Runtime data sources matter because they can steer model output without changing the model itself. A poisoned knowledge base, tool response, or memory store can persistently alter decisions across sessions, which means the control problem is not just model integrity but the integrity of every trusted input.
Why Runtime Data Sources Are Part of the Attack Surface
Model weights matter, but they are only one layer of trust. In production AI systems, retrieval stores, tool APIs, session memory, prompt caches, and orchestration messages can shape decisions at runtime without touching the model file at all. That is why a poisoned knowledge base or compromised tool response can become a durable security issue. NHI Management Group has seen the same pattern in adjacent identity failures, where exposed secrets and weak control of trusted inputs do the damage, not just the core system itself. The DeepSeek breach is a useful reminder that runtime exposure can scale faster than teams expect, and the CSA MAESTRO agentic AI threat modeling framework treats external inputs as first-class threats for that reason.
The practical mistake is assuming the model is the only thing that can be manipulated. In real deployments, output integrity depends on every trusted input path being protected, authenticated, and monitored.
How Runtime Inputs Steer Decisions in Practice
Security teams need to think of runtime sources as policy-relevant inputs, not just convenience features. A retrieval-augmented system can answer differently if the knowledge base is altered. An agent with tool access can be steered by a malicious API response. Memory stores can persist false context across sessions, and that persistence makes the problem harder than a one-off prompt injection. In agentic systems, the issue is amplified because the agent can act on the wrong information, call more tools, and chain decisions autonomously.
Current guidance suggests three controls matter most:
- Authenticate and integrity-check every runtime data source before it reaches the model or agent.
- Limit what the agent can retrieve or execute through tight workload identity, least privilege, and short-lived access.
- Monitor for anomalous tool outputs, retrieval drift, and memory poisoning, then revoke or quarantine suspicious sources fast.
This is where identity security and AI security converge. The Ultimate Guide to NHIs — Key Research and Survey Results shows how often organisations still lack confidence in non-human controls, and that gap becomes more dangerous when an AI agent is making runtime decisions. The Anthropic Project Glasswing work also underscores that tool-using systems need explicit boundaries around what can be trusted at execution time. These controls tend to break down when agents operate across fragmented SaaS stacks because the provenance of each retrieved object or tool response is difficult to verify end to end.
Where the Standard Answer Breaks Down
Tighter runtime control often increases latency, integration effort, and operational overhead, so organisations have to balance stronger provenance against delivery speed. That tradeoff becomes especially visible in high-volume environments where every request fans out to multiple tools, caches, and third-party APIs.
There is no universal standard for this yet, but best practice is evolving toward context-aware authorisation, JIT secrets, and workload identity for the systems that fetch or transform runtime data. For agentic workloads, static RBAC is often too blunt because the agent’s behaviour is goal-driven and dynamic. A better pattern is to issue short-lived credentials per task, bind them to workload identity, and evaluate access at request time using policy-as-code. That approach aligns with CSA MAESTRO agentic AI threat modeling framework, ASP.NET machine keys RCE attack lessons on secret abuse, and the governance direction in NIST AI Risk Management Framework. The real edge case is legacy environments with shared service accounts and long-lived tokens, because those systems make it impossible to prove which runtime source actually influenced the model.
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 inputs and tool abuse are core agentic AI attack paths. | |
| CSA MAESTRO | MAESTRO models tool, memory, and orchestration risks in agentic systems. | |
| NIST AI RMF | AI RMF supports governance of runtime-driven model behaviour and trust. |
Assign owners for runtime inputs, then test and monitor for integrity drift and unsafe downstream decisions.
