Security teams should baseline normal consumption for each agent and application, then alert on deviations in tokens, models, tools, timing, and source location. A sudden spike in usage, calls to unfamiliar models, or activity outside expected hours is a strong compromise signal. Cost monitoring and security monitoring should share the same telemetry so finance anomalies become security alerts immediately.
Baseline the agent before you hunt the attacker
Detection works best when teams know what normal looks like for each agent, not just for the platform as a whole. Baselines should cover consumption volume, preferred models, tool usage, time-of-day patterns, calling location, and the business workflow the agent normally serves. That gives you a reference point for spotting an account that is still authenticating correctly but is no longer behaving normally.
A hijacked agent often keeps using valid access while changing its pattern of activity. That means teams need behavioral telemetry, not only authentication logs, because the compromise may show up first as a new model selection, an unusual tool chain, or a source region the agent never used before.
When a team already tracks spend, usage, and security events separately, the first detection gap is usually correlation. Finance anomalies become much more useful when they are treated as security signals at the same time, because an attacker can burn through tokens, API calls, or third-party tool usage long before anyone reviews a security alert.
Which signals usually expose a hijacked agent first?
The strongest early signals are usually sudden spikes in tokens, requests, or tool invocations, especially when they do not align with the agent’s normal task cadence. Unfamiliar models, new tools, repeated retries, or calls outside expected hours are all clues that the agent may have been repurposed or is being steered by an attacker.
Source location matters because a legitimate agent tends to operate from stable infrastructure or well-understood cloud ranges. If a workload that normally runs from one region, tenant, or network segment starts appearing somewhere else, that shift should be treated as a compromise indicator, not as harmless noise.
The key question is whether the change is explainable by a planned rollout, a known workload migration, or an approved product change. If not, the activity should be investigated as possible agent hijacking rather than accepted as normal experimentation.
Why cost telemetry is a security control, not just a finance report
Cost monitoring is useful because attacker activity often shows up as economic friction before it shows up as overt failure. A hijacked agent may not immediately exfiltrate data or destroy systems, but it can still create a distinct cost pattern through rapid model switching, repeated prompt loops, broad tool calls, or misuse of premium services.
Shared telemetry shortens the path from anomaly to response. If the same event stream supports both budget oversight and security detection, teams can detect abuse earlier, compare it against expected business activity, and freeze the agent’s access before the cost spike turns into a larger compromise.
This works best when alerting is tied to an owner who can answer one practical question fast: is this consumption growth consistent with the intended workflow? Without that ownership, spend anomalies often become a billing problem that arrives after the security window has already closed.
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 MITRE ATLAS address the attack and risk surface, while CIS Controls v8 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Hijacked agents abuse valid identity and privileges to change behavior. |
| ASI02 — Tool Misuse | Unexpected tool calls and chain changes are core hijack indicators. | |
| ASI10 — Rogue Agents | A hijacked agent behaves like an unauthorized autonomous actor. | |
| Recommendation — Alert on unexpected identity, model, and tool changes that indicate abuse. Detect and block anomalous tool invocation patterns immediately. Quarantine agents that exceed expected autonomy or usage bounds. | ||
| MITRE ATLAS | Adversarial AI Threat Knowledge Base | Tracks AI attack techniques such as prompt injection and agent hijacking. |
| Recommendation — Map anomalous agent behavior to adversarial AI techniques for triage. | ||
| CIS Controls v8 | CIS-8 — Audit Log Management | Behavioral telemetry and logging are required to spot hijacked-agent anomalies. |
| Recommendation — Centralize and retain agent telemetry for fast anomaly detection. | ||
| NIST CSF 2.0 | DE.CM-01 — Continuous Monitoring | The question is about monitoring agent activity for deviations from normal. |
| DE.AE-02 — Anomalous Activity Detected | Usage spikes, unusual models, and off-hours activity are anomaly signals. | |
| Recommendation — Monitor agent usage continuously and alert on deviations from baseline. Escalate unexplained spikes and unusual agent behavior as security anomalies. | ||
Practitioner Guidance
What to verify: Baselines need to be per agent, not just per application. A shared service can hide one compromised agent inside a healthy aggregate, so verify that your telemetry can separate identity, model choice, tool usage, and source location at the agent level.
Decision rule: If the agent is still authenticated but its behavior departs from its normal consumption profile, treat it as a compromise candidate and investigate immediately, even if no data loss has been confirmed yet. Late-stage confirmation is less useful than early containment when token burn or tool misuse is already visible.
What practitioners underestimate: The first clue is often financial, not technical. The right operating model is to let spending anomalies trigger security review automatically, because that is where hijacked-agent activity becomes visible before it becomes expensive.
Practitioner takeaway: The goal is not to watch every AI action, but to make abnormal AI consumption unignorable, attributable, and fast to contain.
Related resources from NHI Mgmt Group
- How should security teams handle AI agent visibility?
- How should security teams monitor AI agent activity without disrupting developers?
- How should security teams detect AI agent escapes in Kubernetes before they reach the host or control plane?
- How do security teams detect AI agent sprawl before it becomes a breach issue?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 25, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
