What should organisations do when a dependency can control bot actions at runtime?

Treat the dependency as part of the trusted execution path and limit what the bot can do if the library misbehaves. Restrict outbound network access, constrain the bot’s action scope, and require rapid revocation paths for the session. If runtime code can drive privileged actions, the identity itself needs containment.

Why This Matters for Security Teams

When a dependency can control bot actions at runtime, the dependency is no longer just a supply chain concern. It becomes part of the trusted execution path and can influence decisions, tool use, and privilege boundaries in real time. That changes the risk from ordinary software failure to delegated authority abuse, especially where an agent can reach APIs, data stores, ticketing systems, or payment flows. Guidance from NIST SP 800-53 Rev 5 Security and Privacy Controls is useful here because the issue is not only code integrity, but also control over execution, access, and revocation.

Security teams often underestimate how quickly a runtime dependency can turn into an identity problem. If the dependency can shape prompts, select tools, alter parameters, or trigger privileged workflows, then the bot is effectively operating under a delegated trust model. That means standard application allow-listing is not enough. The organisation needs to ask who can influence the bot, what the bot can do, and how fast that authority can be withdrawn when behaviour changes. In practice, many security teams encounter runtime dependency abuse only after an automated action has already been executed, rather than through intentional control testing.

How It Works in Practice

The practical response is to treat runtime dependencies as constrained participants in the bot’s execution environment. The goal is not to assume the dependency is malicious, but to prevent a single library, plugin, or orchestration component from having unrestricted influence over the agent.

Start by reducing blast radius. Restrict outbound network access so the dependency cannot exfiltrate data or reach unapproved services. Then constrain the bot’s action scope so the dependency can only affect narrowly defined operations. This should include explicit tool permissions, scoped tokens, time-bound sessions, and step-up approval for high-impact actions. For agentic systems, OWASP Agentic AI guidance is particularly relevant because runtime control channels are often where prompt injection, tool abuse, and excessive autonomy converge.

• Define which tools the bot may call and under what conditions.
• Bind runtime permissions to short-lived identities and sessions.
• Log dependency-triggered actions separately from normal application events.
• Use policy checks before execution, not only after the fact.
• Maintain a fast kill switch to revoke tokens, sessions, and API access.

Operationally, this also means knowing whether the dependency can modify prompts, memory, retrieval context, or execution parameters. If it can, those inputs should be treated as security-sensitive control points, not ordinary application data. Where AI-specific risk is present, the NIST AI Risk Management Framework and the MITRE ATLAS knowledge base help teams reason about model abuse, adversarial manipulation, and control placement across the AI lifecycle. These controls tend to break down when a dependency is embedded inside a high-privilege automation pipeline because the bot’s execution path, secret access, and external connectivity are already tightly coupled.

Common Variations and Edge Cases

Tighter containment often increases latency, engineering overhead, and operational friction, requiring organisations to balance autonomy against control. That tradeoff becomes sharper when the bot is expected to complete tasks without human intervention, because every restriction can reduce throughput or user experience. There is no universal standard for this yet, but current guidance suggests treating runtime influence as a privilege boundary rather than a convenience feature.

Some environments need extra caution. In RAG-based systems, a dependency may alter retrieval sources or ranking logic without touching the model itself, which still changes runtime behaviour. In multi-agent workflows, one agent may indirectly control another through shared context or tool delegation. In cloud-native deployments, secrets managers and service identities can amplify the problem if the runtime dependency can read or request credentials.

The most common edge case is a dependency that looks non-sensitive during development but becomes privileged once connected to production tooling. That includes plugins, SDK wrappers, workflow engines, and orchestration middleware. For these cases, the right question is not whether the dependency is trusted in general, but whether it is trusted to participate in specific actions at specific times. If that answer is unclear, the control model is too weak. CISA Secure by Design principles reinforce that insecure defaults and broad authority should be removed early, before runtime trust becomes an incident response problem.

Related resources from NHI Mgmt Group

• When should organisations treat runtime telemetry as a primary control?
• What do organisations get wrong about AI agent runtime control?
• When should organisations move from periodic review to runtime access control?
• How can organisations prove they have control over agent actions?