Warning signs include unusual data access, new destinations, credential store access, repeated shell or file activity, and calls to unfamiliar networks or accounts. A second signal is identity drift, where an agent starts doing things it has never done before, or does them from an unfamiliar ASN, device context, or time pattern. Those shifts warrant immediate review.
What Malicious Agent Behaviour Usually Looks Like in Practice
Malicious behaviour is less about one dramatic event and more about a pattern that breaks the agent’s normal operating envelope. The most useful signals are changes in what data the system touches, where it sends information, which credentials or tokens it tries to use, and whether it begins performing actions that do not fit its prior purpose or approved workflow.
That is why practitioners should look for action drift as well as access drift. A model-driven app may still “look” healthy while quietly expanding its reach, probing new systems, or shifting from routine assistance into collection, exfiltration, or destructive behaviour.
- AI Agent Identity Security: The 2026 Deployment Guide is a useful companion when you need to separate normal agent activity from abnormal authority use.
- The agentic AI applications guide helps frame why tool access, delegation, and runtime boundaries matter when behaviour changes.
- AI Agents: The New Attack Surface report is useful for understanding how excessive permissions and orchestration mistakes can create the conditions for abuse.
Why Identity Drift and Unfamiliar Access Paths Matter
The most important warning sign is when an agent starts behaving as if it is a different actor. Identity drift can show up as unusual account use, unfamiliar device context, a new ASN, odd timing, or access patterns that do not match the agent’s established role. In practice, that often means the agent is either compromised, impersonated, or operating with authority that is broader than intended.
Unfamiliar destinations matter for the same reason. If an agent begins reaching new APIs, storage locations, shells, or external services without a clear business reason, the control question is no longer “Is it answering correctly?” but “Is it still operating within the trust boundary we approved?”
- CoPhish OAuth Token Theft via Copilot Studio shows how token theft can turn ordinary agent interactions into account abuse.
- AI LLM hijack breach illustrates how stolen cloud credentials can be converted into broader platform compromise.
- NIST AI Risk Management Framework provides a governance lens for monitoring trustworthy behaviour and operational boundaries.
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, CSA MAESTRO, MITRE ATLAS and MITRE ATT&CK address the attack and risk surface, while NIST AI RMF, NIST CSF 2.0 and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | GOVERN — Govern | AI behaviour drift requires governance over acceptable use and oversight. |
| Recommendation — Define oversight, accountability, and escalation paths for anomalous agent behaviour. | ||
| OWASP Agentic AI Top 10 | A2 — Tool Misuse | Unfamiliar tool, shell, or destination use is a core agentic abuse signal. |
| A4 — Identity and Privilege Abuse | Identity drift and overbroad authority are central to malicious agent behaviour. | |
| Recommendation — Constrain and monitor tool use so agents cannot expand actions outside intended scope. Bind agent actions to least privilege and alert on privilege or identity anomalies. | ||
| CSA MAESTRO | T1 — Agent Autonomy and Governance | Malicious shifts in autonomous behaviour are a governance and control problem. |
| Recommendation — Set autonomy boundaries and review any agent action that exceeds approved delegation. | ||
| MITRE ATLAS | TA0003 — Initial Access | Prompt injection and token theft can be entry paths for malicious agent behaviour. |
| Recommendation — Model how hostile inputs or stolen credentials can alter agent behaviour. | ||
| MITRE ATT&CK | T1078 — Valid Accounts | Agents that misuse legitimate accounts or tokens fit valid-account abuse patterns. |
| Recommendation — Detect legitimate credential use that occurs from unusual context, timing, or destinations. | ||
Practitioner Guidance
What to verify: Treat the first unexplained access pattern as a validation problem, not a tuning problem. Confirm whether the agent can prove why it needed the destination, the account, and the time of access, and compare that against its normal job function and approved tool set.
Decision rule: If the behaviour includes credential-store access, new network destinations, or a clear shift in action type, assume compromise or misuse until a human reviews it. Delay trust decisions until you can separate delegated automation from unauthorised expansion of authority.
Common mistake: Teams often overfocus on prompt quality or output correctness and miss the operational trail behind the output. The better control is behavioural baselining of access, tool use, and execution context, because malicious agents can produce plausible answers while still doing the wrong things underneath.
Practitioner takeaway: The key judgement is whether the agent is still acting inside its normal identity, scope, and context. Once those shift, the safest assumption is that the system has moved from “helpful automation” to “needs containment and review.”
Related resources from NHI Mgmt Group
- What are the signs that an AI coding agent is behaving in a black-box way during a workflow run?
- What are the signs that a consumer AI app is behaving like a data collection risk in a government environment?
- Who is accountable when an AI agent or mobile app enables authorized fraud?
- Why do AI agent access paths need different controls from ordinary app integrations?
