 Vulnerability”, and we withheld 6: 6 did not mention Agentic AI Top10 Vulnerability. We are showing none, because we could not prove any of them are about Agentic AI Top10 Vulnerability.
Official links
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ECC
Open-source security and optimization for AI coding agents across Claude Code, Codex, Cursor, and OpenCode.
Imbue
Imbue is an open AI lab building coding agent tools that run in parallel and answer to you, not a vendor.
Mindgard
Automated AI red teaming platform that continuously discovers, assesses, and defends AI systems and agents.
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Agentic Ai Top10 Vulnerability vs Push Security
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