Topic starter
25/10/2026 11:07 am
TL;DR: Agentic AI systems break down when authorisation, execution and approval are left to the model, according to Adversa AI. External enforcement has to bind the exact action, because approvals that can be forged or swapped do not hold at runtime.
Editorial analysis by NHI Mgmt Group, based on content published by Adversa AI: “Top AI agent security resources â October 2026”.
By the numbers:
- The article groups 49 resources into incident, defense, attack technique, vulnerability, red teaming, framework and training categories.
Key questions
Q: Why do agent sandboxes fail when egress is only checked from inside the system?
A: Because the agent may still have writable or relay paths that the local sandbox view does not expose.
Q: What do teams get wrong about auto-approval for AI agents?
A: They treat auto-approval as a productivity setting rather than a standing authority grant.
Q: What breaks when an AI agent’s own output is treated as permission to act?
A: The control boundary collapses.
Practitioner guidance
- Define external authorisation for every tool call Require a policy decision outside the model before any action, including read-only operations that can still trigger side effects.
- Inventory every agent egress path Map writable GET endpoints, DNS routes, shared channels, browser plug-ins and public services that can move data or instructions out of scope.
- Bind approvals to the exact execution payload Store the reviewed action, the dispatched action and the approval artefact in a way that prevents swapping after review.
Bottom line: Agentic AI systems fail when authorisation, execution and approval are treated as if the model itself can be trusted to coordinate them.
Glossary terms: Agentic AI Identity, Runtime Enforcement, Endpoint Binding, Egress Boundary
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This topic was modified 7 days ago by NHI Mgmt Group
27/10/2026 7:15 pm
Authorization outside the model is now the defining control boundary: This month’s research repeatedly shows that the model should never be the source of permission. When policies live inside generated text, prompt injection becomes an authorisation bypass, not merely a content-security problem. The implication is structural: identity governance for agents must move from interpretation to deterministic enforcement.
A few things that frame the scale:
- Gartner predicts that more than 50% of successful cyberattacks against AI agents through 2029 will exploit access control weaknesses.
A question worth separating out:
Q: How should organisations govern AI agents alongside human identity and device access?
A: Organisations should treat AI agents as a separate identity class with their own entitlement boundaries, logging expectations, and approval model. Human IAM controls often assume interactive sign-in and review cycles, which do not fit autonomous or programmatic access. The safer approach is to define actor-specific policy and verify which access paths can be delegated without expanding trust unnecessarily.
👉 Read our full editorial: Authorization outside the model is the main agentic AI control gap
This post was modified 7 days ago by NHI Mgmt Group