They create risk because the system can change its behaviour after execution has already started. That means security teams are no longer governing only the initial prompt or configuration, but also the live request path, the user’s response, and the downstream effect of that response. Without approval, attribution, and validation, the request channel becomes a trust boundary with weak visibility.
Why This Matters for Security Teams
Runtime context requests are dangerous because they turn a supposedly fixed AI workflow into a live decision surface. Once a system can ask for more data, permissions, or tool output after execution starts, governance shifts from pre-deployment review to continuous control of the request path. That creates a new trust boundary where approval, attribution, and validation all have to happen in motion, not just at design time.
This is especially relevant for agentic systems that chain tools, call APIs, and adapt behaviour based on intermediate results. Traditional controls such as static prompt review and one-time access approval do not capture what happens when the model requests additional context mid-task. NHI Management Group has repeatedly documented how weak lifecycle controls and visibility gaps amplify identity risk in autonomous systems, including in the Top 10 NHI Issues and the Ultimate Guide to NHIs — Key Challenges and Risks. For broader governance framing, the NIST Cybersecurity Framework 2.0 remains useful, but it must be applied with runtime-aware controls for AI workloads.
In practice, many security teams only discover the risk after a model has already requested excessive context or triggered an unintended downstream action.
How It Works in Practice
Runtime context requests usually appear harmless at first: the system asks for a document, a database lookup, a user confirmation, or a fresh token to continue. The governance problem is that each request can change the execution path. A benign question can become an access escalation if the response unlocks new tools, wider data access, or a higher-trust decision branch. In agentic environments, the issue is not just what the model was initially allowed to do, but what it can negotiate into doing once it is running.
Current guidance suggests treating these requests as policy decisions rather than simple data lookups. That means evaluating each request in real time, with full context: task intent, user identity, workload identity, data sensitivity, and the expected downstream effect. In mature implementations, teams combine policy-as-code, short-lived credentials, and explicit approval gates. Workload identity is the anchor for attribution, while runtime authorisation checks determine whether the requested context is appropriate at that moment. Where possible, ephemeral access should be issued per task and revoked immediately on completion.
Practical controls often include:
- Runtime policy evaluation instead of pre-approved blanket access
- Just-in-time credentials with narrow scope and short TTLs
- Separate approval for sensitive context, such as customer data or production secrets
- Logging that records both the request and the downstream action it enabled
- Validation checks on tool outputs before the agent can continue
NHI Management Group research on breach exposure shows why this matters operationally, especially where secrets and identities are already fragile. The 2024 ESG Report: Managing Non-Human Identities and the LLMjacking research both show how fast compromised identities can become an attack path. For implementation detail, the NIST Cybersecurity Framework 2.0 supports a control model that can be adapted to runtime checks rather than static approvals. These controls tend to break down when agents are allowed to self-sequence across multiple tools because the request chain becomes too dynamic for a single pre-authorisation step to hold.
Common Variations and Edge Cases
Tighter runtime control often increases latency and operational overhead, so organisations have to balance response speed against the risk of over-delegation. That tradeoff becomes visible in production systems where users expect low-friction answers, but the agent needs extra context to complete safely. Best practice is evolving here, and there is no universal standard for every deployment model yet.
The hardest edge cases are multi-agent workflows, delegated approvals, and shared service accounts. In those environments, one agent’s runtime request can indirectly affect another agent’s privileges or data scope. The issue is even sharper when long-lived secrets are used, because a single approved request can expose a broader trust surface than intended. For that reason, current guidance increasingly favours ephemeral access, explicit attribution, and request-by-request validation over standing permissions.
This is also where audit and regulatory expectations start to diverge from engineering convenience. The Ultimate Guide to NHIs — Regulatory and Audit Perspectives is useful for framing evidence collection, while OWASP NHI Top 10 highlights how runtime trust expansion maps to emerging agentic risk. Teams that rely on static role design usually struggle when the agent’s next action cannot be predicted from its initial prompt or user session.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | Runtime context requests expand agent attack surface and trust boundaries. | |
| CSA MAESTRO | MAESTRO addresses agentic control points, approvals, and tool-use governance. | |
| NIST AI RMF | AI RMF governs risk management for changing AI behaviour during operation. |
Apply AI RMF to define owners, monitor runtime decisions, and document residual risk for context requests.
