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02/08/2026 12:11 pm
TL;DR: OWASP's first Top 10 for Agentic Applications identifies ten failure modes including goal hijacking, tool misuse, identity and privilege abuse, supply chain compromise, and cascading failures, reflecting a shift from prompt security to systems that plan and act with tools and delegated access. The category now demands governance that treats agents as controllable runtime identities, not just model outputs.
NHIMG editorial — based on content published by Straikerai: OWASP just released its first Top 10 for Agentic AI and here's what you need to know
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
Questions worth separating out
Q: What breaks when AI agents inherit access from users and service accounts?
A: The main failure is that inherited access can be broader than the agent’s actual task, so privilege becomes easier to reuse than to govern.
Q: When should organisations prioritise agent identity controls over model tuning?
A: Always when the agent can take actions, access data, or call tools that create real business impact.
Q: What do security teams get wrong about multi-agent systems?
A: They treat the system as a set of independent components instead of a trust network.
Practitioner guidance
- Define agent identities as governed access subjects Assign each agent a unique, scoped identity with explicit ownership, approval boundaries, and logging so actions can be traced back to a business function rather than a shared service account.
- Restrict tool access to task-specific policy gates Put policy checks in front of every high-risk tool call, especially database queries, file writes, external requests, and workflow triggers.
- Break shared context to limit cascading failure Separate memory, prompts, and message channels between agents so one poisoned input cannot silently shape the full workflow.
What's in the full article
Straikerai's full blog covers the operational detail this post intentionally leaves for the source:
- The article breaks down each of the ten OWASP agentic risks in plain English, including goal hijack, tool misuse, identity abuse, and rogue behaviour.
- It provides the kitchen analogy and visual structure the author used to explain how the risks chain together across planning, execution, and human review.
- The source links to real-world examples and related research material that can help teams map the risks into their own AI control reviews.
- It includes Straikerai's positioning on runtime guardrails and red-team testing for agentic applications, which is relevant once you move from strategy to implementation.
👉 Read Straikerai's analysis of the OWASP Top 10 for Agentic AI risks →
OWASP agentic AI top 10: what identity teams need to watch?
Explore further
02/08/2026 1:15 pm
Agentic AI identity is now a governance boundary, not a model feature. Once a system can select tools and timing, it behaves like a software identity with operational authority. That means IAM and PAM teams have to treat the agent as an access-bearing entity with lifecycle, policy, and audit requirements. The discipline is no longer just model safety, it is identity governance for action-capable software.
A question worth separating out:
Q: What frameworks should organisations use to assess agentic AI risk?
A: Use OWASP Agentic AI Top 10 for threat modelling, OWASP NHI guidance for credential and privilege governance, and Zero Trust principles for continuous verification of tools and identities. Together, they help teams evaluate agent behaviour, access boundaries, and trust at runtime instead of only at onboarding.
👉 Read our full editorial: OWASP agentic AI top 10 shows why identity controls are failing
