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Runtime signalling

The set of environmental cues an autonomous system reads while selecting actions, tools, and targets. For agentic AI, those cues become part of the attack surface because they can influence the system’s next move as strongly as a permission rule can.

What runtime signalling means in agentic systems

Runtime signalling is the live stream of environmental cues an autonomous system interprets while choosing actions, tools, and targets. Those cues can include system state, prompt context, policy outputs, tool responses, telemetry, and other signals that shape what the system does next.

What makes runtime signalling important is that the signal path is not just observation, it is decision input. In agentic systems, a manipulated cue can redirect behaviour without changing the underlying model weights or formal policy, which is why runtime signalling is part of the security boundary around autonomous action.

How runtime signalling influences tool use and targeting

Runtime signalling becomes operationally significant when an agent uses it to decide whether to invoke a tool, continue a workflow, or switch targets. A small change in the observed environment can change the next action as decisively as a control rule, especially when the system is designed to react dynamically rather than follow a fixed script.

That dynamic quality is useful for adaptability, but it also means the agent is only as reliable as the cues it trusts. If the system treats low-confidence or unauthenticated signals as authoritative, it may escalate actions, leak context, or reach into systems it should not touch.

  • Signals can be direct, such as tool output or policy feedback, or indirect, such as surrounding context that the agent infers from.
  • Signals may be trustworthy in intent but still unsafe if they are stale, spoofed, incomplete, or out of sequence.
  • The more autonomy a system has, the more carefully its signal sources need to be bounded and interpreted.

Why runtime signalling is an attack surface

Runtime signalling is attractive to attackers because it offers a way to influence behaviour without breaking through a traditional permission gate. In NIST AI Risk Management Framework terms, this sits in the class of trust and governance problems where system outputs, context, and decision logic must be treated as risk-bearing inputs.

The security concern is not only that a signal may be malicious, but that it may be persuasive enough to alter agent choice. If an attacker can shape prompts, runtime context, tool responses, or environmental cues, they can steer the system toward unsafe actions, unexpected disclosure, or misuse of connected services.

Failure mechanism: The agent over-weights runtime cues that were never intended to be trusted as control inputs, so a manipulated or misleading signal changes the chosen action path.

Impact: The result can be tool misuse, target drift, unsafe execution, or a broader compromise of the autonomy boundary, especially when the agent can act on behalf of a user or system.

How practitioners should govern runtime signalling

The practical question is not whether an agent uses runtime cues, but which cues are allowed to influence which decisions. Strong designs separate observation from authority, validate high-impact signals before action, and constrain the scope of what a live cue can change.

For autonomy-heavy systems, runtime signalling should be treated as a governed input layer rather than a convenience feature. That means the system design should make clear which signals are advisory, which are decision-critical, and which are too sensitive to be consumed automatically.

Practitioner Guidance: Use runtime signalling boundaries to decide what the agent may observe, trust, and act on, and require stronger validation for cues that can redirect tools, targets, or privileged workflows.

Runtime signalling is closely related to agentic security, runtime trust, and control validation. If the system’s behaviour depends on live cues, then security review should focus on whether those cues can be spoofed, poisoned, replayed, or made ambiguous at the moment of decision.

For deeper treatment of the surrounding control environment, OWASP Agentic AI Top 10 captures the broader classes of agent goal hijacking, tool misuse, and identity and privilege abuse, while NIST AI Risk Management Framework provides the governance lens for managing those risks over time.

Where runtime signalling intersects with adversarial behaviour, MITRE ATLAS adversarial AI threat matrix is useful for mapping signal manipulation to known attack patterns such as context poisoning, tool misuse, and agent hijacking. If the signals are being carried through APIs or service integrations, the API boundary itself may also deserve review through OWASP API Security Top 10.

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, MITRE ATLAS and OWASP API Security Top 10 address the attack and risk surface, while NIST AI RMF sets the governance and control requirements practitioners need to meet.

Framework Control / Reference Relevance
NIST AI RMF Govern AI RMF addresses governance of runtime inputs that influence autonomous decisions.
Recommendation — Define which runtime signals may influence agent decisions and require validation for high-impact cues.
OWASP Agentic AI Top 10 ASI03 — Identity & Privilege Abuse Runtime signals can steer agent authority and privileged action paths.
Recommendation — Constrain runtime cues that can alter tool use or privileged action decisions.
MITRE ATLAS Adversarial AI threat matrix ATLAS covers context poisoning and agent hijacking techniques that manipulate runtime signals.
Recommendation — Map signal manipulation scenarios to ATLAS techniques and monitor for poisoning or hijacking patterns.
OWASP API Security Top 10 API5 — Broken Function Level Authorization API-driven runtime signals can indirectly change which functions an agent reaches and invokes.
Recommendation — Authorize agent-triggered API actions explicitly before runtime cues can redirect function use.