OWASP Just Published a Top 10 for AI Agents. Your Resume Doesn't Mention It Yet.
October 13, 2026
Jillian, CMO— AI-assisted and reviewed prior to publication.
OWASP published its first Top 10 for Agentic Applications in December 2025, a separate document from the long-standing web application and LLM lists, built specifically around AI agents that plan, remember, and act on their own. If you work in security and can't name at least three of its ten risk categories, that gap is now visible in interviews, not just in your resume.
What is the OWASP Top 10 for Agentic Applications, and why does it exist separately from the LLM list?
It exists because agents behave differently than chatbots. The OWASP GenAI Security Project's announcement describes the release as the culmination of input from more than 100 industry leaders, built to address risks specific to systems that act with delegated authority rather than just generate text. The older OWASP Top 10 for LLM Applications covers prompt injection and data leakage in a single request-response exchange. The new one, identified with the prefix ASI01 through ASI10, covers what happens when an agent holds memory across steps, calls tools, and coordinates with other agents without a human reviewing each action.
That distinction matters for anyone preparing for a technical interview right now. A hiring manager asking about prompt injection is testing baseline awareness. A hiring manager asking how you would contain an agent that has been handed broad tool access and a persistent memory store is testing something the OWASP LLM list never covered. One analysis of the new framework put it plainly: it moves the conversation from language model vulnerabilities to risks that emerge when AI systems can plan, decide, and act across multiple steps and systems.
Which of the ten risks are employers actually likely to ask about?
The ones tied to a documented incident, because those are the ones interviewers can anchor a scenario question to. Every entry in the agentic list maps to something that already happened in production, not a projected risk, which is why the framework reads less like theory and more like an incident log with mitigations attached.
| Risk | What it covers | Real incident cited |
|---|---|---|
| ASI01: Agent Goal Hijack | Untrusted content redirects an agent's objective | EchoLeak zero-click data exfiltration from a Microsoft 365 Copilot workflow |
| ASI02: Tool Misuse & Exploitation | An agent is manipulated into misusing a legitimate tool | A poisoned pull request weaponized Amazon's AI coding assistant |
| ASI04: Agentic Supply Chain Vulnerabilities | Malicious or impersonated components enter an agent's toolchain | A fake Model Context Protocol email server secretly BCC'd messages to an attacker |
| ASI10: Rogue Agents | An agent deviates from intended behavior with no attacker involved | An agent deleted a production database during an active code freeze |
A security researcher who can walk through why the Replit database deletion falls under rogue agent behavior rather than tool misuse is demonstrating something a generic "I understand AI risk" answer cannot. The incidents behind these categories are detailed in a breakdown of the real-world attacks mapped to each risk, including the confirmed scale of the Amazon Q compromise and the mechanics of the EchoLeak exploit chain.
Why is this showing up as a hiring filter instead of staying a reference document
Because certification bodies moved fast to build exam content around it, and recruiters follow exam content. CompTIA's new SecAI+ credential devotes 40 percent of its exam, by far the largest single domain, to securing AI systems, and that domain explicitly draws on the OWASP Top 10, the MIT AI Risk Repository, MITRE ATLAS, and the CVE AI Working Group as source material. SecAI+ was built as an expansion certification layered on top of Security+, CySA+, or PenTest+, and CompTIA's own framing ties it directly to the shift in what cybersecurity job roles now require as AI expands into core business processes.
If you are weighing where to spend study time, the practical read is this: a framework that shows up on a vendor-neutral certification exam is a framework an interviewer has a ready-made question bank for. Reviewing the SecAI+ curriculum overview before you commit to a study plan will tell you exactly how much weight agent architecture, sandboxing, and identity controls for AI agents carry relative to governance and AI-assisted operations, so you are not guessing at where to focus.
How management-track professionals should think about this differently
If your role is closer to governance than to hands-on red teaming, the agentic Top 10 still applies, just through a different certification path. ISACA built its Advanced in AI Security Management credential specifically for people who already hold CISM or CISSP and now need to manage AI-specific risk at a program level rather than a technical one. The AAISM exam content outline splits its 90 questions across AI governance and program management, AI risk management, and AI technologies and controls, with the heaviest weighting on the technologies and controls domain.
The prerequisite is strict. ISACA requires an active CISM or CISSP credential to sit for the exam, no exceptions, which keeps the credential aimed at people who already carry security management authority and need the AI-specific layer added on top. For a CISSP holder managing a security program that now includes agentic tools in production, being able to reference ASI03 (identity and privilege abuse) by name in a board conversation about AI governance carries more weight than a general statement about "AI risk."
What this means for how you present your skills
Naming the framework is not optional anymore if you want your experience to read as current. A security engineer who writes "familiar with AI security risks" on a resume is saying less than one who writes "assessed agent tool permissions against OWASP ASI02 and ASI03." The second version tells a reader exactly what you looked at and why, and it signals you have touched the document recently enough to use its actual terminology.
This also changes how you should prepare for technical screens. Instead of rehearsing a general definition of prompt injection, walk through a scenario: an agent with file system access receives a poisoned document and starts acting on embedded instructions. Explain which ASI category that falls under, what a least-agency control would have prevented, and how you would detect it after the fact. That kind of answer demonstrates you have worked with the framework rather than skimmed a summary of it.
The research backing this shift keeps landing in the same place. Job-posting data shows AI agents are the fastest-growing new skill category on cybersecurity engineer postings, ahead of generative AI and large language models as standalone terms. That ordering is not an accident. Employers have moved past wanting people who understand that AI exists and are now hiring for people who can secure the specific way agents operate inside their environment.
If you are rebuilding a study plan around this, start with whichever side of the work matches your role, technical implementation through a credential like CompTIA SecAI+ or program-level governance through AAISM, and build your preparation around the named framework rather than general AI literacy. You can start training with a plan that maps directly to the categories interviewers are now asking about, rather than catching up after the fact.
None of this means the fundamentals stop mattering. Access control, least privilege, and identity scoping are still the controls doing the actual work here, the agentic Top 10 just renames the failure modes in terms specific to autonomous systems. What changes is the vocabulary you need to prove you have kept up, and right now that vocabulary has a name, a publication date, and ten categories you should be able to recite without looking them up.